Primary and Secondary Proteolysis in Eleven Turkish Cheese Varieties

Levels of proteolysis of 75 samples belonging to 11 Turkish cheese varieties, including Civil, Canak, Dil, Divle Tulum, Ezine, Hellim, Malatya, Mihalic, Orgu, Urfa, and Van Otlu, were comparatively studied. The cheeses were mainly produced using traditional methods; however, some varieties were industrially produced. Chemical composition and the levels of soluble nitrogen fractions of the cheeses varied depending on the cheese variety. Gel electrophoresis of the cheeses showed that the samples presented different gel patterns with α_s1-casein being extensively degraded in many cheeses; whereas the hydrolysis of α_s1-casein in Malatya and Hellim was observed to be limited. Peptide profiles by RP-HPLC of the water-soluble fractions were largely different for many of the samples, but some similarities were visualized. Multivariate analysis of the RP-HPLC data grouped the cheeses according to their peptide profiles. The results suggested that each variety of cheese had different levels of proteolysis. The manufacturing technique and ripening conditions employed have played a determinative role on the proteolytic patterns of the cheeses analyzed.     

Analysis of phytohormones and phosphate solubilization in Photorhabdus spp.

Bacteria of the genus Photorhabdus are nematodes (Heterorhabditidae) symbiont and highly entomopathogenic in nature. Present study was concerned to investigate the effects of Photorhabdus temperata M1021 and Photorhabdus luminescens TT01 on plant morphology and physiology (‘Dongjin-beyo’ and ‘waitoc’). A significant (p<0.05) increase in the plant growth attributes including total plant length, root length, biomass, and chlorophyll contents were observed after treatment with culture extracts. Moreover indole-3-acetic acid (IAA) production by respective bacteria was confirmed by GCMS analysis. IAA production through tryptophan independent pathway was confirmed in P. temperata M1021 and P. luminescens TT01. Phosphate solubilization capability was also investigated by growing them on pikovskaya (PVK) agar as well as on PVK liquid medium with pH 7 at 28±2°C for 14 days. IAA production and phosphate solubilization capability of these bacteria could be the potential factors for plant growth promotion.     

Identification of a Small‐Molecule Inhibitor of HIV‐1 Assembly that Targets the Phosphatidylinositol (4,5)‐bisphosphate Binding Site of the HIV‐1 Matrix Protein

The development of drug resistance remains a critical problem for current HIV‐1 antiviral therapies, creating a need for new inhibitors of HIV‐1 replication. We previously reported on a novel anti‐HIV‐1 compound, N²‐(phenoxyacetyl)‐N‐[4‐(1‐piperidinylcarbonyl)benzyl]glycinamide ( 14 ), that binds to the highly conserved phosphatidylinositol (4,5)‐bisphosphate (PI(4,5)P₂) binding pocket of the HIV‐1 matrix (MA) protein. In this study, we re‐evaluate the hits from the virtual screen used to identify compound 14 and test them directly in an HIV‐1 replication assay using primary human peripheral blood mononuclear cells. This study resulted in the identification of three new compounds with antiviral activity; 2‐(4‐{[3‐(4‐fluorophenyl)‐1,2,4‐oxadiazol‐5‐yl]methyl})‐1‐piperazinyl)‐N‐(4‐methylphenyl)acetamide ( 7 ), 3‐(2‐ethoxyphenyl)‐5‐[[4‐(4‐nitrophenyl)piperazin‐1‐yl]methyl]‐1,2,4‐oxadiazole ( 17 ), and N‐[4‐ethoxy‐3‐(1‐piperidinylsulfonyl)phenyl]‐2‐(imidazo[2,1‐b][1,3]thiazol‐6‐yl)acetamide ( 18 ), with compound 7 being the most potent of these hits. Mechanistic studies on 7 demonstrated that it directly interacts with and functions through HIV‐1 MA. In accordance with our drug target, compound 7 competes with PI(4,5)P₂ for MA binding and, as a result, diminishes the production of new virus. Mutation of residues within the PI(4,5)P₂ binding site of MA decreased the antiviral effect of compound 7 . Additionally, compound 7 displays a broadly neutralizing anti‐HIV activity, with IC₅₀ values of 7.5–15.6 μM for the group M isolates tested. Taken together, these results point towards a novel chemical probe that can be used to more closely study the biological role of MA and could, through further optimization, lead to a new class of anti‐HIV‐1 therapeutics.     

Carbon nano onion as versatile contender in polymer compositing and advance application

This review focuses on an inimitable multilayer fullerene material commonly referred as carbon nano onion (CNO). Among carbon nanomaterials, CNO is the least studied form. CNO has unique 0-D structure, small diameter of <10 nm, high electrical conductivity, fine dispersibility, and other superior structural and physical features. Different preparation and functionalization routes have been used for this unique nanocarbon structure. The article summarizes essential available categories of polymer/CNO composite such as polyaniline/CNO, polypyrrole/CNO, polythiophene/CNO, poly(?-caprolactone)/CNO, polystyrene/CNO, poly(diallyldimethyl-ammonium chloride)/CNO, and polyenediyne/CNO. Moreover, significant technical application of CNO-based materials has been discussed in supercapacitor, solar cell, Li-ion battery, dielectric, and biomedical materials.     

