Synthesis of novel 4-substituted-7-trifluoromethylquinoline derivatives with nitric oxide releasing properties and their evaluation as analgesic and anti-inflammatory agents

Six derivatives of the general formula 2- or 4-(7-trifluoromethylquinolin-4-ylamino) benzoic acid N'-(nitrooxyacetyl or propionyl) hydrazide and an oxime of the formula 1-[4-(7-trifluoromethylquinolin-4-ylamino)phenyl]ethanone oxime were synthesized and tested for their in vivo anti-inflammatory, analgesic, and ulcerogenic properties, as well as their in vitro nitric oxide release ability. Compound 2-(7-trifluoromethylquinolin-4-ylamino)benzoic acid N'-(2-nitrooxy propionyl)hydrazide 12 showed an anti-inflammatory activity comparable to that of indomethacin in the carrageenan-induced rat paw edema test, and equipotency to glafenine in the acetic acid mice induced writhing model at 100mg/kg p.o., respectively. All the final compounds showed no tendency to induce stomach ulceration in rats; nitric oxide seems to contribute to their excellent safety profile.     

Analysis and frequency of bovine lymphocyte antigen (BoLA-DRB3) alleles in Iranian Holstein cattle

The bovine lymphocyte antigen (BoLA-DRB3) gene encodes cell surface glycoproteins that initiate immune response by presenting processed antigenic peptides to CD4 T helper cells. DRB3 is the most polymorphic bovine MHC class II gene which encodes the peptide-binding groove. DRB3 gene has been extensively evaluated as a candidate marker for association with various bovine diseases and immunological traits. This study describes genetic variability in the BoLA-DRB3 in Iranian Holstein cattle. This is the first study of the DNA polymorphism of the BoLA-DRB3 gene in Iranian Holstein cattle. Hemi-nested PCR-RFLP method is used for identification the frequency of BoLA-DRB3 alleles. The BoLA-DRB3 locus is highly polymorphic in the studied herd (26 alleles). Almost 67% of the alleles were accounted for four alleles (BoLA-DRB3.2*8, *24, *11 and *16) in Iranian Holstein cattle. The DRB3.2*8 allele frequency (26.6%) was higher than the others. The frequencies of the DRB3.2*54, *37, *36, *28, *25, *14, *13, *10, *1 alleles were lower than 1%. Significant distinctions have been found between Iranian Holstein cattle and other cattle breeds studied. In Iranian Holstein cattle the alleles (BoLA-DRB3.2*22, *2 and *16) associated with a lower risk of cystic ovarian disease in Holstein cattle are found. The alleles associated with the resistance to mastitis and to bovine leukemia virus infection BoLA-DRB3.2*11 and *23 are detected with the frequencies 10.4% and 4.4%, respectively. Thus in the Iranian Holstein cows studied are found alleles which are associated with resistance to various diseases. The method of DNA-typing of animals can be used in agricultural practice for BoLA-DRB3 allele genotyping of cattle in order to reduce spreading of alleles providing susceptibility to mastitis or leukemia in cattle herds.     

MicroRNAs and response to therapy in leukemia

A variety of epigenetic factors involved in leukemia pathogenesis. Among various epigenetic factors, microRNAs (miRNAs) have emerged as important players, which affect a sequence of cellular and molecular signaling pathways. Leukemia is known as progressive cancer, which is related to many health problems in the world. It has been shown that the destruction of the blood-forming organs could lead to abnormal effects on the proliferation and development of leukocytes and their precursors. Despite many attempts for approved effective and powerful therapies for patients with leukemia, finding and developing new therapeutic approaches are required. One of the important aspects of leukemia therapy, identification of underlying cellular and molecular mechanisms involved in the pathogenesis of leukemia. Several miRNAs (ie, miR-103, miR-101, mit-7, let-7i, miR-424, miR-27a, and miR-29c) and play major roles in response to therapy in patients with leukemia. miRNAs exert their effects by targeting a variety of targets, which are associated with response to therapy in patients with leukemia. It seems that more understanding about the roles of miRNAs in response to therapy in patients with leukemia could contribute to better treatment of patients with leukemia. Here, for the first time, we summarized various miRNAs, which are involved in response to therapy in the treatment patients with leukemia.     

κ-casein gene (<Emphasis Type="Italic">CSN3</Emphasis>) allelic polymorphism in Russian cattle breeds and its information value as a genetic marker

