Stability of graph communities across time scales

The complexity of biological, social and engineering networks makes it desirable to find natural partitions into communities that can act as simplified descriptions and provide insight into the structure and function of the overall system. Although community detection methods abound, there is a lack of consensus on how to quantify and rank the quality of partitions. We show here that the quality of a partition can be measured in terms of its stability, defined in terms of the clustered autocovariance of a Markov process taking place on the graph. Because the stability has an intrinsic dependence on time scales of the graph, it allows us to compare and rank partitions at each time and also to establish the time spans over which partitions are optimal. Hence the Markov time acts effectively as an intrinsic resolution parameter that establishes a hierarchy of increasingly coarser clusterings. Within our framework we can then provide a unifying view of several standard partitioning measures: modularity and normalized cut size can be interpreted as one-step time measures, whereas Fiedler's spectral clustering emerges at long times. We apply our method to characterize the relevance and persistence of partitions over time for constructive and real networks, including hierarchical graphs and social networks. We also obtain reduced descriptions for atomic level protein structures over different time scales.Comment: submitted; updated bibliography from v

Modularity functions maximization with nonnegative relaxation facilitates community detection in networks

We show here that the problem of maximizing a family of quantitative functions, encompassing both the modularity (Q-measure) and modularity density (D-measure), for community detection can be uniformly understood as a combinatoric optimization involving the trace of a matrix called modularity Laplacian. Instead of using traditional spectral relaxation, we apply additional nonnegative constraint into this graph clustering problem and design efficient algorithms to optimize the new objective. With the explicit nonnegative constraint, our solutions are very close to the ideal community indicator matrix and can directly assign nodes into communities. The near-orthogonal columns of the solution can be reformulated as the posterior probability of corresponding node belonging to each community. Therefore, the proposed method can be exploited to identify the fuzzy or overlapping communities and thus facilitates the understanding of the intrinsic structure of networks. Experimental results show that our new algorithm consistently, sometimes significantly, outperforms the traditional spectral relaxation approaches

Beurteilung der aldosteronantagonistischen Wirkung eines Diuretikums

Peer Reviewe

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells

Perceived importance of components of asynchronous music in circuit training

This study examined regular exercisers’ perceptions of specific components of music during circuit training. Twenty-four men (38.8 years, s = 11.8 years) and 31 women (32.4 years, s = 9.6 years) completed two questionnaires immediately after a circuit training class. Participants rated the importance of 13 components of music (rhythm, melody, etc.) in relation to exercise enjoyment, and each completed the Affect Intensity Measure (Larsen, 1984) to measure emotional reactivity. Independent t tests were used to evaluate gender differences in perceptions of musical importance. Pearson correlations were computed to evaluate the relationships between affect intensity, age and importance of musical components. Consistent with previous research and theoretical predictions, rhythm response components (rhythm, tempo, beat) were rated as most important. Women rated the importance of melody significantly higher than did men, while men gave more importance to music associated with sport. Affect intensity was found to be positively and significantly related to the perceived importance of melody, lyrical content, musical style, personal associations and emotional content. Results suggest that exercise leaders need to be sensitive to personal factors when choosing music to accompany exercise. Qualitative research that focuses on the personal meaning of music is encouraged

A Music-Related Quality of Life measure to guide music rehabilitation for adult CI users

Purpose: A music-related quality of life (MuRQoL) questionnaire was developed for the evaluation of music rehabilitation for adult cochlear implant (CI) users. The present studies were aimed at refinement and validation. Method: Twenty-four experts reviewed the MuRQoL items for face validity. A refined version was completed by 147 adult CI users and psychometric techniques were used for item selection, assessment of reliability and definition of the factor structure. The same participants completed the Short Form Health Survey for construct validation. MuRQoL responses from 68 CI users were compared with those of a matched group of normal-hearing (NH) adults. Results: Eighteen items measuring music perception & engagement and 18 items measuring their importance were selected; they grouped together into two domains. The final questionnaire has high internal consistency and repeatability. Significant differences between CI users and NH adults and a correlation between music engagement and quality of life (QoL) support construct validity. Scores of music perception & engagement and importance for the 18 items can be combined to assess the impact of music on the QoL. Conclusion: The MuRQoL questionnaire is a reliable and valid measure of self-reported music perception, engagement and their importance for adult CI users with potential to guide music aural rehabilitation

Inflammatory B cells correlate with failure to checkpoint blockade in melanoma patients.

The understanding of the role of B cells in patients with solid tumors remains insufficient. We found that circulating B cells produced TNFα and/or IL-6, associated with unresponsiveness and poor overall survival of melanoma patients treated with anti-CTLA4 antibody. Transcriptome analysis of B cells from melanoma metastases showed enriched expression of inflammatory response genes. Publicly available single B cell data from the tumor microenvironment revealed a negative correlation between TNFα expression and response to immune checkpoint blockade. These findings suggest that B cells contribute to tumor growth via the production of inflammatory cytokines. Possibly, these B cells are different from tertiary lymphoid structure-associated B cells, which have been described to correlate with favorable clinical outcome of cancer patients. Further studies are required to identify and characterize B cell subsets and their functions promoting or counteracting tumor growth, with the aim to identify biomarkers and novel treatment targets

Exploring the Free Energy Landscape: From Dynamics to Networks and Back

The knowledge of the Free Energy Landscape topology is the essential key to understand many biochemical processes. The determination of the conformers of a protein and their basins of attraction takes a central role for studying molecular isomerization reactions. In this work, we present a novel framework to unveil the features of a Free Energy Landscape answering questions such as how many meta-stable conformers are, how the hierarchical relationship among them is, or what the structure and kinetics of the transition paths are. Exploring the landscape by molecular dynamics simulations, the microscopic data of the trajectory are encoded into a Conformational Markov Network. The structure of this graph reveals the regions of the conformational space corresponding to the basins of attraction. In addition, handling the Conformational Markov Network, relevant kinetic magnitudes as dwell times or rate constants, and the hierarchical relationship among basins, complete the global picture of the landscape. We show the power of the analysis studying a toy model of a funnel-like potential and computing efficiently the conformers of a short peptide, the dialanine, paving the way to a systematic study of the Free Energy Landscape in large peptides.Comment: PLoS Computational Biology (in press