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Microalgae, which include over 30,000 different species, are an incredibly rich source of nutrients and health-boosting compounds. They have shown impressive health benefits due to their antioxidant, anti-inflammatory, antimicrobial, and anticancer properties, making them valuable for developing medicines, functional foods, and dietary supplements. Advances in technology, such as improved bioreactor systems, genetic engineering, and metabolic fine-tuning, have made it easier to grow microalgae and increase the production of beneficial compounds. Additionally, using microalgae in wastewater treatment and CO2 capture supports environmental and economic sustainability, contributing to a more circular and eco-friendly economy. However, challenges like high production costs and scalability barriers persist. Despite these hurdles, microalgae present a transformative solution to address the growing demand for bioactive compounds and sustainable health interventions. The ongoing research shows their application across industries, including pharmaceuticals, animal husbandry, and aquaculture, paving the way for novel, sustainable nutrition, and health. However, there are still hurdles to overcome. Producing microalgae on a large scale remains costly, and the amount of bioactive compounds produced can vary. Regulatory challenges and the need to increase consumer awareness and acceptance also make it difficult for microalgal products to reach the mainstream market. Despite these issues, ongoing progress in biotechnology, AI-based process optimization, and strain improvement offers promising solutions to make microalgae production more efficient and commercially viable. The growing use of microalgae in pharmaceuticals, health supplements, animal feed, and aquaculture highlights their potential to revolutionize the health and nutrition industries. This review aims to explore the nutritional profile of microalgae, their health benefits, and the technological and market challenges involved, while suggesting future strategies to unlock their full potential in sustainable health and nutrition.

The textile industry is expanding globally and is considered the backbone of the world’s largest source of foreign exchange. The development of the textile industry has caused environmental contamination due to its dye waste, which is complex and very difficult to resolve with chemical and physical treatments. Azo dye is one of the most widely used dyes in textile and other industries. It is one of the significantly toxic dyes, and when released in water bodies, it causes a serious threat to the environment. A bacterial strain having the potential to degrade a variety of azo dyes such as Congo red (CR), methylene blue (MB), Alizarin Red S (AR), and Remazol Brilliant Blue R (RBBR) was isolated from soil samples in the wood weathering area and further identified and characterized. Ligninolytic microorganisms produce laccase enzymes, lignin peroxidase, manganese peroxidase, and other enzymes that can decolorize dye waste from the textile industry. The research phase was qualitative and quantitative tests of ligninolytic bacteria in the decolorization process using several selected synthetic dyes, antagonism tests, and identification of potential bacteria based on 16S rDNA gene sequences. The L11 isolate showed high performance on CR dye of 82.79%, L1 isolate on dye AR of 40.51%, L7 isolate on dye MB of 38.69%, and L8 isolate on RBBR dye of 30.34%. The L11 isolate with the highest potency was identified as Bacillus paramycoides K7.2 with a similarity of 99.71%. After 7 days of incubation, the quantitative test findings are the same as the qualitative test results, with isolate L11 having the largest clear zone on CR, AR, and RBBR dyes.

The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields, with its applications spanning finance, engineering, healthcare, and more. In recent years, AI’s potential in addressing environmental concerns has garnered significant attention. This review paper provides a comprehensive exploration of the impact that AI has on addressing and mitigating critical environmental concerns. In the backdrop of AI’s remarkable advancement across diverse disciplines, this study is dedicated to uncovering its transformative potential in the realm of environmental monitoring. The paper initiates by tracing the evolutionary trajectory of AI technologies and delving into the underlying design principles that have catalysed its rapid progression. Subsequently, it delves deeply into the nuanced realm of AI applications in the analysis of remote sensing imagery. This includes an intricate breakdown of challenges and solutions in per-pixel analysis, object detection, shape interpretation, texture evaluation, and semantic understanding. The crux of the review revolves around AI’s pivotal role in environmental control, examining its specific implementations in wastewater treatment and solid waste management. Moreover, the study accentuates the significance of AI-driven early-warning systems, empowering proactive responses to environmental threats. Through a meticulous analysis, the review underscores AI’s unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation.

