Effects of sodium selenite on the growth, biochemical composition and selenium biotransformation of the filamentous microalga Tribonema minus.

This study aimed to investigate the physiological and biochemical responses of filamentous microalga Tribonema minus to different Na2SeO3 concentrations and its selenium absorption and metabolism to evaluate the potential in treating selenium-containing wastewater. The results showed that low Na2SeO3 concentrations promoted growth by increasing chlorophyll content and antioxidant capacity, whereas high concentrations caused oxidative damage. Although Na2SeO3 exposure reduced lipid accumulation compared with the control, it significantly increased carbohydrate, soluble sugar, and protein contents, with the highest carbohydrate productivity of 117.97 mg/L/d at 0.5 mg/L Na2SeO3. Furthermore, this alga effectively absorbed Na2SeO3 in the growth medium and converted most of it into volatile selenium and a small part into organic selenium (predominantly as selenocysteine), showing strong selenite removal efficacy. This is the first report on the potential of T. minus to produce valuable biomass while removing selenite, providing new insights into the economic feasibility of bioremediation of selenium-containing wastewater.

Predicting User Susceptibility to Phishing Based on Multidimensional Features.

While antiphishing techniques have evolved over the years, phishing remains one of the most threatening attacks on current network security. This is because phishing exploits one of the weakest links in a network system-people. The purpose of this research is to predict the possible phishing victims. In this study, we propose the multidimensional phishing susceptibility prediction model (MPSPM) to implement the prediction of user phishing susceptibility. We constructed two types of emails: legitimate emails and phishing emails. We gathered 1105 volunteers to join our experiment by recruiting volunteers. We sent these emails to volunteers and collected their demographic, personality, knowledge experience, security behavior, and cognitive processes by means of a questionnaire. We then applied 7 supervised learning methods to classify these volunteers into two categories using multidimensional features: susceptible and nonsusceptible. The experimental results indicated that some machine learning methods have high accuracy in predicting user phishing susceptibility, with a maximum accuracy rate of 89.04%. We conclude our study with a discussion of our findings and their future implications.

Isolation, Characterization and Immunomodulatory Activity Evaluation of Chrysolaminarin from the Filamentous Microalga Tribonema aequale.

The aim of this study is to investigate the differences in the accumulation capacity of chrysolaminarin among six Tribonema species and to isolate this polysaccharide for immunomodulatory activity evaluation. The results showed that T. aequale was the most productive strain with the highest content and productivity of chrysolaminarin, which were 17.20% (% of dry weight) and 50.91 mg/L/d, respectively. Chrysolaminarin was then extracted and isolated from this alga, and its monosaccharide composition was mainly composed of a glucose (61.39%), linked by ß-D-(1→3) (main chain) and ß-D-(1→6) (branch chain) glycosidic bonds, with a molecular weight of less than 6 kDa. In vitro immunomodulatory assays showed that it could activate RAW264.7 cells at a certain concentration (1000 µg/mL), as evidenced by the increased phagocytic activity and upregulated mRNA expression levels of IL-1ß, IL6, TNF-α and Nos2. Moreover, Western blot revealed that this polysaccharide stimulated the phosphorylation of p-65, p-38 and JNK in NF-κB and MAPK signaling pathways. Overall, these findings provide a reference for the further development and utilization of algae-based chrysolaminarin, while also offering an in-depth understanding of the immunoregulatory mechanism.

Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Phishing has become one of the biggest and most effective cyber threats, causing hundreds of millions of dollars in losses and millions of data breaches every year. Currently, anti-phishing techniques require experts to extract phishing sites features and use third-party services to detect phishing sites. These techniques have some limitations, one of which is that extracting phishing features requires expertise and is time-consuming. Second, the use of third-party services delays the detection of phishing sites. Hence, this paper proposes an integrated phishing website detection method based on convolutional neural networks (CNN) and random forest (RF). The method can predict the legitimacy of URLs without accessing the web content or using third-party services. The proposed technique uses character embedding techniques to convert URLs into fixed-size matrices, extract features at different levels using CNN models, classify multi-level features using multiple RF classifiers, and, finally, output prediction results using a winner-take-all approach. On our dataset, a 99.35% accuracy rate was achieved using the proposed model. An accuracy rate of 99.26% was achieved on the benchmark data, much higher than that of the existing extreme model.