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The purpose of this research was to determine the efficacy of iron oxide nanoparticles (Fe 3 O 4 -NPs) using microalgal products as a plant growth stimulant and antifungal agent. The work was conducted with the phyco-synthesis and characterization of Fe 3 O 4 -NPs using 0.1 M ferric/ferrous chloride solution (2:1 ratio; 65 °C) with aqueous extract of the green microalga Chlorella K01. Protein, carbohydrate and polyphenol contents of Chlorella K01 extract were measured. The synthesized microalgal Fe 3 O 4 -NPs made a significant contribution to the germination and vigor index of rice, maize, mustard, green grams, and watermelons. Fe 3 O 4 -NPs also exhibited antifungal activity against Fusarium oxysporum, Fusarium tricinctum, Fusarium maniliforme, Rhizoctonia solani, and Phythium sp. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) scanning electron microscopy (SEM), transmission electron microscopy (TEM), particle size analysers (PSA), and zeta potential (ZP) measurements were used to characterize these green fabricated magnetite NPs. FTIR analysis showed that the synergy of microalgal proteins, carbohydrtates and polyphenols is responsible for the biofabrication of iron nanoparticles. A spheroid dispersion of biosynthesized Fe 3 O 4 -NPs with an average diameter of 76.5 nm was produced in the synthetic process.

Arthrospira platensis proteins are considered as viable ingredients for functional foods with nutraceutical properties and health-promoting effects. In this study, we used five different proteases including pepsin, trypsin, alcalase, papain, and bromelain to hydrolyze A. platensis phycobiliprotein. LC–MS/MS (liquid chromatography-tandem mass spectrometry) was used to determine the composition of the hydrolysates with thousands of bioactive peptides. Based on peptide sequencing, a candidate list of 1,333 bioactive peptides was constructed for the first time for which bioinformatics tools were used to assess the properties of the bioactive peptides. The inhibitory activity of dipeptidyl peptidase IV (DPP-IV) and angiotensin-converting enzyme (ACE), as well as the antioxidant activity of five phycobiliprotein hydrolysates (PBPHs), were verified in vitro. The IC50 values of PBPH-pepsin, PBPH-trypsin, PBPH-alcalase, PBPH-papain, and PBPH-bromelain for DPP-IV inhibited activity were determined to be 4.059, 5.603, 5.257, 3.819, and 4.195 mg mL−1, respectively. All the above five PBPHs were also seen to significantly inhibit ACE activity (P < 0.0001) in the range of 0.1–1.0 mg mL−1. The activity of five PBPHs fraction was found for the 2-azino-bis (3-ethylbenzothiazoline6-sulfonic acid) diammonium salt (ABTS) assay (the highest was 1.37 mM Trolox equivalent of 30 mg mL−1 of PBPH-trypsin), 1,1diphenyl-2-picrylhydrazyl (DPPH) assay (the highest was 204.67 µM Trolox equivalent of 10 mg mL−1 of PBPH-pepsin), and Fe2+ chelating ability (the highest was 639.73 µM FeSO4 equivalent of 10 mg mL−1 of PBPH-trypsin). This study indicated that it was feasible to utilize A. platensis phycobiliprotein as a source of bioactive peptides which may be used as functional food and/or nutritional supplements for human health.

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

A 61-year-old man presented with a non-tapered occlusion at the origin of the left vertebral artery, with the right vertebral artery failing to join the left vertebral artery to form the basilar artery, and basilar artery tip occlusion. Early antegrade endovascular recanalization attempts with microwires failed to traverse the occlusion at the left vertebral artery origin. Digital subtraction angiography revealed a well-developed left ascending cervical artery communicating with the V3 segment of the left vertebral artery. We adopted a retrograde endovascular recanalization strategy and, with adjunctive balloon angioplasty and stent placement, successfully reestablished patency of the left vertebral artery origin.

The scientific consensus is now on developing a biocontrol agent that can cause cellular metabolic reprogramming against agricultural pathogens. Biosynthesis of silver nanoparticles was performed by using phytopathogenic fungi (Alternaria sp.) isolated from banana cultivated soil. Alternaria sp. can grow very fast and produce high enough bioactive compounds. This study aims to biosynthesize silver nanoparticles (AgNPs) using fungal Alternaria sp.’s metabolites as a safe antifungal agent against plant pathogenic fungi (Fusarium spp. and Alternaria sp.). To visualize the formation of AgNPs, analytical instruments were used, such as ultraviolet-visible (UV-Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, scanning transmission electron microscopy (STEM), energy dispersive X-ray (EDX), and elemental mapping. The UV-visible spectra showed a peak at 435 nm. Analysis of scanning transmission electron microscopy (STEM) micrographs evidenced that the size of synthesized silver nanoparticles ranged between 3 and 10 nm. The resulting AgNPs showed distinct antifungal activity against selected plant pathogenic fungi. Synthesized AgNPs have demonstrated remarkable potential for the use of antifungal compounds to combat plant diseases.