Microplastics (MPs) are ubiquitous pollutants persisting almost everywhere in the environment. With the increase in anthropogenic activities, MP accumulation is increasing enormously in aquatic, marine, and terrestrial ecosystems. Owing to the slow degradation of plastics, MPs show an increased biomagnification probability of persistent, bioaccumulative, and toxic substances thereby creating a threat to environmental biota. Thus, remediation of MP-pollutants requires efficient strategies to circumvent the mobilization of contaminants leaching into the water, soil, and ultimately to human beings. Over the years, several microorganisms have been characterized by the potential to degrade different plastic polymers through enzymatic actions. Metagenomics (MGs) is an effective way to discover novel microbial communities and access their functional genetics for the exploration and characterization of plastic-degrading microbial consortia and enzymes. MGs in combination with metatranscriptomics and metabolomics approaches are a powerful tool to identify and select remediation-efficient microbes in situ. Advancement in bioinformatics and sequencing tools allows rapid screening, mining, and prediction of genes that are capable of polymer degradation. This review comprehensively summarizes the growing threat of microplastics around the world and highlights the role of MGs and computational biology in building effective response strategies for MP remediation.

Recent years have seen a significant increase in microplastic contamination across terrestrial ecosystems, facilitated by various pathways such as atmospheric deposition, wastewater irrigation, and agricultural plastic mulching. This persistence of microplastics in soil raises concerns about their profound impacts on soil health and ecosystem dynamics. Key studies have highlighted detrimental effects, including reduced soil moisture retention, altered soil microbiota composition, and disrupted nutrient cycling processes. Moreover, microplastic pollution adversely affects soil biota, notably earthworms, crucial for soil nutrient cycling and structure maintenance, exhibiting reduced growth rates and increased mortality upon exposure. Notably, microplastics also influence soil microorganisms, potentially compromising overall soil health and ecosystem functioning. Co-exposure of microplastics and other contaminants can also synergistically exacerbate toxicity, impairing ecological balance. Evidence suggests negative repercussions on plant growth, including diminished seed germination rates and altered nutrient profiles in exposed plants. These findings underscore the urgent need for comprehensive reviews to synthesize existing knowledge, and identify research gaps, necessitating a focus on mitigation strategies. Addressing these issues is critical for safeguarding terrestrial ecosystems and ensuring sustainable environmental management in the face of increasing microplastic contamination.

The seafood industry generates waste, including shells, bones, intestines, and wastewater. The discards are nutrient-rich, containing varying concentrations of carotenoids, proteins, chitin, and other minerals. Thus, it is imperative to subject seafood waste, including shrimp waste (SW), to secondary processing and valorization for demineralization and deproteination to retrieve industrially essential compounds. Although several chemical processes are available for SW processing, most of them are inherently ecotoxic. Bioconversion of SW is cost-effective, ecofriendly, and safe. Microbial fermentation and the action of exogenous enzymes are among the significant SW bioconversion processes that transform seafood waste into valuable products. SW is a potential raw material for agrochemicals, microbial culture media, adsorbents, therapeutics, nutraceuticals, and bio-nanomaterials. This review comprehensively elucidates the valorization approaches of SW, addressing the drawbacks of chemically mediated methods for SW treatments. It is a broad overview of the applications associated with nutrient-rich SW, besides highlighting the role of major shrimp-producing countries in exploring SW to achieve safe, ecofriendly, and efficient bio-products.

In this study, amine-functionalized MWCNTs (f-MWCNTs) have been investigated as potential material for the removal of Mordant black dye 11 (MBD 11). To evaluate the optimal condition and adsorption capability of the adsorbents (f-MWCNTs), the effect of temperature, pH, adsorbent dosage, and contact time on adsorption rate are examined. The study shows a stronger interaction between the dye and f-MWCNTs. The highest removal efficiency is observed in acidic medium (pH 2) with an initial dye concentration of 50 mg L−1, where 99% of the dye is adsorbed from the medium in 40 min using 0.05 g of f-MWCNTs. Adsorption isotherm and kinetic studies reveal that adsorption occurs by the Langmuir adsorption model and pseudo-second-order adsorption kinetics. According to the thermodynamic parameters, the adsorption exhibits endothermic and spontaneous behavior.

