10,063 research outputs found
Investing time: Teacher research observing the influence of music history and theory lessons upon student engagement and expressive performance of an advanced high school string quartet
This teacher-conducted research observes the influence of music history and theory instruction upon motivation, engagement, and expressive performance of the author’s high school string students. Two diverse teaching approaches were introduced sequentially as students learned two movements of Schubert’s "Death and the Maiden" Quartet (D810). The first movement was taught using performance-based instruction only, while the second movement was taught with a combination of performance-based instruction and music history and theory lessons. Student comments and teacher observations revealed that the incorporation of music history and theory lessons into performance instruction was (a) motivational to students, (b) a catalyst for expressive performance, and (c) an effective use of rehearsal time. Independent adjudicator scores were higher for the second movement than for the first, although several additional explanations are given that may also explain the variation in scores. Pedagogical recommendations are provided for incorporating music history/theory lessons into performance rehearsals
Neural Information Processing: between synchrony and chaos
The brain is characterized by performing many different processing tasks ranging from elaborate processes such as pattern recognition, memory or decision-making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Some recent empirical and theoretical results support the notion that the brain is naturally poised between ordered and chaotic states. As the largest number of metastable states exists at a point near the transition, the brain therefore has access to a larger repertoire of behaviours. Consequently, it is of high interest to know which type of processing can be associated with both ordered and disordered states. Here we show an explanation of which processes are related to chaotic and synchronized states based on the study of in-silico implementation of biologically plausible neural systems. The measurements obtained reveal that synchronized cells (that can be understood as ordered states of the brain) are related to non-linear computations, while uncorrelated neural ensembles are excellent information transmission systems that are able to implement linear transformations (as the realization of convolution products) and to parallelize neural processes. From these results we propose a plausible meaning for Hebbian and non-Hebbian learning rules as those biophysical mechanisms by which the brain creates ordered or chaotic ensembles depending on the desired functionality. The measurements that we obtain from the hardware implementation of different neural systems endorse the fact that the brain is working with two different states, ordered and chaotic, with complementary functionalities that imply non-linear processing (synchronized states) and information transmission and convolution (chaotic states)
Criticality of mostly informative samples: A Bayesian model selection approach
We discuss a Bayesian model selection approach to high dimensional data in
the deep under sampling regime. The data is based on a representation of the
possible discrete states , as defined by the observer, and it consists of
observations of the state. This approach shows that, for a given sample
size , not all states observed in the sample can be distinguished. Rather,
only a partition of the sampled states can be resolved. Such partition
defines an {\em emergent} classification of the states that becomes finer
and finer as the sample size increases, through a process of {\em symmetry
breaking} between states. This allows us to distinguish between the
of a given representation of the observer defined states ,
which is given by the entropy of , and its which is defined by
the entropy of the partition . Relevance has a non-monotonic dependence on
resolution, for a given sample size. In addition, we characterise most relevant
samples and we show that they exhibit power law frequency distributions,
generally taken as signatures of "criticality". This suggests that
"criticality" reflects the relevance of a given representation of the states of
a complex system, and does not necessarily require a specific mechanism of
self-organisation to a critical point.Comment: 31 pages, 7 figure
Complete RNA inverse folding: computational design of functional hammerhead ribozymes
Nanotechnology and synthetic biology currently constitute one of the most
innovative, interdisciplinary fields of research, poised to radically transform
society in the 21st century. This paper concerns the synthetic design of
ribonucleic acid molecules, using our recent algorithm, RNAiFold, which can
determine all RNA sequences whose minimum free energy secondary structure is a
user-specified target structure. Using RNAiFold, we design ten cis-cleaving
hammerhead ribozymes, all of which are shown to be functional by a cleavage
assay. We additionally use RNAiFold to design a functional cis-cleaving
hammerhead as a modular unit of a synthetic larger RNA. Analysis of kinetics on
this small set of hammerheads suggests that cleavage rate of computationally
designed ribozymes may be correlated with positional entropy, ensemble defect,
structural flexibility/rigidity and related measures. Artificial ribozymes have
been designed in the past either manually or by SELEX (Systematic Evolution of
Ligands by Exponential Enrichment); however, this appears to be the first
purely computational design and experimental validation of novel functional
ribozymes. RNAiFold is available at
http://bioinformatics.bc.edu/clotelab/RNAiFold/.Comment: 17 pages, 2 tables, 7 figures, final version to appear in Nucleic
Acids Researc
Linear processes in high-dimension: phase space and critical properties
In this work we investigate the generic properties of a stochastic linear
model in the regime of high-dimensionality. We consider in particular the
Vector AutoRegressive model (VAR) and the multivariate Hawkes process. We
analyze both deterministic and random versions of these models, showing the
existence of a stable and an unstable phase. We find that along the transition
region separating the two regimes, the correlations of the process decay
slowly, and we characterize the conditions under which these slow correlations
are expected to become power-laws. We check our findings with numerical
simulations showing remarkable agreement with our predictions. We finally argue
that real systems with a strong degree of self-interaction are naturally
characterized by this type of slow relaxation of the correlations.Comment: 40 pages, 5 figure
Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1
Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions
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