Biocatalysis of triglycerides transesterification using fungal biomass: a biorefinery approach.

BACKGROUND: The use of microbial biomasses, such as fungal biomass, to catalyze the transesterification of triglycerides (TG) for biodiesel production provides a sustainable, economical alternative while still having the main advantages of expensive immobilized enzymes. RESULTS: Biomasses of Aspergillus flavus and Rhizopus stolonifera were used to catalyze the transesterification of TG in waste frying oil (WFO). Isopropanol as an acyl-acceptor reduced the catalytic capability of the biomasses, while methanol was the most potent acyl-acceptor with a final fatty acid methyl ester (FAME) concentration of 85.5 and 89.7%, w/w, for R. stolonifer and A. flavus, respectively. Different mixtures of the fungal biomasses were tested, and higher proportions of A. flavus biomass improved the mixture's catalytic capability. C. sorokiniana cultivated in synthetic wastewater was used as feedstock to cultivate A. flavus. The biomass produced had the same catalytic capability as the biomass produced in the control culture medium. Response surface methodology (RSM) was adopted using central composite design (CCD) to optimize the A. flavus biomass catalytic transesterification reaction, where temperature, methanol concentration, and biomass concentration were selected for optimization. The significance of the model was verified, and the suggested optimum reaction conditions were 25.5 °C, 250 RPM agitation with 14%, w/w, biomass, 3 mol/L methanol, and a reaction duration of 24 h. The suggested optimum conditions were tested to validate the model and a final FAME concentration of 95.53%. w/w was detected. CONCLUSION: Biomasses cocktails might be a legitimate possibility to provide a cheaper technical solution for industrial applications than immobilized enzymes. The use of fungal biomass cultivated on the microalgae recovered from wastewater treatment for the catalysis of transesterification reaction provides an additional piece of the puzzle of biorefinery. Optimizing the transesterification reaction led to a valid prediction model with a final FAME concentration of 95.53%, w/w.

Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG).

An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

Giardiasis in symptomatic children from Sharkia, Egypt: genetic assemblages and associated risk factors.

Giardia intestinalis (G. intestinalis) is a common enteric protozoan parasite worldwide and in Egypt. Identification of true prevailing Giardia assemblages helps in identification of the sources of infection. The study's aim was to determine the true prevalence of Giardia assemblages in Egyptian children from Sharkia governorate presenting with gastrointestinal symptoms and to investigate their association with molecularly detected Giardia. A total of 617 stool specimens were collected from children presenting with gastrointestinal symptoms in Alquraeen, Sharkia governorate, Egypt for 17 months. All stool specimens were microscopically examined by wet mount smear before and after stool concentration to recover parasitic stages. Giardia copro-DNA was amplified from microscopically detected stool specimens using Copro-nPCR targeting the tpi gene for Giardia, followed by sequencing products of nPCR. The molecular prevalence of Giardia among symptomatic children was 9.88%, 83% of which were assemblage B and 17% were assemblages A. Giardia affected both sexes and all ages and was most prevalent in preschool children. Abdominal pain was the most common GIT symptom followed by diarrhoea. However, none of the patients' demographic variables (sex, age, weight and height) nor clinical symptoms showed significant association with molecular detection of Giardia. Giardia was common among symptomatic children from Sharkia, Egypt, with the predominance of assemblage B, which suggests the possibility of sharing common transmission source and route. Giardia had age, sex and clinical symptom distributions without statistical significance. The results necessitate further genomic studies targeting multiple gene targets for a better understanding of the ecology, dynamics of transmission, pathogenicity and clinical impact of Giardia infection, to improve its management and strategic control.