APGW/AKH Precursor from Rotifer Brachionus plicatilis and the DNA Loss Model Explain Evolutionary Trends of the Neuropeptide LWamide, APGWamide, RPCH, AKH, ACP, CRZ, and GnRH Families.

In the year 2002, DNA loss model (DNA-LM) postulated that neuropeptide genes to emerged through codons loss via the repair of damaged DNA from ancestral gene namely Neuropeptide Precursor Predictive (NPP), which organization correspond two or more neuropeptides precursors evolutive related. The DNA-LM was elaborated according to amino acids homology among LWamide, APGWamide, red pigment-concentrating hormone (RPCH), adipokinetic hormones (AKHs) and in silico APGW/RPCH NPPAPGW/AKH NPP were proposed. With the above principle, it was proposed the evolution of corazonin (CRZ), gonadotropin-releasing hormone (GnRH), AKH, and AKH/CRZ (ACP), but any NPP never was considered. However, the evolutive relation via DNA-LM among these neuropeptides precursors not has been established yet. Therefore, the transcriptomes from crabs Callinectes toxotes and Callinectes arcuatus were used to characterized ACP and partial CRZ precursors, respectively. BLAST alignment with APGW/RPCH NPP and APGW/AKH NPP allow identified similar NPP in the rotifer Brachionus plicatilis and other invertebrates. Moreover, three bioinformatics algorithms and manual verification were used to purify 13,778 sequences, generating a database with 719 neuropeptide precursors. Phylogenetic trees with the DNA-LM parameters showed that some ACP, CRZ, AKH2 and two NPP share nodes with GnRH from vertebrates and some of this neuropeptide had nodes in invertebrates. Whereas the phylogenetic tree with standard parameters do not showed previous node pattern. Robinson-Foulds metric corroborates the differences among phylogenetic trees. Homology relationship showed four putative orthogroups; AKH4, CRZ, and protostomes GnRH had individual group. This is the first demonstration of NPP in species and would explain the evolution neuropeptide families by the DNA-LM.

Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds.

Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73-43.44%, 44.06-92.11%, and 16.38-24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures.