Dietary supplementation with microalgae enhances the zebrafish growth performance by modulating immune status and gut microbiota.

Microalgae are known to be abundant in various habitats around the globe, and are rich in high value-added products such as fatty acids, polysaccharides, proteins, and pigments. Microalgae can be exploited as the basic and primitive food source of aquatic animals. We investigated the effects of dietary supplementation with Schizochytrium sp., Spirulina platensis, Chloroella sorokiniana, Chromochloris zofingiensis, and Dunaliella salina on the growth performance, immune status, and intestinal health of zebrafish (Danio rerio). The results showed that these five microalgae diets could improve the feed conversion rate (FCR), especially the D. salina (FCR = 1.02%) and Schizochytrium sp. (FCR = 1.20%) additive groups. Moreover, the microalgae diets decreased the gene expression level of the pro-inflammatory cytokines IL6, IL8, and IL1ß at a normal physiological state of the intestine, especially the Schizochytrium sp., S. platensis, and D. salina dietary groups. The expression of neutrophil marker b7r was increased in the C. sorokiniana diet group; after, the zebrafish were challenged with Vibrio anguillarum, improving the ability to resist this disease. We also found that microalgae diets could regulate the gut microbiota of fish as well as increase the relative abundance of probiotics. To further explain, Cetobacterium was significantly enriched in the S. platensis additive group and Stenotrophomonas was higher in the Schizochytrium sp. additive group than in the other groups. Conversely, harmful bacteria Mycoplasma reduced in all tested microalgae diet groups. Our study indicated that these microalgae could serve as a food source supplement and benefit the health of fish. KEY POINTS: • Microalgae diets enhanced the growth performance of zebrafish. • Microalgae diets attenuated the intestinal inflammatory responses of zebrafish. • Microalgae diets modulated the gut microbiota composition to improve fish health.

Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation.

Federated learning is a framework for multiple devices or institutions, called local clients, to collaboratively train a global model without sharing their data. For federated learning with a central server, an aggregation algorithm integrates model information sent from local clients to update the parameters for a global model. Sample mean is the simplest and most commonly used aggregation method. However, it is not robust for data with outliers or under the Byzantine problem, where Byzantine clients send malicious messages to interfere with the learning process. Some robust aggregation methods were introduced in literature including marginal median, geometric median and trimmed-mean. In this article, we propose an alternative robust aggregation method, named γ-mean, which is the minimum divergence estimation based on a robust density power divergence. This γ-mean aggregation mitigates the influence of Byzantine clients by assigning fewer weights. This weighting scheme is data-driven and controlled by the γ value. Robustness from the viewpoint of the influence function is discussed and some numerical results are presented.

Fabrication of carbon dots for sequential on-off-on determination of Fe3+ and S2- in solid-phase sensing and anti-counterfeit printing.

Glutathione and 2-aminopyridine are used as carbon sources to prepare carbon dots (CDs) by a one-step hydrothermal reaction. The results show that the average particle diameter of CDs is 8.64 nm with uniform size distribution and the fluorescence quantum yield is 13.62%. We further demonstrate that novel CDs possess highly selective sensing of Fe3+ from 0.2 to 200 µM with a low detection limit (0.194 µM). Meanwhile, the fluorescence of CDs can be repeated many times by the addition of S2-. Moreover, the CDs are used for biological imaging of living cells with well cell penetrability and low toxicity. Furthermore, it is successfully applied for anti-counterfeiting and information encryption. More interestingly, it can be doped with hydrogel and filter paper to prepare solid-phase sensors exhibiting high sensitivity and fast response, demonstrating their tremendous potential for the simple, rapid, and low-cost monitoring of Fe3+ and S2-.

Detection of Cu2+ and S2- with fluorescent polymer nanoparticles and bioimaging in HeLa cells.

Novel spherical polymer nanoparticles were synthesized by hyperbranched polyethylenimine (hPEI) and 6-hydroxy-2-naphthaldehyde (HNA) via Schiff base reaction (one-pot reaction), which had great advantages in water solubility and green synthesis. Meanwhile, probe PEI-HNA could quickly detect Cu2+ in the range of 0-60 µM in 30 s with the detection limit of 243 nM. The fluorescence of PEI-HNA-Cu2+ could be recovered by the addition of S2- in 50 s with the detection limit of 227 nM. Based on the excellent optical properties, PEI-HNA has been used in the bioimaging of living cells with excellent cell penetrability and low toxicity. More importantly, PEI-HNA has been doped into filter paper, hydrogel, and nanofibrous film to prepare solid-phase sensors, displaying rapid response and excellent sensitivity. Moreover, the low-cost and simple preparation of these sensors offers great potential and possibilities for industrialization, which could help accelerate the development of sensors in environmental and biological fields.

Near-Infrared Spectroscopy Analysis of the Phytic Acid Content in Fuzzy Cottonseed Based on Machine Learning Algorithms.

Cottonseed is rich in oil and protein. However, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is an efficient and eco-friendly analytical technique for crop quality analysis. Despite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatments, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear support vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonseed. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best prediction performance. The RF model had a coefficient of determination in prediction (R2p) of 0.9114, and its residual predictive deviation (RPD) was 3.9828, which indicates its high accuracy in measuring the PA content in fuzzy cottonseed. Additionally, this method avoids the costly and time-consuming delinting and crushing of cottonseeds, making it an economical and environmentally friendly alternative.

High sensitivity for detecting trace Sn2+ in canned food using novel covalent organic frameworks.

Tin (Sn) element plays a vital role in the human body, and its detection is a mandatory inspection item for canned food. The application of covalent organic frameworks (COFs) in fluorescence detection has received extensive attentions. In this work, we designed a kind of novel COFs (COF-ETTA-DMTA) with high specific surface area (353.13 m2/g) by solvothermal synthesis using 2,5-dimethoxy-1,4-dialdehyde and tetra (4-aminophenyl) ethylene as precursors. It shows fast response time (about 50 s), low detection limit (228 nM) and good linearity (R2 = 0.9968) for the detection of Sn2+. Via coordination behavior, the recognition mechanism of COFs toward Sn2+ was simulated and verified by the small molecule with the same functional unit. More importantly, this COFs was successfully applied to identify Sn2+ in solid canned food (luncheon pork, canned fish, canned red kidney beans) with satisfactory results. This work provides a new approach for determining metal ions with COFs taking the advantage of their natural rich reaction set and specific surface area, improving the detection sensitivity and capacity.