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Non-aerated compost tea (CT) was prepared from compost derived from rockweed (Ascophyllum nodosum) and fish (cod, common ling, haddock, saithe) residues that fermented in water. Electrical conductivity, pH, concentrations of dry matter, ash, C, macronutrients (N, P, K, Ca, and Mg), and micronutrients (Cu, Fe, Mn, Mo, and Zn) of CT prepared under different fermentation conditions were measured. The effects of process factors, i.e., water/compost mass ratio (4.2–9.8 g/g) and fermentation time (4.2–9.8 days = 100–236 h), on the physicochemical properties of CT were quantified using quadratic polynomial models. CT obtained at optimal levels of process factors (4.2 g/g and 5.6 days = 134 h) was tested for lettuce seed germination and seedling growth. Diluted CT (25% CT + 75% ultrapure water) improved seedling growth while achieving a high germination percentage (97%).

In a contemporary sustainable economy, innovation is a prerequisite to recycling waste into new efficient materials designed to minimize pollution and conserve non-renewable natural resources. Using an innovative approach to remediating metal-polluted water, in this study, eggshell waste was used to prepare two new low-cost nanoadsorbents for the retrieval of nickel from aqueous solutions. Scanning electron microscopy (SEM) results show that in the first eggshell–zeolite (EZ) adsorbent, the zeolite nanoparticles were loaded in the eggshell pores. The preparation for the second (iron(III) oxide-hydroxide)–eggshell–zeolite (FEZ) nanoadsorbent led to double functionalization of the eggshell base with the zeolite nanoparticles, upon simultaneous loading of the pores of the eggshell and zeolite surface with FeOOH particles. Structural modification of the eggshell led to a significant increase in the specific surface, as confirmed using BET analysis. These features enabled the composite EZ and FEZ to remove nickel from aqueous solutions with high performance and adsorption capacities of 321.1 mg/g and 287.9 mg/g, respectively. The results indicate that nickel adsorption on EZ and FEZ is a multimolecular layer, spontaneous, and endothermic process. Concomitantly, the desorption results reflect the high reusability of these two nanomaterials, collectively suggesting the use of waste in the design of new, low-cost, and highly efficient composite nanoadsorbents for environmental bioremediation.

Brown algae and fish waste contain high-value compounds with potentially beneficial effects on plant growth. Several commercial fertilizer products are currently available, but the characteristics of the materials are usually not well-described. Fish and seaweed residues originating from the Norwegian coast are available, after industrial processing, which may be combined into complete fertilizers exerting additional effects on crop plants (biostimulants). In this study, raw samples of fish and seaweed residues were investigated using ecofriendly technologies (drying, leaching), targeting search and isolation of potential biostimulants, followed by physicochemical characterization (elemental analysis, UV–visible, FT-IR, ICP-MS, ICP-OES, electrical conductivity, pH, etc.). Organic solvent extractions were employed to determine the available mineral content, micro- and macro-nutrients, antioxidant compounds, and amino acid content by chemical hydrolysis. The in vitro biotoxicity profile (cell viability, morphology, migration) of the generated extracts was also perused, employing Gram-positive (Staphylococcus aureus) and Gram-negative bacteria (Escherichia coli) along with sensitive neuronal eukaryotic cell lines N2a58 and SH-SY5Y, to assess their time- and concentration-dependent efficacy as antimicrobials and agents counteracting oxidative stress. The analytical composition of all raw materials showed that they contain important nutrients (K, P, Ca, N) as well as organic compounds and amino acids (Gly, Asp, Glu, Leu, Phe) capable of acting as plant biostimulants. Concurrently, the inherently high conductivity values and salt content necessitated leaching processes, which result in Na+ and K+ decreasing by more than ~60% and justifying further their use in soil treatment formulations. The aforementioned results and assertions, combined with physical measurements (pH, electrical conductivity, etc.) on naturally occurring and dried samples as well as green solvent extracts, formulated a physicochemical profile reflecting well-defined inorganic–organic species that might function as biostimulants. The collective physicochemical and biological properties support the notion that appropriate mixtures of marine organism residues may be efficient fertilizers for crop plants and concurrently possess biostimulant characteristics.

An outdoor experiment was performed for six months to evaluate the effects of organic fertilizers obtained from marine residual materials on strawberry plants. Three types of organic fertilizers were used, i.e., cod (Gadus morhua) bone powder, common ling (Molva molva) bone powder, and pellets obtained by mixing small cod bone powder and rockweed (Ascophyllum nodosum) residues. A tabletop system for strawberry cultivation was designed, in which two bare-root strawberry plants of cultivar ‘Albion’ were planted in a peat substrate in each pot. Five treatments were applied, i.e., cod bone powder (F1), common ling bone powder (F2), small cod bone powder and rockweed residue pellets (FA), chemical fertilizer (E), and a control (C). The number of leaves and their nutrient content, fruit yield and quality characteristics of the strawberries grown using the organic fertilizers were similar or better than those corresponding to treatments E and C. Organic fertilizers derived from the residues of fish and macroalgae could be a promising alternative to chemical fertilizers in strawberry production.

