Fuzzy clustering: insights and a new approach

Fuzzy clustering extends crisp clustering in the sense that objects can belong to various clusters with different membership degrees at the same time, whereas crisp or deterministic clustering assigns each object to a unique cluster. The standard approach to fuzzy clustering introduces the so-called fuzzifier which controls how much clusters may overlap. In this paper we illustrate, how this fuzzifier can help to reduce the number of undesired local minima of the objective function that is associated with fuzzy clustering. Apart from this advantage, the fuzzifier has also some drawbacks that are discussed in this paper. A deeper analysis of the fuzzifier concept leads us to a more general approach to fuzzy clustering that can overcome the problems caused by the fuzzifier

Learning fuzzy systems: an ojective function-approach

One of the most important aspects of fuzzy systems is that they are easily understandable and interpretable. This property, however, does not come for free but poses some essential constraints on the parameters of a fuzzy system (like the linguistic terms), which are sometimes overlooked when learning fuzzy system automatically from data. In this paper, an objective function-based approach to learn fuzzy systems is developed, taking these constraints explicitly into account. Starting from fuzzy c-means clustering, several modifications of the basic algorithm are proposed, affecting the shape of the membership functions, the partition of individual variables and the coupling of input space partitioning and local function approximation

Infection- and procedure-dependent effects on pulmonary gene expression in the early phase of influenza A virus infection in mice

BACKGROUND: Investigating the host response in the early stage of influenza A virus (IAV) infection is of considerable interest. However, it is conceivable that effects due to the anesthesia and/or intranasal infection procedure might introduce artifacts. We therefore aimed to evaluate the effects of anesthesia and/or intranasal infection on transcription of selected pulmonary mRNAs in two inbred mouse strains with differential susceptibility to IAV infection. RESULTS: DBA/2J and C57BL/6J mice were evaluated in a time course experiment in which lung tissue was sampled after 6, 12, 18, 24, 48 and 120 h. After anesthesia with ketamine and xylazine, a suspension of mouse-adapted IAV strain PR8_Mun in 20 μl sterile buffer, or 20 μl sterile buffer only, was instilled intranasally. The mice receiving anesthesia and PBS only were designated the “mock treatment” group. Pulmonary expression of 10 host mRNAs (Fos, Retnla, Irg1, Il6, Il1b, Cxcl10, Stat1, Ifng, Ifnl2, and Mx1) and viral hemagglutinin (HA) mRNA were determined at the designated time points. As expected, weight loss and viral replication were greater in the DBA/2J strain (which is more susceptible to IAV infection). Four mRNAs (Retnla, Irg1, Il6, and Cxcl10) were procedure-dependently regulated in DBA/2J mice between 6 and 24 h, and two (Retnla and Il6) in C57BL/6J mice, although to a lesser extent. All 10 mRNAs rose after infection, but one (Fos) only in DBA/2J mice. These infection-dependent effects could be separated from procedure-dependent effects beginning around 12 h in DBA/2J and 18 h in C57BL/6J mice. The interferon-related mRNAs Stat1, Ifng, Infl2, and Mx1 were unaffected by mock treatment in either mouse strain. Mx1 and Infl2 correlated best with HA mRNA expression (r = 0.97 and 0.93, respectively, in DBA/2J). CONCLUSIONS: These results demonstrate effects of the anesthesia and/or intranasal infection procedure on pulmonary gene expression, which are detectable between approximately 6 and 24 h post procedure and vary in intensity and temporal evolution depending on the mouse strain used. Mock infection controls should be included in all studies on pulmonary gene expression in the early phase of infection with IAV and, likely, other respiratory pathogens

Single pass clustering for large data sets

The presence of very large data sets poses new problems to standard neural clustering and visualization algorithms such as Neural Gas (NG) and the Self-Organizing-Map (SOM) due to memory and time constraints. In such situations, it is no longer possible to store all data points in the main memory at once and only a few, ideally only one run over the whole data set is still affordable to achieve a feasible training time. In this contribution we propose single pass extensions of the classical clustering algorithms NG and fuzzy-k-means which are based on a simple patch decomposition of the data set and fast batch optimization schemes of the respective cost function. The algorithms maintain the benefits of the original ones including easy implementation and interpretation as well as large flexibility and adaptability because of the underlying cost function. We demonstrate the efficiency of the approach in a variety of experiments

Evolutionary conservation of essential and highly expressed genes in Pseudomonas aeruginosa

<p>Abstract</p> <p>Background</p> <p>The constant increase in development and spread of bacterial resistance to antibiotics poses a serious threat to human health. New sequencing technologies are now on the horizon that will yield massive increases in our capacity for DNA sequencing and will revolutionize the drug discovery process. Since essential genes are promising novel antibiotic targets, the prediction of gene essentiality based on genomic information has become a major focus.</p> <p>Results</p> <p>In this study we demonstrate that pooled sequencing is applicable for the analysis of sequence variations of strain collections with more than 10 individual isolates. Pooled sequencing of 36 clinical <it>Pseudomonas aeruginosa </it>isolates revealed that essential and highly expressed proteins evolve at lower rates, whereas extracellular proteins evolve at higher rates. We furthermore refined the list of experimentally essential <it>P. aeruginosa </it>genes, and identified 980 genes that show no sequence variation at all. Among the conserved nonessential genes we found several that are involved in regulation, motility and virulence, indicating that they represent factors of evolutionary importance for the lifestyle of a successful environmental bacterium and opportunistic pathogen.</p> <p>Conclusion</p> <p>The detailed analysis of a comprehensive set of <it>P. aeruginosa </it>genomes in this study clearly disclosed detailed information of the genomic makeup and revealed a large set of highly conserved genes that play an important role for the lifestyle of this microorganism. Sequencing strain collections enables for a detailed and extensive identification of sequence variations as potential bacterial adaptation processes, e.g., during the development of antibiotic resistance in the clinical setting and thus may be the basis to uncover putative targets for novel treatment strategies.</p

