A task scheduling algorithm for arbitrarily-connected processors with awareness of link contention

In this paper, we present a new task scheduling algorithm, called Contention-Aware Scheduling (CAS) algorithm, with the objective of delivering good quality of schedules in low running-time by considering contention on links of arbitrarily-connected, heterogeneous processors. The CAS algorithm schedules tasks on processors and messages on links by considering the earliest finish time attribute with the virtual cut-through (VCT) or the store-and-forward (SAF) switching. There are three types of CAS algorithm presented in this paper, which differ in ordering the messages from immediate predecessor tasks. As part of the experimental study, the performance of the CAS algorithm is compared with two well-known APN (arbitrary processor network) scheduling algorithms. Experiments on the results of the synthetic benchmarks and the task graphs of the well-known problems clearly show that our CAS algorithm outperforms the related work with respect to performance (given in normalized schedule length) and cost (given in running time) to generate output schedules. Ali Fuat Alkaya received the B.Sc. degree in mathematics from Koc University, Istanbul, Turkey in 1998, and the M.Sc. degree in computer engineering from Marmara University, Istanbul, Turkey in 2002. He is currently a Ph.D. student in engineering management department at the same university. His research interests include task scheduling and analysis of algorithms. Haluk Rahmi Topcuoglu received the B.Sc. and M.Sc. degrees in computer engineering from Bogazici University, Istanbul, Turkey, in 1991 and 1993, respectively. He received the Ph.D. degree in computer science from Syracuse University in 1999. He has been on the faculty at Marmara University, Istanbul, Turkey since Fall 1999, where he is currently an Associate Professor in computer engineering department. His main research interests are task scheduling and mapping in parallel and distributed systems; parallel processing; evolutionary algorithms and their applicability for stationary and dynamic environments. He is a member of the ACM, the IEEE, and the IEEE Computer Society. e-mail: haluk@eng.marmara.edu.tr e-mail: falkaya@eng.marmara.edu.tr     

土壤动物的分子分类预测策略评估

土壤动物类群包含庞大的生物多样性, 由于传统的形态学鉴定技术很难满足该类群多样性调查和监测的巨大需求, 基于DNA等遗传物质的分子层面的鉴定技术(分子分类预测)逐渐登上舞台。然而, 分子分类预测能否在参考分子序列严重匮乏的土壤动物分类研究中实现有效鉴定、如何利用分子分类预测更为准确高效地获取土壤动物的分类信息, 是当下分子分类预测在土壤动物应用中的两大难题。为探究这两大难题, 本文基于宏条形码技术, 对5款常用的分子分类预测软件(VSEARCH、HS-BLASTN、EPA-NG、RAPPAS和APPLES; 前两款基于相似度算法, 其余基于系统发育位置算法)进行了准确性(科和属阶元)、运行速度和内存占用等性能的比较和评估。其中, 预测准确性的评估基于4类土壤动物(弹尾纲, 蜱螨亚纲, 环带纲和色矛纲)和3种分子标记(COI、16S和18S)展开。结果表明: EPA-NG在大部分场合下准确性最高, 尤其是在使用COI标记时, 准确性远高于其他工具。VSEARCH和HS-BLASTN准确性也较高, 基于16S和18S标记时, 它们的准确性和EPA-NG相当。此外, VSEARCH在所有软件中运行速度最快且内存占用最小, 这使得它在16S和18S的应用中比EPA-NG更具竞争力。RAPPAS和APPLES具有较低的假阳性, 但假阴性很高, 相对保守的算法使得它们无法将一些物种鉴定到低阶元。总体来说, 即使是在参考数据库缺少目标物种且小部分物种在分类上存在界定争议的前提下, 5款分子分类预测软件都能极为准确地将土壤动物预测至科级阶元, 因此分子分类预测在土壤动物应用中前景远大。COI标记在土壤动物科、属和种阶元上的覆盖度最广且能有效实现分子鉴定, 在目前最适合作为土壤动物尤其是土壤节肢动物的分子标记。在应用COI标记且参考数据库规模不大时, EPA-NG是分子分类预测的最佳选择; 而在应用16S、18S标记或参考数据库规模较大时, 更推荐使用VSEARCH。     

A critical review of selected tools for assessing community resilience

The concept of resilience is increasingly used in academic and policy circles. To operationalize this concept and reduce the ambiguities surrounding it, since the turn of the century, various resilience assessment methodologies have been introduced. This paper provides a critical review of 36 selected community resilience assessment tools. These tools have been developed by a variety of entities, including national and local organizations, international donor organizations, and academic researchers. First, an overview of the selected tools is presented. This overview analysis shows that while some commonalities exist, there are also considerable differences between the tools. Next, based on literature review, an analytical framework is developed that identifies six criteria for evaluating performance of resilience assessment tools. These are, namely, addressing multiple dimensions of resilience, accounting for cross-scale relationships, capturing temporal dynamism, addressing uncertainties, employing participatory approaches, and developing action plans. Results show that limited success has been achieved in addressing these criteria. In terms of comprehensiveness, the environmental dimension has received relatively less attention in spite of its significance for building community resilience. Further improvements are needed to account for dynamics over time and across space. More attention to employing iterative processes that involve scenario-based planning is needed to better address challenges associated with uncertainties. Results also show that more attention needs to be paid to stakeholder participation in developing assessment tools. The paper concludes by highlighting several other areas of weakness that need to be addressed and discussing major challenges that still remain.     

A novel matrix for high performance affinity chromatography and its application in the purification of antithrombin III

Viscose fiber, a regenerated cellulose, was evaluated for using as a novel matrix for high performance affinity chromatography. With a one-step activation with epichlorohydrin, heparin can be readily covalently attached to the matrix. This heparin-viscose fiber material was used for purifying antithrombin III (AT III) from human plasma. The purity of the AT III from this one-step purification is 93% as measured by SDS-PAGE and the protein recovery yield is about 90%. This column is highly specific as described by the dissociation constant of the complex of immobilized heparin and AT III, which was 2.83 x 10(-5)mol/L. And more important, this viscose fiber material demonstrated its excellent mechanical property that allows the flow rate to reach up to 900 cm/h or more.     

A new high performance liquid chromatographic method for quantification of atomoxetine in human plasma and its application for pharmacokinetic study

Atomoxetine is the first, non-stimulant alternative to other stimulant medications used for the treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). Reported methods for the determination of atomoxetine include expensive liquid chromatography tandem mass spectrometry (LCMS) and high performance liquid chromatography (HPLC) with liquid scintillation counting (LSC) detection. Till date, no method has been reported in literature to determine atomoxetine using HPLC with UV detection. In this paper, we describe a new HPLC method for the determination of atomoxetine using liquid-liquid extraction with tertiary butyl methyl ether and UV detector. This method was found to be linear over the concentration range of 0.05-3.0 microg/ml. The limit of quantification was 0.05 microg/ml. Intra- and inter-day precision was <15% and accuracy was in the range of 95.67-108.80%. Stability studies showed that atomoxetine was stable in human plasma for short- and long-term period for sample preparation and analysis. This method was used for sample analysis in a pharmacokinetic study of atomoxetine (25mg) in five healthy adult female volunteers. The observed mean+/-S.D. pharmacokinetic parameters Cmax, Tmax and AUC(0-t) were 0.40+/-0.06 microg/ml, 3.40+/-0.42 h and 1.34+/-0.52 microg h/ml, respectively.     

A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects

Background

Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way.

Results

We demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively.

Conclusions

These results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-314) contains supplementary material, which is available to authorized users.     

A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

Background

Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.

Results

We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% ‘precision’ (accuracy of prediction), 97% ‘recall’ (sensitivity)] in predicting ‘true-positive’ targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low ‘recall’ values. Score optimizations increased the ‘recall’ to only 70% (corresponding ‘precision’: 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly ‘precise’ predictions. The large number of ‘false negative’ predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.

Conclusion

Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-348) contains supplementary material, which is available to authorized users.     

A rapid, novel high performance liquid chromatography method for the purification of glutathione S-transferase: an application to the human placental enzyme

A simple High Performance Liquid Chromatography procedure is detailed for the purification of Glutathione S-transferase. The human placental transferase was used to assess its potential. Unlike conventional methods of purification, the procedure is rapid and resolution of the various forms is achieved in less than 20 min. Since recovery is essentially complete, it is possible to isolate different minor forms. Three forms, one major and two minor, were separated. The major form represented about 97% of the total recovered activity and exhibited a specific activity of 254.94 mumoles/min/mg protein with a purification of 1342-fold. Electrophoresis of the major form revealed the presence of a single band, suggesting homogeneity.     

A high performance liquid chromatography assay for the rapid analysis of the subunit content of concanavalin A

A high performance liquid chromatography system is described which provides a rapid and convenient assay for the relative amounts of intact (26 000 dalton) and fragmented (14 000 and 12 000 dalton) subunits present in preparations of concanavalin A. Analyses were performed on an HPLC size exlusions column using either 8 M urea or 6 M guanidine hydrochloride as denaturing eluents. The efficiency and resolving power of this technique were confirmed by sodium dodecyl sulfate polyacrylamide gel electrophoresis. This HPLC assay facilitated the monitoring of the purification of concanavalin A to prepare a homogeneous preparation necessary for its biological evaluation.     

Assignment of polar states for protein amino acid residues using an interaction cluster decomposition algorithm and its application to high resolution protein structure modeling

We have developed a new method (Independent Cluster Decomposition Algorithm, ICDA) for creating all-atom models of proteins given the heavy-atom coordinates, provided by X-ray crystallography, and the pH. In our method the ionization states of titratable residues, the crystallographic mis-assignment of amide orientations in Asn/Gln, and the orientations of OH/SH groups are addressed under the unified framework of polar states assignment. To address the large number of combinatorial possibilities for the polar hydrogen states of the protein, we have devised a novel algorithm to decompose the system into independent interacting clusters, based on the observation of the crucial interdependence between the short range hydrogen bonding network and polar residue states, thus significantly reducing the computational complexity of the problem and making our algorithm tractable using relatively modest computational resources. We utilize an all atom protein force field (OPLS) and a Generalized Born continuum solvation model, in contrast to the various empirical force fields adopted in most previous studies. We have compared our prediction results with a few well-documented methods in the literature (WHATIF, REDUCE). In addition, as a preliminary attempt to couple our polar state assignment method with real structure predictions, we further validate our method using single side chain prediction, which has been demonstrated to be an effective way of validating structure prediction methods without incurring sampling problems. Comparisons of single side chain prediction results after the application of our polar state prediction method with previous results with default polar state assignments indicate a significant improvement in the single side chain predictions for polar residues.     

A fluorescence-based high performance liquid chromatographic method for the characterization of palmitoyl acyl transferase activity

Although protein palmitoylation is essential for targeting many important signaling proteins to the plasma membrane, the mechanism by which palmitoylation occurs is uncharacterized, since the enzyme(s) responsible for this modification remain unidentified. To study palmitoyl acyl transferase (PAT) activity, we developed an in vitro palmitoylation (IVP) assay using a fluorescently labeled substrate peptide, mimicking the N-terminal palmitoylation motif of proteins such as non-receptor Src-related tyrosine kinases. The palmitoylated and non-palmitoylated forms of the peptide were resolved by reverse-phase HPLC and detected by fluorescence. The method was optimized for PAT activity using lysates from the MCF-7 and Hep-G2 human tumor cell lines. The PAT activity was inhibited by boiling, reducing the incubation temperature, or adding 10 microM 2-bromopalmitate, a known palmitoylation inhibitor. This IVP assay provides the first method that is suitable to study all facets of the palmitoylation reaction, including peptide palmitoylation by PAT(s), depalmitoylation by thioesterases, and evaluation of potential palmitoylation inhibitors.     

A straightforward protocol for the preparation of high performance microarray displaying synthetic MUC1 glycopeptides

Background

Human serum MUC1 peptide fragments bearing aberrant O-glycans are secreted from columnar epithelial cell surfaces and known as clinically important serum biomarkers for the epithelial carcinoma when a specific monoclonal antibody can probe disease-relevant epitopes. Despite the growing importance of MUC1 glycopeptides as biomarkers, the precise epitopes of most anti-MUC1 monoclonal antibodies remains unclear.

Methods

A novel protocol for the fabrication of versatile microarray displaying peptide/glycopeptide library was investigated for the construction of highly sensitive and accurate epitope mapping assay of various anti-MUC1 antibodies.

Results

Selective imine-coupling between aminooxy-functionalized methacrylic copolymer with phosphorylcholine unit and synthetic MUC1 glycopeptides-capped by a ketone linker at N-terminus provided a facile and seamless protocol for the preparation of glycopeptides microarray platform. It was demonstrated that anti-KL-6 monoclonal antibody shows an extremely specific and strong binding affinity toward MUC1 fragments carrying sialyl T antigen (Neu5Acα2,3Galβ1,3GalNAcα1→) at Pro-Asp-Thr-Arg motif when compared with other seven anti-MUC1 monoclonal antibodies such as VU-3D1, VU-12E1, VU-11E2, Ma552, VU-3C6, SM3, and DF3. The present microarray also uncovered the occurrence of IgG autoantibodies in healthy human sera that bind specifically with sialyl T antigen attached at five potential O-glycosylation sites of MUC1 tandem repeats.