Non-aerated compost tea (CT) was prepared from compost derived from rockweed (Ascophyllum nodosum) and fish (cod, common ling, haddock, saithe) residues that fermented in water. Electrical conductivity, pH, concentrations of dry matter, ash, C, macronutrients (N, P, K, Ca, and Mg), and micronutrients (Cu, Fe, Mn, Mo, and Zn) of CT prepared under different fermentation conditions were measured. The effects of process factors, i.e., water/compost mass ratio (4.2–9.8 g/g) and fermentation time (4.2–9.8 days = 100–236 h), on the physicochemical properties of CT were quantified using quadratic polynomial models. CT obtained at optimal levels of process factors (4.2 g/g and 5.6 days = 134 h) was tested for lettuce seed germination and seedling growth. Diluted CT (25% CT + 75% ultrapure water) improved seedling growth while achieving a high germination percentage (97%). [ABSTRACT FROM AUTHOR]

Exposure to particulate matter (PM) is one of the most important environmental issues in Europe with major health impact. Various sizes of PM are suspended in the atmosphere and contributes to ambient air pollution. The current study aimed to explore the differential gene expression in blood, and the effect on the respective biological signaling pathways in Wistar rats, after exposure to PM2.5 and PM1 ambient air particles for an eight-week period. A control group was included with animals breathing non-filtered atmospheric air. In parallel, filtered PM2.5 and PM1 was collected in separate samplers. The results after whole genome microarray analysis showed 23 differentially expressed genes (DEGs) between control and PM2.5 group. In addition, pairwise comparison between control and PM1 group displayed 5635 DEGs linked to 69 biological pathways involved in inflammatory response, cell cycle and carcinogenicity. The smaller the size of the inhaled particles, the more gene alterations are triggered compared to non-filtered air group. More specifically, in inflammation signaling procedures differentially regulated gene expression was shown for interleukin-4 (IL-4), IL-7, IL-1, IL-5, IL-9, IL-6 and IL-2. We have identified that RASGFR1, TRIM65, TRIM33, PLEKHB1, CAR4, S100A8, S100A9, ALPL, NP4 and the PROK2 genes are potential targets for the development of adverse outcome pathways (AOPs) due to “real-life” exposure of Wistar rats. Particle measurements during the exposure period showed elevated concentrations of Fe, Mn and Zn in both PM1 and PM2.5 filter fractions, and of Cu in PM2.5. In addition, water-soluble concentration of metals showed significant differences between PM1 and PM2.5 fractions for V, Zn, As, Pb and Mn. In summary, in this study specific gene biomarkers of exposure to ambient air have been identified and heavy metals that are possibly linked to their altered regulation have been found. The results of this research will pave the way for the development of novel AOPs concerning the health effects of the environmental pollution.

Novel antimicrobial compounds can be added to foods directly or incorporated into packaging materials in order to improve food safety and shelf life. One such potential antimicrobial compound is the bioinorganic complex Co(II)-citrate (NH4)4[Co(C6H5O7)2] (Co-cit). Its antimicrobial activity against Listeria monocytogenes, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa was investigated in solution, both alone and in combination with existing preservatives. The antimicrobial activity of poly(lactic acid) films (PLA) with incorporated Co-cit (23.3% w/w) against L. monocytogenes was determined using culture medium and model foods (slices of turkey ham and smoked salmon). In nutrient broth, without preservatives, all four bacteria were significantly (p < 0.05) inhibited by 0.5 mM Co-cit, and L. monocytogenes and P. aeruginosa were the most sensitive. The addition of preservatives to the broth increased the antimicrobial activity of Co-cit in many cases, but not in a way that was consistent between the different bacteria investigated and was likely due to additional physiological stress exerted on the cells rather than any effect on the activity of the Co-cit itself. PLA films with Co-cit were bacteriostatic against L. monocytogenes on artificial media and on foods. However, in the latter case, the PLA film itself was the main contributor to the inhibition. Composite PLA-(Co-cit) films exhibited antimicrobial activity against foodborne bacteria and have potential application as active packaging materials to enhance food safety.

Supplementary cementing materials (SCMs) and the challenges associated with admixture compatibility and durability performance are continuous challenges for the construction industry. Utilizing SCMs has apparent benefits in reducing the carbon footprint and improving the durability performance of concrete structures. This work investigates the performance of mortars composed of Portland limestone cement, calcium carbonate, butyl stearate, and oleic acid. The effort focuses on transport properties using electrical resistivity, chloride migration, porosity, and water permeability measurements. Then, various methods based on the last parameters are compared to assess the changes in the effective chloride diffusion coefficient and the intrinsic liquid water permeability. Measurements for drying shrinkage, thermal expansion, and compressive strength are conducted to verify durability and mechanical performance. The effect of the admixture addition on the service life of a structure fully submerged in marine exposure conditions is then evaluated using Fick’s second law of diffusion and the approach described by FIB model code 34. The results indicate that incorporating calcium carbonate, butyl stearate, and oleic acid in mortar mixtures provides enhanced durability compared to plain Portland limestone cement mortars.

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM. Keywords: Machine learning, Data mining, Diabetes mellitus, Diabetic complications, Disease prediction models, Biomarker(s) identification