Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts

As a fruit of the current revolution in sequencing technology, transcriptomes can now be analyzed at an unprecedented level of detail. These advances have been exploited for detecting differential expressed genes across biological samples and for quantifying the abundances of various RNA transcripts within one gene. However, explicit strategies for detecting the hidden differential abundances of RNA transcripts in biological samples have not been defined. In this work, we present two novel statistical tests to address this issue: a 'gene structure sensitive' Poisson test for detecting differential expression when the transcript structure of the gene is known, and a kernel-based test called Maximum Mean Discrepancy when it is unknown. We analyzed the proposed approaches on simulated read data for two artificial samples as well as on factual reads generated by the Illumina Genome Analyzer for two _C. elegans_ samples. Our analysis shows that the Poisson test identifies genes with differential transcript expression considerably better that previously proposed RNA transcript quantification approaches for this task. The MMD test is able to detect a large fraction (75%) of such differential cases without the knowledge of the annotated transcripts. It is therefore well-suited to analyze RNA-Seq experiments when the genome annotations are incomplete or not available, where other approaches have to fail

Dependability modeling framework : a test procedure for high availability in cloud operating systems

This paper describes a framework on how to test High Availability capabilities of cloud architectures, particularly OpenStack. The “Dependability Modeling Framework” which consists of a modelling of the system parts, user interactions and dependencies between them will form the basis for this test. The test procedure consists of simulating random shutdown of system components, polling the availability of user interactions and measuring the impact of outages and expected downtime. Outage impacts and downtime are used to rate the underlying system architecture. The test procedure is applied on a single node OpenStack installation in order to show validity of the test concept

Impact of foot progression angle modification on plantar loading in individuals with diabetes mellitus and peripheral neuropathy

AIMS: To determine if participants can reduce foot progression angle (FPA), and if FPA reduction decreases regional plantar stresses and forces in individuals with diabetes. METHODS: DESIGN: Three-group cross-sectional design with repeated measures. SUBJECTS: twenty-eight participants either with diabetes mellitus (DM), diabetes and peripheral neuropathy with (DMPN+NPU) or without a prior history of ulceration (DMPN−NPU) were studied. INTERVENTION: Participants were first instructed to walk over a 3.6 m walkway at their preferred FPA, and then to walk with their foot aligned parallel with the line of gait progression at their self-selected speed. Dynamic plantar kinetics in six masked regions were collected using an EMED-st-P-2 pedobarograph. MAIN MEASURES: Primary outcome measures were FPA, peak plantar pressure (PPP), and force-time integral (FTI). A repeated measures ANOVA was conducted to determine group differences in FPA for both walking conditions. Regional differences in PPPs and FTIs between preferred and corrected walking conditions were analyzed using repeated measures ANCOVA. RESULTS: Participants showed a reduction in FPA magnitude on the ‘Involved’ foot between the preferred and corrected walking conditions (p<0.01). There were no differences in PPPs or FTIs in any mask between walking conditions (p>0.05). CONCLUSION: Results from this investigation offer important evidence that people with diabetes can modify their FPA with a simple intervention of visual and verbal cueing. Future research should examine if gait retraining strategies in regular footwear more effectively offload areas of elevated regional plantar stresses and forces in adults with diabetes mellitus and peripheral neuropathy

Attitude Determination from Single-Antenna Carrier-Phase Measurements

A model of carrier phase measurement (as carried out by a satellite navigation receiver) is formulated based on electromagnetic theory. The model shows that the phase of the open-circuit voltage induced in the receiver antenna with respect to a local oscillator (in the receiver) depends on the relative orientation of the receiving and transmitting antennas. The model shows that using a {\it single} receiving antenna, and making carrier phase measurements to seven satellites, the 3-axis attitude of a user platform (in addition to its position and time) can be computed relative to an initial point. This measurement model can also be used to create high-fidelity satellite signal simulators that take into account the effect of platform rotation as well as translation.Comment: 12 pages, and one figure. Published in J. Appl. Phys. vol. 91, No. 7, April 1, 200

Towards a Swiss National Research Infrastructure

In this position paper we describe the current status and plans for a Swiss National Research Infrastructure. Swiss academic and research institutions are very autonomous. While being loosely coupled, they do not rely on any centralized management entities. Therefore, a coordinated national research infrastructure can only be established by federating the various resources available locally at the individual institutions. The Swiss Multi-Science Computing Grid and the Swiss Academic Compute Cloud projects serve already a large number of diverse user communities. These projects also allow us to test the operational setup of such a heterogeneous federated infrastructure

Monitoring resilience in a rook-managed containerized cloud storage system

Distributed cloud storage solutions are currently gaining high momentum in industry and academia. The enterprise data volume growth and the recent tendency to move as much as possible data to the cloud is strongly stimulating the storage market growth. In this context, and as a main requirement for cloud native applications, it is of utmost importance to guarantee resilience of the deployed applications and the infrastructure. Indeed, with failures frequently occurring, a storage system should quickly recover to guarantee service availability. In this paper, we focus on containerized cloud storage, proposing a resilience monitoring solution for the recently developed Rook storage operator. While, Rook brings storage systems into a cloud-native container platform, in this paper we design an additional module to monitor and evaluate the resilience of the Rook-based system. Our proposed module is validated in a production environment, with software components generating a constant load and a controlled removal of system elements to evaluate the self-healing capability of the storage system. Failure recovery time revealed to be 41 and 142 seconds on average for a 32GB and a 215GB object storage device respectively

Optimum Detection Location-Based Cooperative Spectrum Sensing in Cognitive Radio

Cognitive radio arises as a hot research issue in wireless communications recently, attributed to its capability of enhancing spectral efficiency and catering for the growing demand for bandwidth. As a good embodiment of cognitive radio’s unique feature, i.e. making use of every bit spectral resource, spectrum sensing plays a vital role in the implementation of cognitive radio. To alleviate negative effect on cooperative spectrum sensing brought by bit errors, we introduce a novel concept, i.e. Optimum Detection Location (ODL) and present two algorithms of different computational complexity for locating ODL, together with an ODL-Based cooperative spectrum sensing scheme, with the motivation to exploit the gain derived from geographic advantages and multiuser diversity. Numerical and simulation results both demonstrate that our proposed spectrum sensing scheme can significantly improve the sensing performance in the case of reporting channel with bit errors

Self-managing cloud-native applications : design, implementation and experience

Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed: (1) to adjust their resources to the incoming load and (2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention. In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job. In this paper we discuss the main characteristics of cloud native applications, propose a novel architecture that enables scalable and resilient self-managing applications in the cloud, and relate on our experience in porting a legacy application to the cloud applying cloud-native principles

Hera Object Storage : a seamless, automated multi-tiering solution on top of OpenStack Swift

Over the last couple of decades, the demand for storage in the Cloud has grown exponentially. Distributed Cloud storage and object storage for the increasing share of unstructured data, are in high focus in both academic and industrial research activities. At the same time, efficient storage and the corresponding costs are often contrasting parameters raising a trade-off problem for any proposed solution. To this aim, classifying the data in terms of access probability became a hot topic. This paper introduces Hera Object Storage, a storage system built on top of OpenStack Swift that aims at selecting the most appropriate storage tier for any object to be stored. The goal of the multi-tiering storage we propose is to be automated and seamless, guaranteeing the required storage performance at the lowest possible cost. The paper discusses the design challenges, the proposed algorithmic solutions to the scope and, based on a prototype implementation it presents a basic proof-of-concept validation