Structural characterization, antioxidant and anti-uropathogenic potential of biogenic silver nanoparticles using brown seaweed Turbinaria ornata.

Alternative treatment strategies for urinary tract infections (UTIs) are becoming more necessary due to increasing drug resistance patterns in uropathogens. Nanoparticle-based therapeutics is emerging as a way to treat UTIs. In the present study, using Turbinaria ornata extract, silver nanoparticles (AgNPs) were synthesized, characterized, and their anti-uropathogenic activity was evaluated. The stability and formation of synthesized To-AgNPs were confirmed by UV-visible spectroscopy, FTIR, XRD, SEM, and DLS. An FTIR spectrum confirmed the presence of seaweed functional groups in To-AgNPs, a XRD analysis confirmed their crystalline nature, and SEM imaging confirmed their spherical nature with an average size of 73.98 nm with diameters ranging from 64.67 to 81.28 nm. This was confirmed by TEM results. DLS determined that the cumulant hydrodynamic diameter of To-AgNPs was 128.3 nm with a PdI of 0.313 and the zeta potential value were found to be -63.3 mV which indicates the To-AgNPs are negatively charged and more stable. DPPH assays were used to assess the antioxidant activity of biosynthesized To-AgNPs, while an agar well diffusion method was used to test the antibacterial activity against uropathogens, including Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Enterococcus faecalis, and Klebsiella pneumoniae. The To-AgNPs showed the highest susceptibility to S. aureus (15.75 ± 0.35 mm) and E. coli (15 ± 0.7 mm) with MIC values of 0.0625 and 0.125 mg/ml, respectively in macro broth dilution method and observed considerable membrane damage under CLSM and SEM. To-AgNPs displayed stronger antioxidant and antimicrobial activity, suggesting they may be developed as a new class of antimicrobial agents for treating UTIs.

Hydrothermal synthesis of spindle structure copper ferrite-graphene oxide nanocomposites for enhanced photocatalytic dye degradation and in-vitro antibacterial activity.

In this study, a one-step hydrothermal approach was used to make pure magnetic copper ferrite (CuFe2O4) and copper ferrite-graphene oxide (CuFe2O4-rGO) nanocomposites (NCs) and spinel structure CuFe2O4 with a single phase of tetragonal CuFe2O4-rGO-NCs was confirmed by the XRD. Then, characterization of CuFe2O4-rGO-NCs was done using ng Raman spectroscopy, FT-IR, TGA-DTA, EDS, SEM, and TEM. The synthesized NCs was exposed to UV light to evaluate its photocatalytic activity for the degradation of methylene blue (MB) and rhodamine B (RhB) with CuFe2O4 and CuFe2O4-rGO-NCs, respectively. The catalyst CuFe2O4-rGO-NCs provided higher degradation of MB (94%) than for RhB (86%) under UV light irradiation compared to CuFe2O4. Further, the antibacterial activities of CuFe2O4-NPs and CuFe2O4-rGO-NCs were tested against Gram-negative and -positive bacterial pathogens such as Vibrio cholera (V. cholera); Escherichia coli (E. coli); Pseudomonas aeruginosa (P. aeruginosa); Bacillus subtilis (B. subtilis); Staphylococcus aureus (S. aureus); and Staphylococcus epidermidis (S. epidermidis) by well diffusion method. At 100 µg/mL concentrations of CuFe2O4-rGO-NCs, maximal growth inhibition was shown against E. coli (18 mm) and minimum growth inhibition against S. epidermidis (12 mm). This study suggests that CuFe2O4-rGO-NCs as a high-efficacy antibacterial material and plays an important role in exhibiting higher sensitivity depending on concentrations. The results encourage that the synthesized CuFe2O4-rGO-NCs can be used as a promising material for the antibacterial activity and also for dye degradation in the water/wastewater treatment plants.

Multifarious extraction methodologies for ameliorating lipid recovery from algae.

Amongst the current alternatives, algae were proven to be a promising source of biofuel, which is renewable and capable of meeting world demand for transportation fuels. However, a suitable lipid extraction method that efficiently releases the lipids from different algal strains remains a bottleneck. The multifarious pretreatment methods are prevalent in this field of lipid extraction, and therefore, this article has critically reviewed the various lipid extraction methods for ameliorating the lipid yield from algae, irrespective of the strains/species. Physical, mechanical, and chemical are the different types of pretreatment methods. In this review, methodologies such as homogenization, sonication, Soxhlet extraction, microwave treatment, and bead-beating, have been studied in detail and are the most commonly used methods for lipid extraction. Specific advanced/emerging processes such as supercritical CO2 extraction, ionic liquid, and CO2 switchable solvent-based algal lipid extraction are yet to be demonstrated at pilot-scale, though promising. The extraction of lipids has to be financially conducive, environmentally sustainable, and industrially applicable for further conversion into biodiesel. Hence, this paper discusses variable pretreatment for lipid extraction and imparts a comparative analysis to elect an efficient, economically sound lipid extraction method for pilot-scale biodiesel production.

An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques.

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.

Trace Metal Inference on Seaweeds in Wandoor Area, Southern Andaman Island.

Trace metals concentrations were studied for three different seasons on four seaweed species (Halimeda gracilis, Padina pavonica, Sargassum swartzii and Turbinaria ornata) from Wandoor area of southern Andaman Island. Though diversified seaweed population records in the study area, the studied four species were predominantly present in all the seasons. The observed average levels on trace metals concentrations were Cd 0-0.311 µg/g, Cr 0.002-0.334 µg/g, Cu 0.003-0.291 µg/g, Mn 0.118-7.736 µg/g, Pb 0-0.165 µg/g and Zn 0.019-5.537 µg/g respectively. Brown seaweed P. pavonica showed higher level of Mn concentration as 7.73 µg/g during northeast monsoon reflects physiological properties and sequestrative nature. Our result suggests, the metal concentrations were not higher with reference to Dadolahi's metal pollution index. Based on univariate analysis of variance, there is no significant variation found in both species and seasons. To maintain the tradition of the study area, continues monitoring is essential for better understanding and ecosystem conservation.