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions.     

Hybrid Petri-nets for modelling and performance evaluation of supply chains

Modelling and analysis of complex and co-ordinated supply chains is a crucial task due to its inherent complexity and uncertainty. Therefore, the current research direction is to devise an efficient modelling technique that maps the dynamics of a real life supply chain and assists industrial practitioners in evaluating and comparing their network with other competing networks. Here an effective modelling technique, the hybrid Petri-net, is proposed to efficiently handle the dynamic behaviour of the supply chain. This modelling methodology embeds two enticing features, i.e. cost and batch sizes, in deterministic and stochastic Petri-net for the modelling and performance evaluation of supply chain networks. The model is subsequently used for risk management to investigate the issues of supply chain vulnerability and risk that has become a major research subject in recent years. In the test bed, a simple productive supply chain and an industrial supply chain are modelled with fundamental inventory replenishment policy. Subsequently, its performance is evaluated along with the identification and assessment of risk factors using analytical and simulation techniques respectively. Thus, this paper presents a complete package for industrial practitioners to model, evaluate performance and manage risky events in a supply chain.     

Environment-Friendly Synergistic Abiotic Stress for Enhancing the Yield of Lipids from Oleaginous Yeasts

The microbial lipids isolated from oleaginous yeasts are a potential alternative to tree borne oils. There is a need to optimize and enhance the production of lipid by various stress approaches. In the present study, yeasts are subjected to physico-chemical stresses, and growth, as well as lipid concentration at different time intervals are monitored. It is found that the nanoparticles (NP) such as Ag-NP and Zn-NP have an inhibitory effect on yeast growth. Most yeast strains show an increase in growth and lipid accumulation when ionic liquid (1-ethyl-3-methylimidazolium acetate) ([EMIM][OAc]) and table salt (NaCl) stress are applied. Lipid is chemically characterized using gas chromatography furnished with flame ionization detector (GC-FID), GC/MS, and NMR techniques. It contains a higher percentage of saturated fatty acids (SFA: 74.3%), monounsaturated fatty acids (19.1%) with low amounts of polyunsaturated fatty acids (1.9%). The thermo-stability study reveals that the lipid have higher volatility (380–410 °C) as closely compared with coconut oil, and much lower with respect to the winged bean oil (430–470 °C). The melting point of the lipids (37 °C) is determined through differential scanning calorimetry (DSC). The DSC and physico-chemical properties are supported that the yeast lipids may use as a cocoa-butter substitute. Production of lipid under NaCl stress (200 × 10⁻³ m ) is more than 60.4% higher as compared to the control. However, the combined stress effect of NaCl (200 × 10⁻³ m ) and 15 × 10⁻³ m of [EMIM][OAc] results in more than 96.4% yield of lipid. The exchange of inorganic and organic ions in combined treatment forces the microbial cells to accumulate more amounts of lipid, which may form a lipid-emulsion layer to protect the cell from osmosis. It is interestingly observed that the stress cells shift the flux to accumulate a significantly improved percentage of SFA, which could be provided better protection cover due to its expanded structure, less reactive characteristics, and completely insoluble nature in ionic-aqueous solvent. Practical applications: Oleaginous yeast is multiplied in a very limited space, and easily scalable for sustainable production of lipid to meet its commercial demand. This novel approach for enhancing the yield of lipid with the application of synergistic stress in between NaCl and the green solvent (ionic liquid) is being reported for the first time. This lipid has potential alternative applications as cocoa-butter.     

Intelligent Data Fusion for Smart IoT Environment: A Survey

Wireless Personal Communications - Efficient data collection and communication are key tasks in smart IoT environment consisting of a large number of devices. Here imprecise data are generated due...     

Novel sensing and joint beam and null steering based resource allocation for cross-tier interference mitigation in cognitive femtocell networks

Wireless Networks - Cognitive radios and femtocell networks are gaining much popularity due to the formers ability to carry out unlicensed transmission in licensed bands and the latter’s...     

Diagnosing bot infections using Bayesian inference

Prior research in botnet detection has used the bot lifecycle to build detection systems. These systems, however, use rule-based decision engines which lack automated adaptability and learning, accuracy tunability, the ability to cope with gaps in training data, and the ability to incorporate local security policies. To counter these limitations, we propose to replace the rigid decision engines in contemporary bot detectors with a more formal Bayesian inference engine. Bottleneck, our prototype implementation, builds confidence in bot infections based on the causal bot lifecycle encoded in a Bayesian network. We evaluate Bottleneck by applying it as a post-processing decision engine on lifecycle events generated by two existing bot detectors (BotHunter and BotFlex) on two independently-collected datasets. Our experimental results show that Bottleneck consistently achieves comparable or better accuracy than the existing rule-based detectors when the test data is similar to the training data. For differing training and test data, Bottleneck, due to its automated learning and inference models, easily surpasses the accuracies of rule-based systems. Moreover, Bottleneck’s stochastic nature allows its accuracy to be tuned with respect to organizational needs. Extending Bottleneck’s Bayesian network into an influence diagram allows for local security policies to be defined within our framework. Lastly, we show that Bottleneck can also be extended to incorporate evidence trustscore for false alarm reduction.