Reduction of Some Enzymes Produced by Irradiated Fungal Strains Isolated from Certain Medicinal Plants

Medicinal plants normally carry high bioburden due to their origin, offering potentials hazards to the consumer. Fungal extracellular enzymes play a role in biodeterioration of medicinal plants and undesirably effect human health cause immunotoxigenic diseases. Ten different medicinal plants were screened for their mold contamination. The isolates were identified as genera Aspergillus, Alternaria, Cladosporium and Penicillum, they tested for their enzymatic activities (protease, cellulase and lipase). All isolates were able to produce enzymes under study in a varying degree. Of the fungal isolates , Asp. niger and Asp. flavus showed high protease activity. Whereas P. roquefortii and Asp. parasiticus were the more potent strains producing cellulase. Lipase was found to be highly produced by Asp. fumigatus and P. italicum . The present study presumes to monitor the fungal growth and enzymatic activity in relation to gamma irradiation. The results showed that, the log number of survivors was found to be inversely proportional to the irradiation dose. 6.0 and 4.0 kGy resulted in complete inhibition the growth of  highly protease produces (Asp. niger and Asp.flavus). While,  P. roquefortii and Asp. parasiticus which highly produce cellulase were inhibited at 4.0 and 6.0 kGy, respectively. On the other hand, Asp.fumigatus and P. italicum which highly produce lipase were inhibited at dose 4.0 and 6.0 kGy, respectively. Subleathal doses of gamma- irradiation resulted in high significant reduction of enzymes production. The stability of acquired character for the strains under study which were exposed to gamma-irradiation was studied. Statistical analysis revealed that, the enzyme activities estimated after 6 months of storage gave difference data between the strains under study. This study indicates that gamma irradiation is an effective treatment for reduction of fungi contaminating medicinal plants as well as its ability to produce some enzymes. Key words: medicinal plants, fungal isolates, enzymatic activity, gamma irradiation

Growth Performance, Serum Biochemical, Economic Evaluation and IL6 Gene Expression in Growing Rabbits Fed Diets Supplemented with Zinc Nanoparticles

Zinc nanoparticles showed a great potential as mineral feed supplements in animals than the conventional sources. However, this potential has not been applied in rabbit nutrition. Therefore, this study was designed to evaluate the effects of dietary nano-zinc oxide on the growth performance, serum biochemical, economic parameters and gene expression of interleukin-6 in growing rabbits. A total of 120 male, five-week-old New Zealand White (NZW) rabbits were randomly distributed into four equal groups. The control group (Z0) was fed on a basal diet with zinc free premix; the other three experimental groups received the basal diet supplemented with 60 mg zinc oxide/kg diet (Z1), 60 mg nano-zinc oxide/kg diet (Z2) and 30 mg nano-zinc oxide/kg diet (Z3), respectively. The results revealed that rabbits in the groups Z2 and Z3 had higher body weight, daily weight gain, daily feed intake, serum total protein, globulin, IgG and SOD when compared with those of groups Z0 and Z1 (P<0.001). In addition, growth hormone level was higher in Z3 group than in the other groups, whereas no significant differences were recorded among the treated groups in respect to serum TSH concentration (P>0.05). Hepatic and serum zinc contents were high in Z2 and Z3 groups, but the copper contents were decreased. Rabbits of group Z3 yielded the highest gross margin with the lowest expenses to produce 1 kg of live weight compared with the others. The production of IL6 in spleen was increased in Z3 group than that in the other groups. Thus, it can be concluded that nano-zinc oxide at a concentration of 30 mg/kg diet may be used instead of the traditional zinc sources in rabbit diets

Probabilistic performance modelling when using partial reconfiguration to accelerate streaming applications with non-deterministic task scheduling

Many streaming applications composed of multiple tasks self-adapt their tasks’ execution at runtime as response to the processed data. This type of application promises a better solution to context switches at the cost of a non-deterministic task scheduling. Partial reconfiguration is a unique feature of FPGAs that not only offers a higher resource reuse but also performance improvements when properly applied. In this paper, a probabilistic approach is used to estimate the acceleration of streaming applications with unknown task schedule thanks to the application of partial reconfiguration. This novel approach provides insights in the feasible acceleration when partially reconfiguring regions of the FPGA are partially reconfigured in order to exploit the available resources by processing multiple tasks in parallel. Moreover, the impact of how different strategies or heuristics affect to the final performance is included in this analysis. As a result, not only an estimation of the achievable acceleration is obtained, but also a guide at the design stage when searching for the highest performance

