A profiling based task scheduling approach for multicore network processors

 Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet-based delay-sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling-based task scheduling approach, targeting on improving the throughput of multicore network processor (Intel IXP) systems in the balanced pipeline way. In this work, we investigate a profiling-based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright © 2012 John Wiley & Sons, Ltd.

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.

Fundamentals and applications of Maldi-Tof MS

MALDI was developed for the ionisation of relatively large polypeptides and proteins but its application has widened to incorporate glycoproteins, oligonucleotides and complex carbohydrates. A great advantage of MALDI TOF MS is that the process of soft-ionisation causes little or no fragmentation of analytes, allowing the molecular ions of analytes to be identified, even within mixtures. MALDI TOF MS analysis is sensitive and very rapid as once the sample has been mixed with a 'matrix' on a MALDI target, a spectrum can be generated within seconds. Hence, the majority of protein and oligonucleotide analysis in the context of high throughput proteomics and genomics is carried out by MALDI TOF mass spectrometry.

Progressive clustering based method for protein function prediction

In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.

Metabolite profiling of biological extracts

Metabolite profiling, HPLC, LC-QTOF-MS, GC-MS. A workflow will be presented for comprehensive metabolomics using LC- and GC-MS. Metabolomics is an emerging field in the suite of ‘omic’ approaches for Systems Biology. The goal of metabolomics is to detect the presence of all small-molecules in a biological sample. This presents a significant challenge due to the chemical diversity and large concentration range of metabolites. Currently, there is no single method which enables the entire metabolome to be analysed, therefore a suite of analytical approaches are required to increase the coverage of detected metabolites. The routinely used techniques for metabolite profiling are LC- and GC-MS and NMR. Here we present complementary approaches using MS hyphenated to different chromatographic techniques. GC-MS represent the most robust standardised technique for high throughput metabolite profiling however there are still no standard LC-based methods for profiling. Polar compounds represent the most challenging aspect of LC-based metabolomics. A robust chromatographic technique for profiling polar compounds using HILIC chromatography and QTOF-MS will be presented as well as the complimentary reverse phase LC-MS method. The polar separation was carried out using a diamond hydride column. This unique stationary phase provides stable retention times and fast re-equilibration which contrasts to other forms of HILIC stationary phases. These LC-based methods will be compared to the well established GC-MS method as well as NMRbased profiling.

A surface-stress-based microcantilever aptasensor

Biosensors based on microcantilevers convert biological recognition events into measurable mechanical displacements. They offer advantages such as small size, low sample volume, label-free detection, ease of integration, high-throughput analysis, and low development cost. The design and development of a microcantilever-based aptasensor employing SU-8 polymer as the fabrication material is presented in this paper. Aptamers are employed as bioreceptor elements because they exhibit superior specificity compared to antibodies due to their small size and physicochemical stability. To immobilise thrombin DNA aptamer on the bare SU-8 surface of the aptasensor, a combined plasma mode treatment method is implemented which modifies the surface of the aptasensor. Label-free detection of thrombin molecules using the fabricated aptasensor is successfully demonstrated. The measured deflection is one order of magnitude higher than that of a silicon nitride microcantilever biosensor. The developed aptasensor also demonstrates high specificity.

A surface-stress-based microcantilever aptasensor

Biosensors based on microcantilevers convert biological recognition events into measurable mechanical displacements. They offer advantages such as small size, low sample volume, label-free detection, ease of integration, high-throughput analysis, and low development cost. The design and development of a microcantilever-based aptasensor employing SU-8 polymer as the fabrication material is presented in this paper. Aptamers are employed as bioreceptor elements because they exhibit superior specificity compared to antibodies due to their small size and physicochemical stability. To immobilise thrombin DNA aptamer on the bare SU-8 surface of the aptasensor, a combined plasma mode treatment method is implemented which modifies the surface of the aptasensor. Label-free detection of thrombin molecules using the fabricated aptasensor is successfully demonstrated. The measured deflection is one order of magnitude higher than that of a silicon nitride microcantilever biosensor. The developed aptasensor also demonstrates high specificity.

[Al4(OH)2(OCH3)4(H2N-bdc)3]⋅x H2O: A 12-connected porous metal–organic framework with an unprecedented aluminum-containing brick

A new stable aluminum aminoterephthalate system contains octameric building blocks that are connected by organic linkers to form a 12-connected net (see picture). The structure adopts a cubic centered packing motive in which octameric units replace individual atoms, thus forming distorted octahedral (red sphere) and tetrahedral cages (green spheres) with effective accessible diameters of 1 and 0.45 nm, respectively

Differential temporal expression of milk miRNA during the lactation cycle of the marsupial tammar wallaby (Macropus eugenii)

Lactation is a key aspect of mammalian evolution for adaptation of various reproductive strategies along different mammalian lineages. Marsupials, such as tammar wallaby, adopted a short gestation and a relatively long lactation cycle, the newborn is immature at birth and significant development occurs postnatally during lactation. Continuous changes of tammar milk composition may contribute to development and immune protection of pouch young. Here, in order to address the putative contribution of newly identified secretory milk miRNA in these processes, high throughput sequencing of miRNAs collected from tammar milk at different time points of lactation was conducted. A comparative analysis was performed to find distribution of miRNA in milk and blood serum of lactating wallaby.