Phycobiliproteins have gained increasing attention for their antidiabetic potential, yet the specific bioactive peptides and their targets and molecular mechanisms have remained unclear. In this study, four peptides with potential hypoglycemic activity were identified through virtual screening. Network pharmacology was employed to elucidate their hypoglycemic mechanism in the treatment of T2DM. A subsequent in vitro assay confirmed that the synthesized peptides, GR-5, SA-6, VF-6, and IR-7, exhibited significant inhibitory activity against α-glucosidase and DPP-IV. In insulin-resistant HepG2 models, all four peptides exhibited no cytotoxicity. Among them, GR-5 demonstrated the most promising therapeutic potential by remarkably enhancing cellular glucose consumption capacity. Furthermore, GR-5 administration substantially increased glycogen synthesis and enzymatic activities of hexokinase and pyruvate kinase with statistically significant improvements compared to the control groups. This study provides novel peptide candidates for T2DM treatment and validates an integrative strategy for targeted bioactive peptide discovery, advancing the development of algal protein-based therapeutics.

The study of human gut microbiota has attracted increasing interest in the fields of life science and healthcare. However, the complicated and interconnected associations between gut microbiota and human diseases are still difficult to determine in a predictive fashion. Artificial intelligence such as machine learning (ML) and deep learning can assist in processing and interpreting biological datasets. In this study, we aggregated data from different studies based on the species composition and relative abundance of gut microbiota in children with autism spectrum disorder (ASD) and typically developed (TD) individuals and analyzed the commonalities and differences of ASD-associated microbiota across cohorts. We established a predictive model using an ML algorithm to explore the diagnostic value of the gut microbiome for the children with ASD and identify potential biomarkers for ASD diagnosis. The results indicated that the Shenzhen cohort achieved a higher area under the receiver operating characteristic curve (AUROC) value of 0.984 with 97% accuracy, while the Moscow cohort achieved an AUROC value of 0.81 with 67% accuracy. For the combination of the two cohorts, the average prediction results had an AUROC of 0.86 and 80% accuracy. The results of our cross-cohort analysis suggested that a variety of influencing factors, such as population characteristics, geographical region, and dietary habits, should be taken into consideration in microbial transplantation or dietary therapy. Collectively, our prediction strategy based on gut microbiota can serve as an enhanced strategy for the clinical diagnosis of ASD and assist in providing a more complete method to assess the risk of the disorder.

In an effort to study undrained post-liquefaction shear deformation of sand, the discrete element method (DEM) is adopted to conduct undrained cyclic biaxial compression simulations on granular assemblies consisting of 2D circular particles. The simulations are able to successfully reproduce the generation and eventual saturation of shear strain through the series of liquefaction states that the material experiences during cyclic loading after the initial liquefaction. DEM simulations with different deviatoric stress amplitudes and initial mean effective stresses on samples with different void ratios and loading histories are carried out to investigate the relationship between various mechanics- or fabric-related variables and post-liquefaction shear strain development. It is found that well-known metrics such as deviatoric stress amplitude, initial mean effective stress, void ratio, contact normal fabric anisotropy intensity, and coordination number, are not adequately correlated to the observed shear strain development and, therefore, could not possibly be used for its prediction. A new fabric entity, namely the Mean Neighboring Particle Distance (MNPD), is introduced to reflect the space arrangement of particles. It is found that the MNPD has an extremely strong and definitive relationship with the post-liquefaction shear strain development, showing MNPD’s potential role as a parameter governing post-liquefaction behavior of sand.

As unmanned aerial vehicles (UAVs) have limited energy resources and diverse constraints, the reconfiguration of their formation encounters substantial challenges. In this paper, we employ a leader-follower method. In order to minimize flight distance and resource consumption, a greedy algorithm is used to allocate leader and follower positions. Based on the limitations of the receding horizon control (RHC) method and the control parameterization and time discretization (CPTD) method, we propose the no reference path RHC (NRPRHC) method. The proposed method transforms the formation reconfiguration into smaller local optimization problems, leading to a reduction in the size of the optimization stages and computational complexity. For each local optimization problem, we propose the adaptive population differential evolution (APDE) algorithm to optimize the control inputs. Finally, the results are provided to illustrate the feasibility and effectiveness of the proposed method.

Eukaryotic microalgae are important primary producers in nature that play an important role in the energy cycle of nature. The eukaryotic microalgae are also regarded as potential bioactive substance producers. In recent years, with the aid of genetic engineering, eukaryotic microalgae have found a wider range of potential applications in medicine, food, health products, cosmetics, and environmental protection. This article reviews the state of the art of microalga genetic engineering from the aspects of gene transfer system, gene editing technology, and screening method, with examples of the enhancement of lipids, carotenoids, polysaccharide, and functional proteins. Potential application scenarios of microalga genetic engineering and their products in the field of food and medicine are also highlighted. Furthermore, strategies for protein expression optimization in eukaryotic microalgae are reviewed. The existing shortcomings of eukaryotic microalga genetic engineering are also analyzed and highlighted.