506 research outputs found
Finding instabilities in the community structure of complex networks
The problem of finding clusters in complex networks has been extensively
studied by mathematicians, computer scientists and, more recently, by
physicists. Many of the existing algorithms partition a network into clear
clusters, without overlap. We here introduce a method to identify the nodes
lying ``between clusters'' and that allows for a general measure of the
stability of the clusters. This is done by adding noise over the weights of the
edges of the network. Our method can in principle be applied with any
clustering algorithm, provided that it works on weighted networks. We present
several applications on real-world networks using the Markov Clustering
Algorithm (MCL).Comment: 4 pages, 5 figure
Effectiveness of computer-based auditory training in improving the perception of noise-vocoded speech
Five experiments were designed to evaluate the effectiveness of “high-variability” lexical training in improving the ability of normal-hearing subjects to perceive noise-vocoded speech that had been spectrally shifted to simulate tonotopic misalignment. Two approaches to training were implemented. One training approach required subjects to recognize isolated words, while the other training approach required subjects to recognize words in sentences. Both approaches to training improved the ability to identify words in sentences. Improvements following a single session (lasting 1–2 h) of auditory training ranged between 7 and 12 %pts and were significantly larger than improvements following a visual control task that was matched with the auditory training task in terms of the response demands. An additional three sessions of word- and sentence-based training led to further improvements, with the average overall improvement ranging from 13 to 18 %pts. When a tonotopic misalignment of 3 mm rather than 6 mm was simulated, training with several talkers led to greater generalization to new talkers than training with a single talker. The results confirm that computer-based lexical training can help overcome the effects of spectral distortions in speech, and they suggest that training materials are most effective when several talkers are included
SwissTargetPrediction: a web server for target prediction of bioactive small molecules.
Bioactive small molecules, such as drugs or metabolites, bind to proteins or other macro-molecular targets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mechanisms underlying their bioactivity and predicting potential side effects or cross-reactivity. Recently, large datasets of protein-small molecule interactions have become available, providing a unique source of information for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTargetPrediction, a web server to accurately predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five different organisms, and mapping predictions by homology within and between different species is enabled for close paralogs and orthologs. SwissTargetPrediction is accessible free of charge and without login requirement at http://www.swisstargetprediction.ch
Uncovering the topology of configuration space networks
The configuration space network (CSN) of a dynamical system is an effective
approach to represent the ensemble of configurations sampled during a
simulation and their dynamic connectivity. To elucidate the connection between
the CSN topology and the underlying free-energy landscape governing the system
dynamics and thermodynamics, an analytical soluti on is provided to explain the
heavy tail of the degree distribution, neighbor co nnectivity and clustering
coefficient. This derivation allows to understand the universal CSN network
topology observed in systems ranging from a simple quadratic well to the native
state of the beta3s peptide and a 2D lattice heteropolymer. Moreover CSN are
shown to fall in the general class of complex networks describe d by the
fitness model.Comment: 6 figure
Extracting the multiscale backbone of complex weighted networks
A large number of complex systems find a natural abstraction in the form of
weighted networks whose nodes represent the elements of the system and the
weighted edges identify the presence of an interaction and its relative
strength. In recent years, the study of an increasing number of large scale
networks has highlighted the statistical heterogeneity of their interaction
pattern, with degree and weight distributions which vary over many orders of
magnitude. These features, along with the large number of elements and links,
make the extraction of the truly relevant connections forming the network's
backbone a very challenging problem. More specifically, coarse-graining
approaches and filtering techniques are at struggle with the multiscale nature
of large scale systems. Here we define a filtering method that offers a
practical procedure to extract the relevant connection backbone in complex
multiscale networks, preserving the edges that represent statistical
significant deviations with respect to a null model for the local assignment of
weights to edges. An important aspect of the method is that it does not
belittle small-scale interactions and operates at all scales defined by the
weight distribution. We apply our method to real world network instances and
compare the obtained results with alternative backbone extraction techniques
Computational KIR copy number discovery reveals interaction between inhibitory receptor burden and survival.
Natural killer (NK) cells have increasingly become a target of interest for immunotherapies. NK cells express killer immunoglobulin-like receptors (KIRs), which play a vital role in immune response to tumors by detecting cellular abnormalities. The genomic region encoding the 16 KIR genes displays high polymorphic variability in human populations, making it difficult to resolve individual genotypes based on next generation sequencing data. As a result, the impact of polymorphic KIR variation on cancer phenotypes has been understudied. Currently, labor-intensive, experimental techniques are used to determine an individual's KIR gene copy number profile. Here, we develop an algorithm to determine the germline copy number of KIR genes from whole exome sequencing data and apply it to a cohort of nearly 5000 cancer patients. We use a k-mer based approach to capture sequences unique to specific genes, count their occurrences in the set of reads derived from an individual and compare the individual's k-mer distribution to that of the population. Copy number results demonstrate high concordance with population copy number expectations. Our method reveals that the burden of inhibitory KIR genes is associated with survival in two tumor types, highlighting the potential importance of KIR variation in understanding tumor development and response to immunotherapy
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
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