Non-referenced genome assembly from epigenomic short-read data

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.

Fifteen to twenty percent of HIV substitution mutations are associated with recombination

HIV undergoes high rates of mutation and recombination during reverse transcription, but it is not known whether these events occur independently or are linked mechanistically. Here we used a system of silent marker mutations in HIV and a single round of infection in primary T lymphocytes combined with a high-throughput sequencing and mathematical modeling approach to directly estimate the viral recombination and mutation rates. From >7 million nucleotides (nt) of sequences from HIV infection, we observed 4,801 recombination events and 859 substitution mutations (≈1.51 and 0.12 events per 1,000 nt, respectively). We used experimental controls to account for PCR-induced and transfection-induced recombination and sequencing error. We found that the single-cycle virus-induced mutation rate is 4.6 × 10(-5) mutations per nt after correction. By sorting of our data into recombined and nonrecombined sequences, we found a significantly higher mutation rate in recombined regions (P = 0.003 by Fisher's exact test). We used a permutation approach to eliminate a number of potential confounding factors and confirm that mutation occurs around the site of recombination and is not simply colocated in the genome. By comparing mutation rates in recombined and nonrecombined regions, we found that recombination-associated mutations account for 15 to 20% of all mutations occurring during reverse transcription.

Reliable multicast with pipelined network coding using opportunistic feeding and routing

Multicast is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including throughput, delay, energy efficiency, etc. Traditionally, an ideal loss-free channel model is widely used to facilitate routing protocol design. However, the quality of wireless links is affected or even jeopardized resulting in transmission failures by many factors like collisions, fading or the noise of environment. In this paper, we propose a reliable multicast protocol, called CodePipe, with energy-efficiency, high throughput and fairness in lossy wireless networks. Building upon opportunistic routing and random linear network coding, CodePipe can not only eliminate coordination between nodes, but also improve the multicast throughput significantly by exploiting both intra-batch and inter-batch coding opportunities. In particular, four key techniques, namely, LP-based opportunistic routing structure, opportunistic feeding, fast batch moving and inter-batch coding, are proposed to offer significant improvement in throughput, energy-efficiency and fairness.Moreover, we design an efficient online extension of CodePipe such that it can work in a dynamic network where nodes join and leave the network as time progresses. We evaluate CodePipe on ns2 simulator by comparing with other two state-of-art multicast protocols,MORE and Pacifier. Simulation results show that CodePipe significantly outperforms both of them.

Testing the boundaries of closely related daisy taxa using metabolomic profiling

Advances in high-throughput, comprehensive small molecule analytical techniques have seen the development of the field of metabolomics. The coupling of mass spectrometry with high-resolution chromatography provides extensive chemical profiles from complex biological extracts. These profiles include thousands of compounds linked to gene expression, and can be used as taxonomic characters. Studies have shown metabolite profiles to be taxon specific in a range of organisms, but few have investigated taxonomically problematic plant taxa. This study used a phenetic analysis of metabolite profiles to test taxonomic boundaries in the Olearia phlogopappa (Asteraceae) complex as delimited by morphological data. Metabolite profiles were generated from both field- and shade house-grown material, using liquid chromatography-mass spectrometry (LC-MS). Aligned profiles of 51 samples from 12 taxa gave a final dataset of over 10,000 features. Multivariate analyses of field and shade house material gave congruent results, both confirming the distinctiveness of the morphologically defined species and subspecies in this complex. Metabolomics has great potential in alpha taxonomy, especially for testing the boundaries of closely related taxa where DNA sequence data has been uninformative.

Paediatric leukaemia DNA methylation profiling using MBD enrichment and SOLiD sequencing on archival bone marrow smears

BACKGROUND: Acute Lymphoblastic Leukaemia (ALL) is the most common cancer in children. Over the past four decades, research has advanced the treatment of this cancer from a less than 60% chance of survival to over 85% today. The causal molecular mechanisms remain unclear. Here, we performed sequencing-based genomic DNA methylation profiling of eight paediatric ALL patients using archived bone marrow smear microscope slides. FINDINGS: SOLiD™ sequencing data was collected from Methyl-Binding Domain (MBD) enriched fractions of genomic DNA. The primary tumour and remission bone marrow sample was analysed from eight patients. Four patients relapsed and the relapsed tumour was analysed. Input and MBD-enriched DNA from each sample was sequenced, aligned to the hg19 reference genome and analysed for enrichment peaks using MACS (Model-based Analysis for ChIP-Seq) and HOMER (Hypergeometric Optimization of Motif EnRichment). In total, 3.67 gigabases (Gb) were sequenced, 2.74 Gb were aligned to the reference genome (average 74.66% alignment efficiency). This dataset enables the interrogation of differential DNA methylation associated with paediatric ALL. Preliminary results reveal concordant regions of enrichment indicative of a DNA methylation signature. CONCLUSION: Our dataset represents one of the first SOLiD™MBD-Seq studies performed on paediatric ALL and is the first to utilise archival bone marrow smears. Differential DNA methylation between cancer and equivalent disease-free tissue can be identified and correlated with existing and published genomic studies. Given the rarity of paediatric haematopoietic malignancies, relative to adult counterparts, our demonstration of the utility of archived bone marrow smear samples to high-throughput methylation sequencing approaches offers tremendous potential to explore the role of DNA methylation in the aetiology of cancer.