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This study aimed to enhance the production of the intracellular L-asparaginase bacterial enzyme, which is known to impede the growth of cancer cells, by optimizing the culture conditions. In the present study, we exploited the ability of a new Actinomycete strain isolated from a food byproduct, wheat bran, to produce the desired enzyme. Streptomyces paulus CA01 was identified following morphological, biochemical, and molecular testing, and its total L-asparaginase activity was evaluated using ADS agar. Intracellular L-asparaginase activity was measured on the same medium using the well-diffusion method and was quantified by Nesslerization. A Box-Behnken design was employed to optimize the production of the enzyme by analyzing the effect of three factors: temperature, L-Asparagine concentration and glucose concentration. The ADS base medium, which was previously optimized and consisted of starch, K2HPO4, and MgSO4, was also included in the analysis. The optimal conditions for intracellular L-asparaginase production were temperature of 27.5 °C, L-Asparagine concentration of 1.19%, glucose concentration of 0.26% and ADS base medium composition, with which we achieved an incrase in enzymatic activity from 3.20 IU/mL to 8.39 IU/mL. This study indicates that Algerian wheat bran is a potential source of Streptomyces paulus CA01, which produces the bacterial intracellular L-asparaginase enzyme and also opens up prospects for the exploitation of agricultural by-products as sources of interesting micro-organisms. Despite the complex process of cell lysis required for its production, intracellular L-asparaginase is important and depends on culture conditions, making its optimization and increased yield feasible. [ABSTRACT FROM AUTHOR]

Many sensitive applications require that data remain confidential and undisclosed, even for intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data has become increasingly vital. Deep learning models are becoming good tools to detect anomalies in encrypted data without the need to pass through data decryption. This paper presents a systematic review focusing on the advancements made in deep learning models for intrusion detection over encrypted data with privacy preservation. This study aims to guide researchers on how to select the right tools to set up an intrusion detection system over encrypted data with privacy preservation. The study presented the context and challenges of intrusion detection on encrypted data and how machine learning-based solutions can circumvent these challenges. The paper looks at recently proposed solutions, examines metrics for assessing model performance, and evaluates frequently used reference datasets. Deep learning models are also evaluated with statistics on the most frequent models, datasets, and encryption tools. The performance metrics of the studied solutions are investigated as a function of the encryption tools, the deployed deep learning models, the privacy preservation tools, the deployed datasets, and the eventual additional tools and algorithms. Our recommendations help researchers evaluate their proposals for preserving privacy and detecting intrusions on encrypted data using deep learning techniques. [ABSTRACT FROM AUTHOR]

• The ferromagnetic-paramagnetic transition in La 0.5 Sm 0.2 Sr 0.3 MnO 3 and La 0.5 Sm 0.2 Sr 0.3 Mn 0.95 Fe 0.05 O 3 was studied. • (μ 0 Critical exponents for LaSmSrMnO and LaSmSrMnFeO were determined. • Isotherms M(μ 0 H,T) and -∆S M (μ 0 H,T) were simulated using the Landau model for both compounds. In this paper, an effective calculation method resulting from the combination of Landau theory and the Arrott–Noakes equation is reported. The magnetic entropy change, - Δ S M , in a random ferromagnetic system is simulated. A phase shift from paramagnetic to ferromagnetic behavior is observed in these complex magnetic systems. Their critical behavior was ascertained by magnetization measurements and magnetocaloric computations. The critical exponents of the La 0.5 Sm 0.2 Sr 0.3 MnO 3 and La 0.5 Sm 0.2 Sr 0.3 Mn 0.95 Fe 0.05 O 3 manganites were discovered to be (0.43; 1.19; 3.78), and (0.52; 1.45; 3.78), respectively, using modified Arrott plots (applied to Tricritical mean-field, 3D-Ising, and 3D-Heisenberg models) and the Kouvel-Fisher technique. The critical exponents obtained from the study were used to investigate the Landau model by employing the Gibbs free energy to simulate isotherms of magnetization M (μ 0 H , T) and the curves of - Δ S M (μ 0 H , T). The findings indicate that short-range interactions are critical in determining the magnetic characteristics and are consistent with the 3D Heisenberg and 3D Ising models. [ABSTRACT FROM AUTHOR]

This study evaluates the antibacterial effectiveness of Origanum vulgare hydroethanolic extract, both independently and in combination with antibiotics, against Escherichia coli strains associated with avian colibacillosis—a significant concern for the poultry industry due to the rise of antibiotic-resistant E. coli. The urgent demand for new treatments is addressed by analyzing the extract's phytochemical makeup via High-Performance Liquid Chromatography (HPLC), which identified sixteen phenolic compounds. Antibacterial activity was determined through agar diffusion and the measurement of minimum inhibitory and bactericidal concentrations (MIC and MBC), showing moderate efficacy (MIC: 3.9 to 7.8 mg/mL, MBC: 31.2 to 62.4 mg/mL). Combining the extract with antibiotics like ampicillin and tetracycline amplified antibacterial activity, indicating a synergistic effect and highlighting the importance of combinatory treatments against resistant strains. Further analysis revealed the extract's mechanisms of action include disrupting bacterial cell membrane integrity and inhibiting ATPase/H+ proton pumps, essential for bacterial survival. Moreover, the extract effectively inhibited and eradicated biofilms, crucial for preventing bacterial colonization. Regarding cytotoxicity, the extract showed no hemolytic effect at 1 to 9 mg/mL concentrations. These results suggest Origanum vulgare extract, particularly when used with antibiotics, offers a promising strategy for managing avian colibacillosis, providing both direct antibacterial benefits and moderating antibiotic resistance, thus potentially reducing the economic impact of the disease on the poultry industry. [Display omitted] [ABSTRACT FROM AUTHOR]

The combined effect of temperature, light intensity, and NaNO concentration on lipid biosynthesis and fatty acid composition was investigated for the thermophilic cyanobacterium Gloeocapsa gelatinosa (Kützing ) isolated from a thermal spring in Tunisia. Under optimal growth conditions, the lipid content was 7.3 % DW. Fatty acid analysis revealed the predominance of 16:0 and 18:0 (23.7 and 18.2 %, respectively) as main straight carbon chains of saturated fatty acids. Unsaturated fatty acids were also identified with 18:1n9c (18.8 %) and 16:1n7 (5 %) being the predominant components. The effect of environmental factors on fatty acid composition was monitored by using principal component analysis and central composite design. Variation of light intensity (20 to 150 μmol photons m s), temperature (20 to 60 °C), and nitrogen concentration (0 to 3 g L) induced a significant variation in the amount of fatty acid proportions, whereas lipid content was only slightly modified. Results showed that light intensity had the strongest effect on the composition of fatty acids. Temperature had a synergic effect with light intensity while nitrogen concentration had a trivial effect. The combined effect of high light intensity (150 μmol photons m s) and high temperature (60 °C) increased the proportion of saturated 16:0 and 18:0 fatty acids along with long-chain fatty acids to 82 % which was twofold higher than that in optimal growth conditions. This induced fatty acid profile makes G. gelatinosa-based biofuels adaptable for higher energetic efficiency and higher oxidative stability. [ABSTRACT FROM AUTHOR]