Synthesis of silver nanoparticles using bacterial exopolysaccharide and its application for degradation of azo-dyes.

In this study, the synthesis and characterization of exopolysaccharide-stabilized sliver nanoparticles (AgNPs) was carried out for the degradation of industrial textile dyes. Characterization of AgNPs was done using surface plasmon spectra using UV-Vis spectroscopy, X-ray diffraction (XRD) and Raman spectroscopy. The morphological nature of AgNPs was determined through transmission electron microscopy (TEM), scanning electron microscopy (SEM) and atomic force microscopy (AFM), which indicated that the AgNPs were spherical in shape, with an average size of 35 nm. The thermal behaviour of AgNPs revealed that it is stable up to 437.1 °C and the required energy is 808.2J/g in TGA-DTA analysis. Ability of EPS stabilized AgNPs for degradation of azo dyes such as Methyl orange (MO) and Congo red (CR) showed that EPS stabilized AgNPs were found to be efficient in facilitating the degradation process of industrial textile dyes. The electron transfer takes place from reducing agent to dye molecule via nanoparticles, resulting in the destruction of the dye chromophore structure. This makes EPS-AgNPs a suitable, cheap and environment friendly candidate for biodegradation of harmful textile dyes.

Biomonitoring along the Tropical Southern Indian Coast with Multiple Biomarkers.

We assessed the spatial and temporal variations of pollution indicators and geochemical and trace metal parameters (23 in total) from water and sediment (144 samples) of three different eco-niches (beach, fishing harbor, and estuary) in larger coastal cities of southern India (Cuddalore and Pondicherry) for one year. A total of 120 marine Pseudomonas isolates were challenged against different concentrations of copper solutions and 10 different antibiotics in heavy metal and antibiotic resistance approaches, respectively. The study shows that 4.16% of the isolates could survive in 250 mM of copper; 70% were resistant to minimum concentrations. Strains were resistant (98.4%) to at least one antibiotic in Cuddalore compared to the Pondicherry (78.4%) region. Pollution index (PI) (0-14.55) and antibiotic resistance index (ARI) (0.05-0.10) ratio indicated that high bacterial and antibiotic loads were released into the coastal environment. The degree of trace metal contamination in sediments were calculated by enrichment factor (EF), contamination factor (CF), pollution load index (PLI), and geo-accumulation index (Igeo). Statistical parameters like two-way analysis of variance (ANOVA), correlation, factor analysis and scatter matrix tools were employed between the 23 parameters in order to find sources, pathways, disparities and interactions of environmental pollutants. It indicates that geochemical and biological parameters were not strongly associated with each other (except a few) and were affected by different sources. Factor analysis elucidated, 'microbe-metal' interaction (Factor 1-48.86%), 'anthropogenic' factor (Factor 2-13.23%) and 'Pseudomonas-Cadmium' factor (Factor 3-11.74%), respectively.

Physicochemical parameters aid microbial community? A case study from marine recreational beaches, Southern India.

A total of 176 (water and sediment) samples from 22 stations belonging to four different (urban, semi-urban, rural, and holy places) human habitations of Tamil Nadu beaches were collected and analyzed for physiochemical and microbial parameters during 2008-2009. Bacterial counts were two- to tenfold higher in sediments than in water due to strong bacterial aggregations by dynamic flocculation and rich organic content. The elevated bacterial communities during the monsoon explain rainfalls and several other wastes from inlands. Coliform counts drastically increased at holy and urban places due to pilgrimage and other ritual activities. Higher values of the pollution index (PI) ratio (>1) reveals, human fecal pollutions affect the water quality. The averaged PI ratio shows a substantial higher microbial contamination in holy places than in urban areas and the order of decreasing PI ratios observed were: holy places > urban areas > semi-urban areas > rural areas. Correlation and factor analysis proves microbial communities were not related to physicochemical parameters. Principal component analysis indicates 55.32 % of the total variance resulted from human/animal fecal matters and sewage contaminants whereas 19.95 % were related to organic contents and waste materials from the rivers. More than 80 % of the samples showed a higher fecal coliform and Streptococci by crossing the World Health Organization's permissible limits.

Antibiotic resistant pathogens versus human impacts: a study from three eco-regions of the Chennai coast, southern India.

We assessed the occurrence of pollution indicators and antibiotic resistant bacterial isolates from water and sediment samples of three different eco-regions of the Chennai coast between March - May of 2010. Total of 960 bacterial strains belonging to four genera were isolated which show the highest frequencies of resistance to vancomycin (53.6%) and penicillin (52.6%) (except Enterococcus sp., which is highly resistant to erythromycin) and lowest frequencies of resistance to chloramphenicol (3.43%), ciprofloxacin (3.95%), gentamicin (4.68%), and tetracycline (6.97%). The E. coli, Vibrio sp., Salmonella sp. and Enterococcus sp. show high frequency of resistance to 2-5 antibacterials of 60.4%, 45.83%, 69.16% and 46.6%, respectively. High pollution indices (PI - 6.66-14.06) and antibiotic resistance indices (ARI - 0.29-0.343) indicate that the coastal environment is highly exposed to antibiotic sources that suggesting to avoid direct contact.