Synthesis and characterization of fluorophore attached silver nanoparticles

Silver nanoparticles stabilized by soluble starch were synthesized and characterized. In vivo studies in rats showed no toxicity and revealed their distribution in various tissues and permeability across BBB. This starch stabilized silver nanoparticles have good biological characteristics to act as a potential promising vector for gene/drug delivery.     

Selective enumeration of Lactobacillus delbrueckii ssp. bulgaricus,Streptococcus thermophilus,Lactobacillus acidophilus,bifidobacteria, Lactobacillus casei,Lactobacillus rhamnosus,and propionibacteria

Nineteen bacteriological media were evaluated to assess their suitability to selectively enumerate Lactobacillus delbrueckii ssp. bulgaricus, Streptococcus thermophilus, Lactobacillus casei, Lactobacillus rhamnosus, Lactobacillus acidophilus, bifidobacteria, and propionibacteria. Bacteriological media evaluated included Streptococcus thermophilus agar, pH modified MRS agar, MRS-vancomycine agar, MRS-bile agar, MRS-NaCl agar, MRS-lithium chloride agar, MRS-NNLP (nalidixic acid, neomycin sulfate, lithium chloride and paramomycine sulfate) agar, reinforced clostridial agar, sugar-based (such as maltose, galactose, sorbitol, manitol, esculin) media, sodium lactate agar, arabinose agar, raffinose agar, xylose agar, and L. casei agar. Incubations were carried out under aerobic and anaerobic conditions at 27, 30, 37, 43, and 45 degrees C for 24, 72 h, and 7 to 9 d. S. thermophilus agar and aerobic incubation at 37 degrees C for 24 h were suitable for S. thermophilus. L. delbrueckii ssp. bulgaricus could be enumerated using MRS agar (pH 4.58 or pH 5.20) and under anaerobic incubation at 45 degrees C for 72 h. MRS-vancomycine agar and anaerobic incubation at 43 degrees C for 72 h were suitable to enumerate L. rhamnosus. MRS-vancomycine agar and anaerobic incubation at 37 degrees C for 72 h were selective for L. casei. To estimate the counts of L. casei by subtraction method, counts of L. rhamnosus on MRS-vancomycine agar at 43 degrees C for 72 h under anaerobic incubation could be subtracted from total counts of L. casei and L. rhamnosus enumerated on MRS-vancomycine agar at 37 degrees C for 72 h under anaerobic incubation. L. acidophilus could be enumerated using MRS-agar at 43 degrees C for 72 h or Basal agar-maltose agar at 43 degrees C for 72 h or BA-sorbitol agar at 37 degrees C for 72 h, under anaerobic incubation. Bifidobacteria could be enumerated on MRS-NNLP agar under anaerobic incubation at 37 degrees C for 72 h. Propionibacteria could be enumerated on sodium lactate agar under anaerobic incubation at 30 degrees C for 7 to 9 d. A subtraction method was most suitable for counting propionibacteria in the presence of other lactic acid bacteria from a product. For this method, counts of lactic bacteria at d 3 on sodium lactate agar under anaerobic incubation at 30 degrees C were subtracted from counts at d 7 of lactic bacteria and propionibacteria.     

Alginate stabilized silver nanocube-Rh6G composite as a highly selective mercury sensor in aqueous solution

Alginate-stabilized silver nanocubes are synthesized via a reduction method and are characterized by UV-Vis and fluorescence spectroscopies, SEM, AFM and HRTEM analyses. A silver nanocubes (Ag NCbs) based sensor for detecting Hg2+ ions in aqueous solution has been developed using Rh6G as an external spectroscopic probe. Using this system, Hg2+ ions (as low as 1×10(-10) mol L(-1)) are recognized in aqueous media via a colorimetric method with very high selectivity and sensitivity over other metal ions namely Fe2+, Zn2+, Pb2+, Cu2+, Sn2+, Cd2+, Ni2+, and Co2+.     