Association of polymorphisms in kappa casein gene with milk traits in Holstein Friesian cattle

The genetic polymorphism of milk proteins can be a useful guide for selection and an informative marker in breeding research. Kappa casein (CSN3) is a standout amongst the most vital milk proteins in the mammals that assumes a crucial part in the milk quality and coagulation. Considering polymorphism of CSN3 and its relationship with milk characteristics in Holstein Friesian cattle was the target of the current study. The PCRRFLP utilizing Hind III endonuclease enzyme and DNA sequencing were performed on DNA samples extracted from fifty animals. Restriction digestion analysis of 633bp PCR product indicated two genotypes AA (uncut 633 bp), and AB (633, 416, and 217 bp) with higher frequency of A allele (0.80) than B allele (0.20). Animals with AB genotypes had a significantly higher milk yield and SNF % (10724 and 9.26%, respectively), whereas animals with AA genotype had a superior estimate effect on fat (3.36%) and proteins (3.14%). Comparison of the nucleotide sequences between different genotypes revealed only one nucleotide changes (A405G), corresponding to the same amino acid residue alanine. Molecular selection for animals carrying the B allele could impact breeding programs for dairy production in Egypt

Voltage security constrained reactive power planning considering the costs and performance of VAR devices

This paper deals with optimal allocation of fast and slow VAR devices under different load levels. These devices are utilized to maintain system security in normal and contingency states, where corrective and preventive controls are implemented for the contingency cases. Load shedding and fast VAR devices are used in the corrective state in order to quickly restore system stability even though they are expensive, while cheap slow VAR devices can be used in the preventive state to obtain the desired security level. The main objective of this paper is to make a trade-off between economy and security by determining the optimal combination of fast and slow controls (load shedding, new slow and fast VAR devices). To meet the desired security limits, a variety of constraints have to be considered during the investigated transitions states. The proposed RPP problem is a combinatorial optimization problem, which cannot be solved easily by conventional optimization methods. Swarm optimization methods are reported to be efficient to solve combinatorial optimization problems. This paper discovers the efficiency of Particle Swarm Optimization (PSO) and Evolutionary PSO (EPSO) in solving the proposed RPP problem. The proposed approaches have been successfully tested on IEEE 14 bus system and a comparative study is illustrated

Application of metaheuristic methods to reactive power planning: A comparative study for GA, PSO and EPSO

This paper proposes the application of metaheuristic methods to Reactive Power Planning (RPP). RPP involves optimal allocation of reactive sources to satisfy voltage constraints during normal and contingency states. The main objective of the proposed RPP is to make a trade-off between economy and security by determining the optimal combination of fast and slow controls (load shedding, new slow and fast VAR devices). The overall problem is formulated as a large scale mixed integer nonlinear programming problem. The proposed RPP problem is a combinatorial optimization problem, which cannot be solved easily by conventional optimization methods. Metaheuristic methods are reported to be efficient to solve combinatorial optimization problems. Among the well-known metaheuristic methods, this paper discovers the efficiency of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Evolutionary PSO (EPSO) in solving the proposed RPP problem. The proposed approaches have been successfully tested on IEEE 14 bus system and a comparative study is illustrated

Multi-load level reactive power planning considering slow and fast VAR devices by means of particle swarm optimisation

The problem of optimal allocation of fast and slow reactive power VAR devices under different load levels is addressed. These devices are supposed to be utilised to maintain system security in normal and contingency states, where corrective and preventive controls are implemented for the contingency cases. Load shedding and fast VAR devices are used in the corrective state in order to restore the system stability very quickly, even though they are highly expensive, whereas cheap slow VAR devices can be used in the preventive state to obtain the desired security level. The main objective is to establish a trade-off between economy and security by determining the optimal combination of fast and slow controls (load shedding, new slow and fast VAR devices). To meet the desired steady-state security limits, a variety of constraints have to be considered during the investigated transition states. The overall problem is formulated as a large-scale mixed-integer nonlinear programming problem. Particle swarm optimisation as an efficient method for solving such problems is applied to solve the problem. The proposed approach has been successfully tested on the IEEE-14 as well as IEEE-57 bus systems