SUMMARYThe Infrastructure-as-a-Service (IaaS) cloud is attracting applications due to the scalability, dynamic resource provision, and payas-you-go cost model. Scheduling scientific workflow in the IaaS cloud is faced with uncertainties like resource performance variations and unknown failures. A schedule is said to be robust if it is able to absorb some degree of the uncertainties during the workflow execution. In this paper, we propose a novel workflow scheduling algorithm called Dynamic Earliest-Finish-Time (DEFT) in the IaaS cloud improving both makespan and robustness. DEFT is a dynamic scheduling containing a set of list scheduling loops invoked when some tasks complete successfully and release resources. In each loop, unscheduled tasks are ranked, a best virtual machine (VM) with minimum estimated earliest finish time for each task is selected. A task is scheduled only when all its parents complete, and the selected best VM is ready. Intermediate data is sent from the finished task to each of its child and the selected best VM before the child is scheduled. Experiments show that DEFT can produce shorter makespans with larger robustness than existing typical list and dynamic scheduling algorithms in the IaaS cloud.

Autosomal recessive (AR) non-syndromic hearing loss (NSHL) is the most common form of hereditary deafness. Mutations in the gap junction protein beta 2 (GJB2) gene encoding connexin 26 (Cx26) account for about 50% of cases of ARNSHL. In the current study, a combination of exome sequencing and Sanger sequencing in a Chinese Dong family with ARNSHL allowed identification of a novel compound heterozygous mutation c.240G>C(p. Q80H)/C.109G>A(p.V37I) in exon 2 of the GJB2 gene, which co-segregated with the disease phenotype in this family and was not evident in 100 healthy controls. Bioinformatic analysis revealed that the two mutations in the GJB2 gene were probably pathogenic. Results indicated that the compound heterozygous variants, p.Q80H and p.V37I, in the GJB2 gene are associated with ARNSHL. The Q80H variant was initially identified in patients of Dong Chinese origin with NSHL. The current results broaden the spectrum of GJB2 mutations responsible for NSHL and have important implications for molecular diagnosis, treatment, and genetic counseling for this family.

The R345W mutation in EFEMP1 caused Malattia leventinese/Doyne honeycomb retinal dystrophy in a Chinese family. This is the first report, as per our knowledge, of the R345W mutation in EFEMP1 in a Chinese pedigree of this disease.

Neurofibromatosis type 1 (NF1) is a progressive neurocutaneous disorder in humans, mainly characterized by café-au-lait macules (CALMs) and neurofibromas. NF1 is caused by variants of the neurofibromin 1 gene (NF1), which encodes a Ras-GTPase-activating protein called neurofibromin. NF1 variants may result in loss of neurofibromin function and elevation of cell proliferation and tumor formation. In this study, a Chinese NF1 family with an autosomal dominant inheritance pattern was recruited. Exome sequencing and Sanger sequencing were performed to discover the causative variant responsible for the family, followed by molecular analysis of effect of the mutated NF1 protein on Ras activity. A novel frameshift variant c.541dupC (p.(Gln181Profs∗20)) in the NF1 gene was identified in all three affected family members. The variant cosegregated with the disease phenotypes in the pedigree and was absent in 100 healthy controls. Bioinformatic analysis showed that the variant c.541dupC (p.(Gln181Profs∗20)) was pathogenic. The further molecular analysis verified the cells expressing NF1 variant p.(Gln181Profs∗20) partially enhanced Ras activity and elevated cell proliferation and tumor formation due to loss of neurofibromin function caused by the variant. Taken together, the data strongly advocate the c.541dupC (p.(Gln181Profs∗20)) variant as the underlying genetic cause of the Chinese family with NF1. Moreover, our findings broaden the spectrum of NF1 variants and provide molecular insights into the pathogenesis of NF1.

Background: With wide application of virtualization technology, the demand is increasing for performance analysis and system diagnosis in virtualization environment. There are some profiling toolkits based on hardware events, such as OProfile in native Linux and Xenoprof in Xen virtual machine environment. However, sometimes users in different domains need monitor different hardware events individually at the same time. For programming and profiling in environment for virtual machine, it may become popular in the coming future. In this paper, we present Metis, a system-wide profiling toolkit for Xen virtual machine environment based on the virtualization of hardware performance counters.