Significance of Carbon Nanotube in Flame-Retardant Polymer/CNT Composite: A Review

In this review, flame-retardant characteristics of polymer/carbon nanotube composite have been focused. Nonflammability behavior of carbon nanotube filler with different types of polymer matrices has been discussed. In addition, thermal, mechanical, and electrical properties of these materials have also been argued. Main types of polymers considered are epoxy, polystyrene, polyaniline, polypropylene, and polyurethane. Considerable enhancement in nonflammability properties of polymers with carbon nanotube was studied using limiting oxygen index measurement, UL 94 tests, and cone calorimetry. Toward the end, significance of flame-retardant composites in aerospace, electronics, radar absorbing materials, electromagnetic shielding, and wind turbine blades has been conversed.     

Effect of Bifidobacterium thermacidophilum probiotic feeding on enterohemorrhagic Escherichia coli O157:H7 infection in BALB/c mice

The effectiveness of Bifidobacterium thermacidophilum RBL 71 as a probiotic against enterohemorrhagic Escherichia coli O157:H7 infection was studied using a murine model. BALB/c mice were fed the probiotic for 7 days before or after single challenge with E. coli O157:H7. Fecal B. thermacidophilum RBL 71 and E. coli O157:H7 counts obtained by selective culturing methods were assessed for 1 week before and after infection while feed intake, body weight and composition were monitored during 1 week after infection. Histology of gut tissue (jejunum, ileum and colon) and production of fecal IgA antibodies and serum IgG+IgM antibodies to E. coli O157:H7 were analyzed until 1 and 2 weeks post-infection, respectively. The pathogenicity of E. coli O157:H7, marked by body weight loss and intestinal histopathological changes in the infected group, was significantly reduced in the B. thermacidophilum-treated group. Feeding B. thermacidophilum RBL 71 for 7 days before infection resulted in greater post-challenge feed intake and weight gain and lower fecal levels of E. coli O157:H7. Post-infection levels of anti-E. coli O157:H7-specific IgA in feces and IgG+IgM in serum were higher in mice fed bifidobacteria. Intestinal injuries were also attenuated and reaction of the lymphoid component in the mucosa of the ileum was greater in the bifidobacteria-fed group. A lesser degree of protection against E. coli O157:H7 infection was observed when bifidobacteria were given during the 7 days after E. coli O157:H7 infection. These results demonstrate that feeding the probiotic B. thermacidophilum RBL 71 to mice can reduce the severity of E. coli O157:H7 infection, and suggest that this strain represents a good candidate for the prevention of enteric infections in human.     

A rapid turbidometric microplate bioassay for accurate quantification of lactic acid bacteria bacteriocins

A 1 day turbidometric microplate bioassay (TMB) was developed for the rapid, accurate and precise quantification of lactic acid bacteria (LAB) bacteriocins (nisin Z and pediocin PA-1). Parameters such as the concentration of the indicator strains and the incubation time were optimized for each bacteriocin. A high correlation coefficient (r²=0.992±0.004) was obtained for the exponential regression in the nisin Z concentration range of 20–120 ng/ml with 1×10⁷ CFU indicator strain (Pediococcus acidilactici UL5) and an incubation time of 3 h. Using these parameters, the detection limit was estimated at 80 ng/ml (3.2 IU/ml), compared to 300 ng/ml for the agar diffusion assay (ADA). High precision (<7%) and accuracy (10%) were obtained for all nisin Z concentrations tested. Similar results were obtained with pediocin PA-1 with r²=0.993±0.005, a precision (8.2%) and an accuracy lower than 15%.     

In situ semi-quantitative analysis of polluted soils by laser-induced breakdown spectroscopy (LIBS)

Time-saving, low-cost analyses of soil contamination are required to ensure fast and efficient pollution removal and remedial operations. In this work, laser-induced breakdown spectroscopy (LIBS) has been successfully applied to in situ analyses of polluted soils, providing direct semi-quantitative information about the extent of pollution. A field campaign has been carried out in Brittany (France) on a site presenting high levels of heavy metal concentrations. Results on iron as a major component as well as on lead and copper as minor components are reported. Soil samples were dried and prepared as pressed pellets to minimize the effects of moisture and density on the results. LIBS analyses were performed with a Nd:YAG laser operating at 1064 nm, 60 mJ per 10 ns pulse, at a repetition rate of 10 Hz with a diameter of 500 μm on the sample surface. Good correlations were obtained between the LIBS signals and the values of concentrations deduced from inductively coupled plasma atomic emission spectroscopy (ICP-AES). This result proves that LIBS is an efficient method for optimizing sampling operations. Indeed, "LIBS maps" were established directly on-site, providing valuable assistance in optimizing the selection of the most relevant samples for future expensive and time-consuming laboratory analysis and avoiding useless analyses of very similar samples. Finally, it is emphasized that in situ LIBS is not described here as an alternative quantitative analytical method to the usual laboratory measurements but simply as an efficient time-saving tool to optimize sampling operations and to drastically reduce the number of soil samples to be analyzed, thus reducing costs. The detection limits of 200 ppm for lead and 80 ppm for copper reported here are compatible with the thresholds of toxicity; thus, this in situ LIBS campaign was fully validated for these two elements. Consequently, further experiments are planned to extend this study to other chemical elements and other matrices of soils.     

Characterization of a disulphide-bound Pir-cell wall protein (Pir-CWP) of Yarrowia lipolytica

In this work we have studied the disulphide-bound group of cell wall mannoproteins of Yarrowia lipolytica and Candida albicans. In the case of Y. lipolytica, SDS-PAGE analysis of the beta-mercaptoethanol-extracted material from the purified cell walls of the yeast form, showed the presence of a main polypeptide of 45 kDa and some minor bands in the 100-200 kDa range. This pattern of bands is similar to that obtained in identical extracts in Saccharomyces cerevisiae (Moukadiri et al., 1999), and besides, all these bands cross-react with an antibody raised against beta-mercaptoethanol-extracted material from the purified cell walls of S. cerevisiae, suggesting that the 45 kDa band could be the homologue of Pir4 of S. cerevisiae in Y. lipolytica. To confirm this possibility, the amino-terminal sequences of two internal regions of the 45 kDa protein were determined, and degenerate oligonucleotides were used to clone the gene. The gene isolated in this way codes a 286 amino acid polypeptide that shows homology with the Pir family of proteins of S. cerevisiae (Russo et al., 1992; Toh-e et al., 1993), accordingly we have named this gene YlPIR1. Disruption of YlPIR1 led to a slight increase in the resistance of the cells to calcofluor white, Congo red and zymolyase, but did not cause changes in cell morphology, growth rate or morphological transition.     

MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into binary classes, i.e., Normal and Pneumonia. The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models: MobileNet, DenseNet-201, EfficientNet-B0, and VGG-16, which have been finetuned and trained on 5,856 CXR images. The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision, accuracy, recall, AUC-roc, and f1-score. The model effectively decreases training loss while increasing accuracy. The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%, an accuracy of 92.15%, a recall of 90.90%, an auc-roc score of 90.9%, and f-score of 91.49% with minimal data pre-processing, data augmentation, finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.     

Mine Wastewater Treatment with Upflow Anaerobic Fixed Film Reactors

Mine Water and the Environment - Microbial bioremediation of metals in wastewater by sulfate-reducing bacteria (SRB) has received much attention due to its high efficiency, eco-friendly techniques,...