The frequencies of the κ-casein gene (CSN3) alleles and genotypes have been determined in five Russian cattle breeds (Bestuzhev, Kalmyk, Russian Black Pied, Yaroslavl, and Yakut breeds) by means of PCR-RFLP analysis using two independent restriction nucleases (HinfI and TaqI) and by allele-specific PCR. Typing alleles A and B of CSN3 is of practical importance, because allele B is correlated with commercially valuable parameters of milk productivity (protein content and milk yield) and improves the cheese yielding capacity. The frequencies of the B allele of CSN3 in the breeds studied vary from 0.16 to 0.50; and those of the AB and BB genotypes, from 0.27 to 0.60 and from 0.02 to 0.23, respectively. The Yaroslavl breed had the highest frequencies of CSN3 allele B and genotype BB (0.50 and 0.23, respectively). The frequencies of the B allele and BB genotype in other breeds studied varied from 0.25 to 0.32 and from 0.03 to 0.09, respectively. In none of the breeds studied have the observed and expected heterozygosities been found to differ from each other significantly. However, the observed genotype distributions significantly differ from the expected one in some herds (in most such cases, an excess of heterozygotes is observed). Two herds of the Yaroslavl breed dramatically differ from each other in the heterozygosity level: a deficit (D = ?0.14) and an excess (D = 0.20) of heterozygotes have been observed at the Mikhailovskoe and Gorshikha farms, respectively. In general, however, the heterozygosity of the Yaroslavl breed corresponds to the expected level (D = 0.04). Analysis of breeds for homogeneity with the use of Kulback’s test has shown that all cattle breeds studied are heterogeneous, the CSN3 diversity within breeds being higher than that among different breeds, which is confirmed by low F _st values (0.0025–0.0431). Thus, a DNA marker based on CSN3 gene polymorphism is extremely important for breeding practice as a marker of milk quality; however, it is inapplicable to marking differences between breeds or phylogenetic relationships between cattle breeds because of the high diversity with respect to this locus within breeds.     

Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.     

A Common View of the Opportunities, Challenges, and Research Actions for Pongamia in Australia

Interest in biofuels is increasing in Australia due to volatile and rising oil prices, the need to reduce GHG emissions, and the recent introduction of a price on carbon. The seeds of Pongamia (Millettia pinnata) contain oils rich in C18:1 fatty acid, making it useful for the manufacture of biodiesel and other liquid fuels. Preliminary assessments of growth and seed yield in Australia have been promising. However, there is a pressing need to synthesise practical experience and existing fragmented research and to use this to underpin a well-founded and co-ordinated research strategy to support industry development, including better management of the risks associated with investment. This comprehensive review identifies opportunities for Pongamia in Australia and provides a snapshot of what is already known and the risks, uncertainties, and challenges based on published research, expert knowledge, and industry experience. We conclude that whilst there are major gaps in fundamental understanding of the limitations to growth of Pongamia in Australia, there is sufficient evidence indicating the potential of Pongamia as a feedstock for production of biofuel to warrant investment into a structured research and development program over the next decade. We identify ten critical research elements and propose a comprehensive research approach that links molecular level genetic research, paddock scale agronomic studies, landscape scale investigations, and new production systems and value chains into a range of aspects of sustainability.     

Estimating allowable take for an increasing bald eagle population in the United States

Effectively managing take of wildlife resulting from human activities poses a major challenge for applied conservation. Demographic data essential to decisions regarding take are often expensive to collect and are either not available or based on limited studies for many species. Therefore, modeling approaches that efficiently integrate available information are important to improving the scientific basis for sustainable take thresholds. We used the prescribed take level (PTL) framework to estimate allowable take for bald eagles (Haliaeetus leucocephalus) in the conterminous United States. We developed an integrated population model (IPM) that incorporates multiple sources of information and then use the model output as the scientific basis for components of the PTL framework. Our IPM is structured to identify key parameters needed for the PTL and to quantify uncertainties in those parameters at the scale at which the United States Fish and Wildlife Service manages take. Our IPM indicated that mean survival of birds >1 year old was high and precise (0.91, 95% CI = 0.90–0.92), whereas mean survival of first-year eagles was lower and more variable (0.69, 95% CI = 0.62–0.78). We assumed that density dependence influenced recruitment by affecting the probability of breeding, which was highly imprecise and estimated to have declined from approximately 0.988 (95% CI = 0.985–0.993) to 0.66 (95% CI = 0.34–0.99) between 1994 and 2018. We sampled values from the posterior distributions of the IPM for use in the PTL and estimated that allowable take (e.g., permitted take for energy development, incidental collisions with human made structures, or removal of nests for development) ranged from approximately 12,000 to 20,000 individual eagles depending on risk tolerance and form of density dependence at the scale of the conterminous United States excluding the Southwest. Model-based thresholds for allowable take can be inaccurate if the assumptions of the underlying framework are not met, if the influence of permitted take is under-estimated, or if undetected population declines occur from other sources. Continued monitoring and use of the IPM and PTL frameworks to identify key uncertainties in bald eagle population dynamics and management of allowable take can mitigate this potential bias, especially where improved information could reduce the risk of permitting non-sustainable take.     

Improving load balancing for data-duplication in big data cloud computing networks

Data transmission and retrieval in a cloud computing environment are usually handled by storage device providers or physical storage units leased by third parties. Improving network performance considering power connectivity and resource stability while ensuring workload balance is a hot topic in cloud computing. In this research, we have addressed the data duplication problem by providing two dynamic models with two variant architectures to investigate the strengths and shortcomings of architectures in Big Data Cloud Computing Networks. The problems of the data duplication process will be discussed accurately in each model. Attempts have been made to improve the performance of the cloud network by taking into account and correcting the flaws of the previously proposed algorithms. The accuracy of the proposed models have been investigated by simulation. Achieved results indicate an increase in the workload balance of the network and a decrease in response time to user requests in the model with a grouped architecture for all the architectures. Also, the proposed duplicate data model with peer-to-peer network architecture has been able to increase the cloud network optimality compared to the models presented with the same architecture.