Conclusion

We established a straightforward strategy toward the standardized microarray platform allowing highly sensitive and accurate epitope mapping analysis by reducing the background noise due to nonspecific protein adsorption.

General significance

The present approach would greatly accelerate the discovery research of new class autoantibodies as well as the development of therapeutic mAbs reacting specifically with disease-relevant epitopes.     

A set of procedures for resolving purine compounds by reversed-phase high performance liquid chromatography: application to the study of purine nucleotide and nucleic acid metabolism

A set of simple procedures for the separation of major purine 5'-ribonucleotides including diguanosine polyphosphates, purine and pyrimidine bases, and 2'- and 3'-nucleotide monophosphates using reversed-phase high-performance liquid chromatography and isocratic elution study of purine nucleotide and nucleic acid biosynthesis in Artemia is presented.     

A slow gradient approach for the purification of synthetic polypeptides by reversed phase high performance liquid chromatography

Unquestionably, the purification of polypeptides by chromatographic methods is a considerable bottleneck in their preparation. Peptides synthesised by solid phase synthesis typically contain chromatographically similar impurities that complicate purification by reversed phase high performance liquid chromatography (HPLC) techniques. We report on the application of a slow gradient HPLC protocol that allows, in a single chromatographic step, the purification of hundreds of milligrammes of material. This technique was applied to an extensive collection of synthetic polypeptides some incorporating non‐proteinogenic functionality. In all cases examined, the peptides were not only obtained in high purity peptides but were also recovered in multi‐milligramme amounts. Copyright © 2012 European Peptide Society and John Wiley & Sons, Ltd.     

A high density GBS map of bread wheat and its application for dissecting complex disease resistance traits

Background

Genotyping-by-sequencing (GBS) is a high-throughput genotyping approach that is starting to be used in several crop species, including bread wheat. Anchoring GBS tags on chromosomes is an important step towards utilizing them for wheat genetic improvement. Here we use genetic linkage mapping to construct a consensus map containing 28644 GBS markers.

Results

Three RIL populations, PBW343 × Kingbird, PBW343 × Kenya Swara and PBW343 × Muu, which share a common parent, were used to minimize the impact of potential structural genomic variation on consensus-map quality. The consensus map comprised 3757 unique positions, and the average marker distance was 0.88 cM, obtained by calculating the average distance between two adjacent unique positions. Significant variation of segregation distortion was observed across the three populations. The consensus map was validated by comparing positions of known rust resistance genes, and comparing them to wheat reference genome sequences recently published by the International Wheat Genome Sequencing Consortium, Rye and Ae. tauschii genomes. Three well-characterized rust resistance genes (Sr58/Lr46/Yr29, Sr2/Yr30/Lr27, and Sr57/Lr34/Yr18) and 15 published QTLs for wheat rusts were validated with high resolution. Fifty-two per cent of GBS tags on the consensus map were successfully aligned through BLAST to the right chromosomes on the wheat reference genome sequence.

Conclusion

The consensus map should provide a useful basis for analyzing genome-wide variation of complex traits. The identified genes can then be explored as genetic markers to be used in genomic applications in wheat breeding.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1424-5) contains supplementary material, which is available to authorized users.     

A methodology for centrifuge selection for the separation of high solids density cell broths by visualisation of performance using windows of operation

Expression systems capable of growing to high cell densities are now readily available and are popular due to the benefits of increased product concentration. However, such high solids density cultures pose a major challenge for bioprocess engineers as choosing the right separation equipment and operating it at optimal conditions is crucial for efficient recovery. This study proposes a methodology for the rapid determination of suitable operating conditions for the centrifugal recovery of high cell density fermentation broths. An ultra scale-down (USD) approach for the prediction of clarification and dewatering levels achieved in a range of typical high-speed centrifuges is presented. Together with a visualisation tool, a Window of Operation, this provides for the rapid analysis of separation performance and evaluation of the available operating conditions, as an aid in the selection of the centrifuge equipment most appropriate for a given process duty. A case study examining centrifuge selection for the processing of a high cell density Pichia pastoris culture demonstrates the method. The study examines semi-continuous disc-stack centrifuges and batch-operated machines such as multi-chamber bowls and Carr Powerfuges. Performance is assessed based on the variables of clarification, dewatering and product yield. Inclusion of limits imposed by the centrifuge type and design, and operation itself, serve to constrain the process and to define the Windows of Operation. The insight gained from the case study provides a useful indication of the utility of the methodology presented and illustrates the challenges of centrifuge selection for the demanding case of high solids concentration feed streams.