Survival of Lactobacillus acidophilus, Lactobacillus paracasei subsp. paracasei, Lactobacillus rhamnosus, Bifidobacterium animalis and Propionibacterium in cheese-based dips and the suitability of dips as effective carriers of probiotic bacteria

The suitability of cheese-based dips as a delivery vehicle for probiotic bacteria including Lactobacillus acidophilus, Lactobacillus paracasei subsp. paracasei, Lactobacillus rhamnosus, Bifidobacterium animalis, and Propionibacterium freudenreichii subsp. shermanii was studied by evaluating the survival of these organisms in dips. Effects of organic acids, oils and gums, -cysteine and NaHCO₃ on the survival of probiotics in cheese-based dips were also studied. Eight different combinations and five individual bacteria as controls of these probiotic bacteria were added to 21 batches of French onion dip and selective enumeration of these probiotic bacteria was carried out over a period of 10 weeks of storage. The population of L. acidophilus and B. animalis reduced by 1 log and 2 log per g, respectively. However, when the inoculation level of these bacteria were increased to 8 log per g, they maintained a population of more than 6 log over the shelf life. L. paracasei subsp. paracasei and L. rhamnosus remained at the inoculated level or increased slightly during the storage. A rapid increase in the population of P. freudenreichii subsp. shermanii occurred to attain more than the inoculation level following reduction in their number by 3 log. Except bacterial interaction, no other factors showed significant effect on the survival of individual probiotic bacteria. Each of L. acidophilus, B. animalis, L. paracasei subsp. paracasei, and L. rhamnosus showed varied levels of antogonism, while P. freudenreichii subsp. shermanii showed no effect. Any combination of these bacteria can be used as probiotics in cheese-based French onion dip. However, the inoculation level should be 8 log per g for L. acidophilus and B. animalis and 7 log per g for L. paracasei subsp. paracasei, and/or L. rhamnosus to obtain greater than 6 log of individual bacterial population at the end of shelf life.     

Antimicrobial effects of probiotic bacteria against selected species of yeasts and moulds in cheese-based dips

The antimicrobial properties of selected probiotic bacteria against Aspergillus niger , Penicillium roqueforti , Fusarium spp., Candida albicans and Saccharomyces cerevisiae were examined. Well diffusion and spot and streak methods showed strong inhibition effect of probiotic bacteria and their metabolites against moulds and minimal effect against yeasts. Among the moulds species tested, the inhibitory effect was strongest against Fusarium spp., moderate against Penicillium roqueforti and minimal against A. niger . All strains of Lactobacillus rhamnosus and L. paracasei subsp. paracasei showed maximum inhibitory effect. When probiotic bacteria and yeasts and moulds were co-cultured in broth media, strains of L. rhamnosus showed maximum inhibitory effect, whereas L. paracasei subsp. paracasei , L. acidophilus , Bifidobacterium animalis and Propionibacterium showed moderate inhibitory effect against C. albicans . Saccharomyces cerevisiae was minimally controlled by probiotic bacteria. Pre-grown probiotic bacterial culture metabolites controlled yeasts and moulds more effectively than their freeze-dried or frozen forms. Adding metabolites of probiotic bacteria (5% w/w) showed an effective control against A. niger , Fusarium spp. and C. albicans during the shelf life of 10 weeks at 4 °C and no colonies of yeasts and moulds were formed on the surface of the dip.     

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience. Conventional data aggregation models for Fog enabled IoT environments possess high computational complexity and communication cost. Therefore, in order to resolve the issues and improve the lifetime of the network, this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer (HDAG-CBMO) technique. The HDAG-CBMO technique derives a fitness function from many relational matrices, like residual energy, average distance to neighbors, and centroid degree of target area. Besides, a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles. Moreover, a learning based data offloading method has been developed for reducing the response time to IoT user requests. A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.