Background: Syringodium isoetifolium is a seagrass that grows abundantly in Indonesian territorial waters and has been known to be of high significance not only for the seawater ecosystem, but also for human beings (as food, nutritional and pharmaceutical products). In this study, the bioactive constituent of Syringodium isoetifolium was extracted using several different techniques to recover a maximum yield. Methods: Extraction was carried out by conventional and non-conventional (Microwave-assisted extraction, ultrasound-assisted extraction-bath system, and ultrasound-assisted extraction- (UAE) probe system) techniques with green solvents (water, 50% ethanol, and 100% ethanol). Results: As a result, 50% ethanol and water extracts exhibited a significantly higher yield. Total phenol content was significantly higher for 50% ethanol extract. Different extraction techniques (using 50% ethanol solvent) showed that the UAE-probe was the best technique since it yielded the highest total phenol (17.37 ± 2.16 mg GAE/g) and the richest bioactive compounds (Choline, betaine, 3,5-di-tert-butyl-4-hydroxybenzoic acid, 7-Hydroxycoumarine, 4-Methoxycinnamic acid, Zearalenone, Caffeic acid, Levalbuterol, Phloretin, Dihydrocaffeic acid, Quercetin-3β-D-glucoside and Quercetin). Interestingly, choline was the most abundant compound in the extract obtained with different extraction techniques. In this   in silico assay, choline from seagrass extract was shown as an anti-inflammatory. The interaction pathway of the choline compound with receptors (TNF-α, IL-1β, and IL6) had a higher binding affinity value than the inhibitor-receptor interaction (i.e. -3.4, -3.0, and -2.8 kcal/mol). The cytotoxicity test on TIG-1 cells showed that the extract did not have a toxic effect on them. Conclusions: These findings support the potential use of Syringodium isoetifolium as a bioactive food ingredient.

Background: Antimicrobial resistance (AMR) poses significant risks to human and animal health, while the environment can contribute to its spread. National AMR surveillance programs are pivotal for assessing AMR prevalence, trends, and intervention outcomes; however, integrating advanced surveillance tools can be difficult. This pilot study, conducted by FAO ECTAD Indonesia and DGLAHS, the Indonesian Ministry of Agriculture, evaluated the costs and benefits of integrating the Nanopore MinION, Illumina MiSeq, and Sensititre system into a culture-based slaughterhouse–river surveillance system. Methods: Water samples were collected from six chicken slaughterhouses and adjacent rivers (pre- and post-treatment effluent, upstream, and downstream). Culture-based ESBL and general E. coli concentrations were estimated via the WHO Tricycle Protocol, while isolates (n = 42) were sequenced (MinION, MiSeq) and antimicrobial susceptibility testing conducted (Sensititre). Results: The Tricycle Protocol results provided estimates of effluent and river concentrations of ESBL and general E. coli identifying ESBL-to-general E. coli ratios of 13.8% and 6.2%, respectively. Compared to hybrid sequencing assemblies, MinION had a higher concordance than MiSeq for ARG identification (98%), virulence genes (96%), and locations for both (predominately plasmids). Furthermore, MinION concordance with Sensititre AST was 91%. Conclusions: Cost–benefit comparisons suggest sequencing can complement culture-based methods but is dependent on the value placed on the additional information gained.

Calculus has a high abstractness so calculus requires the ability to think in solving mathematical problems. Calculus is one of the fields of mathematics that is full of calculations and formulas, so it needs the motivation to be happy and interested in calculus. This study aims to analyze discovery leaning with link maps and learning motivation on calculus. This research is quantitative descriptive with research variables including discovery learning, link maps and academic abilities of students. The study is conducted at Universitas Kanjuruhan Malang with 105 students. The research data is obtained from the results of the students’ academic ability test, the results of student worksheets on discovery learning and link maps and learning motivation with questionnaires. Data analysis uses multiple linear regressions assisted by SPSS. The results show that discovery learning with link maps has a positive effect on students’ academic abilities. In addition, motivation influences on discovery learning with link maps and student academic abilities. If students have high motivation and the ability to find their own concepts, the results of the concept last a long time in students’ memory, the students’ thinking ability and memory of students in understanding mathematical concepts, therefore it can solve mathematics problems better. Discovery learning with link maps gives students better academic abilities.