Smoothed Analysis of the Minimum-Mean Cycle Canceling Algorithm and the Network Simplex Algorithm

The minimum-cost flow (MCF) problem is a fundamental optimization problem with many applications and seems to be well understood. Over the last half century many algorithms have been developed to solve the MCF problem and these algorithms have varying worst-case bounds on their running time. However, these worst-case bounds are not always a good indication of the algorithms' performance in practice. The Network Simplex (NS) algorithm needs an exponential number of iterations for some instances, but it is considered the best algorithm in practice and performs best in experimental studies. On the other hand, the Minimum-Mean Cycle Canceling (MMCC) algorithm is strongly polynomial, but performs badly in experimental studies. To explain these differences in performance in practice we apply the framework of smoothed analysis. We show an upper bound of $O(mn^2\log(n)\log(\phi))$ for the number of iterations of the MMCC algorithm. Here $n$ is the number of nodes, $m$ is the number of edges, and $\phi$ is a parameter limiting the degree to which the edge costs are perturbed. We also show a lower bound of $\Omega(m\log(\phi))$ for the number of iterations of the MMCC algorithm, which can be strengthened to $\Omega(mn)$ when $\phi=\Theta(n^2)$. For the number of iterations of the NS algorithm we show a smoothed lower bound of $\Omega(m \cdot \min \{ n, \phi \} \cdot \phi)$.Comment: Extended abstract to appear in the proceedings of COCOON 201

Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph-Based Benchmark Suite (GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201

BIOSORPTION EVALUATION OF SYNTHETICDYE FROM FOOD INDUSTRY WASTEWATER ONTO SILKWORMCOCOONS: PRELIMINARY STUDIES

The aim of this study was to evaluate thesynthetic azo dye (tartrazine) biosorption onto second line silkworm cocoons. Batch adsorption system was used to investigate the effect of pH and initial tartrazine concentration in theliquid phase. Also, the kinetic mechanismwas investigated at 20°C and pH 2.0.The highest adsorption was obtained at the lowest pH evaluated, while the assays with pH above 3.0 did not show significant adsorption at the first hour of theprocess, indicating that biosorption is more favorable at lower values ofpH.The adsorption kinetic was studied at pH 2.0andcompared withthe kinetic mechanism ofconvective mass transfer and diffusion models.The kinetic behavior of biosorption process showed a high amount of dye adsorbedat the beginning of the process, however, after saturation of the functional groups, the adsorption rate decreased over time until saturation. After 24 hours of batch operation, the uptake of tartrazine onto silkworm cocoons was 40.39 mg g-1,and the rate of adsorption reduces from 3.430 to 0.003 mg min-1g-1, suggesting that the process approaches the equilibrium. The Crank model provided the best fit. The results revealed that silkworm cocoons have the potential to be used as a biosorbent for wastewater treatment containing tartrazin

Loss of anion transport without increased sodium absorption characterizes newborn porcine cystic fibrosis airway epithelia

Defective transepithelial electrolyte transport is thought to initiate cystic fibrosis (CF) lung disease. Yet, how loss of CFTR affects electrolyte transport remains uncertain. CFTR -/- pigs spontaneously develop lung disease resembling human CF. At birth, their airways exhibit a bacterial host defense defect, but are not inflamed. Therefore, we studied ion transport in newborn nasal and tracheal/bronchial epithelia in tissues, cultures, and in vivo. CFTR -/- epithelia showed markedly reduced Cl - and HCO 3 - transport. However, in contrast to a widely held view, lack of CFTR did not increase transepithelial Na + or liquid absorption or reduce periciliary liquid depth. Like human CF, CFTR -/- pigs showed increased amiloride-sensitive voltage and current, but lack of apical Cl - conductance caused the change, not increased Na + transport. These results indicate that CFTR provides the predominant transcellular pathway for Cl - and HCO 3 - in porcine airway epithelia, and reduced anion permeability may initiate CF airway disease. © 2010 Elsevier Inc.published_or_final_versio

Proteomic DIGE analysis of the mitochondria-enriched fraction from aged rat skeletal muscle

Skeletal muscle aging is associated with a loss in tissue mass and contractile strength, as well as fiber type shifting and bioenergetic adaptation processes. Since mitochondria represent the primary site for energy generation via oxidative phosphorylation, we investigated potential changes in the expression pattern of the mitochondrial proteome using the highly sensitive DIGE approach. The comparative analysis of the mitochondria-enriched fraction from young adult versus aged muscle revealed an age-related change in abundance for 39 protein species. MS technology identified the majority of altered proteins as constituents of muscle mitochondria. An age-dependent increase was observed for NADH dehydrogenase, the mitochondrial inner membrane protein mitofilin, peroxiredoxin isoform PRX-III, ATPase synthase, succinate dehydrogenase, mitochondrial fission protein Fis1, succinate-coenzyme A ligase, acyl-coenzyme A dehydrogenase, porin isoform VDAC2, ubiquinol-cytochrome c reductase core I protein and prohibitin. Immunoblotting, enzyme testing and confocal microscopy were used to validate proteomic findings. The DIGE-identified increase in key mitochondrial elements during aging agrees with the concept that sarcopenia is associated with a shift to a slower contractile phenotype and more pronounced aerobic-oxidative metabolism. This suggests that mitochondrial markers are reliable candidates that should be included in the future establishment of a biomarker signature of skeletal muscle aging

Computer aided synthesis: a game theoretic approach

In this invited contribution, we propose a comprehensive introduction to game theory applied in computer aided synthesis. In this context, we give some classical results on two-player zero-sum games and then on multi-player non zero-sum games. The simple case of one-player games is strongly related to automata theory on infinite words. All along the article, we focus on general approaches to solve the studied problems, and we provide several illustrative examples as well as intuitions on the proofs.Comment: Invitation contribution for conference "Developments in Language Theory" (DLT 2017

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn

Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

Background: The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation. Results: Over 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from ≥88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections. Conclusion: To overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field

Exploring men's and women's experiences of depression and engagement with health professionals: more similarities than differences? A qualitative interview study

<p>Abstract</p> <p>Background</p> <p>It is argued that the ways in which women express emotional distress mean that they are more likely to be diagnosed with depression, while men's relative lack of articulacy means their depression is hidden. This may have consequences for communicating with health professionals. The purpose of this analysis was to explore how men and women with depression articulate their emotional distress, and examine whether there are gender differences or similarities in the strategies that respondents found useful when engaging with health professionals.</p> <p>Methods</p> <p>In-depth qualitative interviews with 22 women and 16 men in the UK who identified themselves as having had depression, recruited through general practitioners, psychiatrists and support groups.</p> <p>Results</p> <p>We found gender similarities and gender differences in our sample. Both men and women found it difficult to recognise and articulate mental health problems and this had consequences for their ability to communicate with health professionals. Key gender differences noted were that men tended to value skills which helped them to talk while women valued listening skills in health professionals, and that men emphasised the importance of getting practical results from talking therapies in their narratives, as opposed to other forms of therapy which they conceptualised as 'just talking'. We also found diversity among women and among men; some respondents valued a close personal relationship with health professionals, while others felt that this personal relationship was a barrier to communication and preferred 'talking to a stranger'.</p> <p>Conclusion</p> <p>Our findings suggest that there is not a straightforward relationship between gender and engagement with health professionals for people with depression. Health professionals need to be sensitive to patients who have difficulties in expressing emotional distress and critical of gender stereotypes which suggest that women invariably find it easy to express emotional distress and men invariably find it difficult. In addition it is important to recognise that, for a minority of patients, a personal relationship with health professionals can act as a barrier to the disclosure of emotional distress.</p