Social participation and mental health of immunocompromised individuals before and after COVID-19 vaccination–Results of a longitudinal observational study over three time points

IntroductionThe coronavirus disease 2019 (COVID-19) pandemic impacted how people perform their daily lives in manifold and sometimes massive ways. Particularly, individuals who are at high risk for a severe disease progression, like immunocompromised people, may have experienced drastic changes in social participation during the pandemic. A COVID-19 basic vaccination may have changed the safety behavior of immunocompromised individuals in terms of infection risk and thereby influence social participation and mental wellbeing.MethodsThis study aims to investigate self-perceived social participation at baseline before and at follow-up 1 and 6 months after basic vaccination. Beginning in March 2021, 274 immunocompromised persons 18 years or older were enrolled in the COVID-19 Contact Immune study (CoCo study) in Lower Saxony, Germany. Measurements were performed at three time points regarding social participation [Index for the Assessment of Health Impairments (IMET)], mental health [Patient Health Questionnaire-4 (PHQ-4)], subjective health status (five-point Likert-scale) and quality of life (five-point Likert-scale).ResultsIn total, 126 participants were included in the final analysis. About 60% of the participants showed increasing social participation over time. The greatest increase in social participation was observed within the first month after basic vaccination (p &lt; 0.001). During the following 5 months, social participation remained stable. The domains “social activities,” “recreation and leisure” and “close personal relationships” were responsible for the overall change in social participation. No association was found between social participation and mental health, sociodemographic or medical factors (except hypertension).DiscussionIt is unclear why social participation increased after basic vaccination. Perceived vaccine efficacy and a feeling of being protected by the vaccine may have caused relaxed social distancing behaviors. Reducing safety behaviors may, however, increase the risk of a COVID-19 infection for immunocompromised individuals. Further investigations are needed to explore the health-related consequences of more social participation among immunocompromised persons

Transcriptional network analysis identifies key elements governing the recombinant protein production provoked reprogramming of carbon and energy metabolism in Escherichia coli BL21 (DE3)

The impact of recombinant protein production on carbon and energy metabolism in Escherichia coli BL21 (DE3) was studied through transcriptome and proteome analysis of cells induced in carbon-limited fed-batch cultures during either fast or slow growth. Production of human basic fibroblast growth factor (pET expression system, T7 promoter) during fast growth leads to a macroscopically observable response classifiable into two consecutive steps: i. apparently unperturbed growth and respiration with concomitant formation of pyruvate and acetate followed by ii. inhibition of growth, respiratory activity and glucose uptake. Down-regulation of genes involved in sugar and acetate uptake, tricarboxylic acid (TCA) cycle, and respiratory energy generation started already during apparently unperturbed growth with the exceptions of up-regulated genes encoding the less energy efficient NADH dehydrogenase and terminal oxidases. A transcription factor target gene network analysis revealed that observed changes are mainly attributable to the vanishing influence of the transcription factor CRP-cAMP but also to a strong down-regulation of AcrA-P repressed genes. Moreover, down-regulation of MalT activated and up-regulation of PdhR repressed genes contribute among others to the reorganization of the transcriptome. The main drivers were identified as accumulating metabolites, for example, pyruvate, which affect transcription factor activity. The resulting restructured proteome leads to reduced glucose uptake, TCA cycle, and respiratory capacities this way decreasing catabolic carbon breakdown and metabolite accumulation. At slow growth, the production provoked transcriptome rearrangements are more subtle not leading to a macroscopically evident response. In summary, the transcriptomic response towards recombinant gene expression mimics a carbon or nutrient up-shift response aiming to match catabolic carbon processing with compromised anabolic capacities of induced cells. It is not the reason for growth inhibition and the metabolic burden but the cellular attempt to attenuate the “toxic effect” of recombinant gene expression by reducing carbon catabolism

JProGO: a novel tool for the functional interpretation of prokaryotic microarray data using Gene Ontology information

A novel program suite was implemented for the functional interpretation of high-throughput gene expression data based on the identification of Gene Ontology (GO) nodes. The focus of the analysis lies on the interpretation of microarray data from prokaryotes. The three well established statistical methods of the threshold value-based Fisher's exact test, as well as the threshold value-independent Kolmogorov–Smirnov and Student's t-test were employed in order to identify the groups of genes with a significantly altered expression profile. Furthermore, we provide the application of the rank-based unpaired Wilcoxon's test for a GO-based microarray data interpretation. Further features of the program include recognition of the alternative gene names and the correction for multiple testing. Obtained results are visualized interactively both as a table and as a GO subgraph including all significant nodes. Currently, JProGO enables the analysis of microarray data from more than 20 different prokaryotic species, including all important model organisms, and thus constitutes a useful web service for the microbial research community. JProGO is freely accessible via the web at the following address