A bioinformatic analysis identifies circadian expression of splicing factors and time-dependent alternative splicing events in the HD-MY-Z cell line

The circadian clock regulates key cellular processes and its dysregulation is associated to several pathologies including cancer. Although the transcriptional regulation of gene expression by the clock machinery is well described, the role of the clock in the regulation of post-transcriptional processes, including splicing, remains poorly understood. In the present work, we investigated the putative interplay between the circadian clock and splicing in a cancer context. For this, we applied a computational pipeline to identify oscillating genes and alternatively spliced transcripts in time-course high-throughput data sets from normal cells and tissues, and cancer cell lines. We investigated the temporal phenotype of clock-controlled genes and splicing factors, and evaluated their impact in alternative splice patterns in the Hodgkin Lymphoma cell line HD-MY-Z. Our data points to a connection between clock-controlled genes and splicing factors, which correlates with temporal alternative splicing in several genes in the HD-MY-Z cell line. These include the genes DPYD, SS18, VIPR1 and IRF4, involved in metabolism, cell cycle, apoptosis and proliferation. Our results highlight a role for the clock as a temporal regulator of alternative splicing, which may impact malignancy in this cellular model

Towards a style-specific basis for computational beat tracking

Outlined in this paper are a number of sources of evidence, from psychological, ethnomusicological and engineering grounds, to suggest that current approaches to computational beat tracking are incomplete. It is contended that the degree to which cultural knowledge, that is, the specifics of style and associated learnt representational schema, underlie the human faculty of beat tracking has been severely underestimated. Difficulties in building general beat tracking solutions, which can provide both period and phase locking across a large corpus of styles, are highlighted. It is probable that no universal beat tracking model exists which does not utilise a switching model to recognise style and context prior to application

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

Feature Extraction in Music information retrival using Machine Learning Algorithms

Music classification is essential for faster Music record recovery. Separating the ideal arrangement of highlights and selecting the best investigation technique are critical for obtaining the best results from sound grouping. The extraction of sound elements could be viewed as an exceptional case of information sound information being transformed into sound instances. Music division and order can provide a rich dataset for the analysis of sight and sound substances. Because of the great dimensionality of sound highlights as well as the variable length of sound fragments, Music layout is dependent on the overpowering computation. By focusing on rhythmic aspects of different songs, this article provides an introduction of some of the possibilities for computing music similarity. Almost every MIR toolkit includes a method for extracting the beats per minute (BPM) and consequently the tempo of each music. The simplest method of computing very low-level rhythmic similarities is to sort and compare songs solely by their tempo There are undoubtedly far better and more precise solutions.  work discusses some of the most promising ways for computing rhythm similarities in a Big Data framework usaing machine Learning algorithms

Computational methods for percussion music analysis : the afro-uruguayan candombe drumming as a case study

Most of the research conducted on information technologies applied to music has been largely limited to a few mainstream styles of the so-called `Western' music. The resulting tools often do not generalize properly or cannot be easily extended to other music traditions. So, culture-specific approaches have been recently proposed as a way to build richer and more general computational models for music. This thesis work aims at contributing to the computer-aided study of rhythm, with the focus on percussion music and in the search of appropriate solutions from a culture specifc perspective by considering the Afro-Uruguayan candombe drumming as a case study. This is mainly motivated by its challenging rhythmic characteristics, troublesome for most of the existing analysis methods. In this way, it attempts to push ahead the boundaries of current music technologies. The thesis o ers an overview of the historical, social and cultural context in which candombe drumming is embedded, along with a description of the rhythm. One of the specific contributions of the thesis is the creation of annotated datasets of candombe drumming suitable for computational rhythm analysis. Performances were purposely recorded, and received annotations of metrical information, location of onsets, and sections. A dataset of annotated recordings for beat and downbeat tracking was publicly released, and an audio-visual dataset of performances was obtained, which serves both documentary and research purposes. Part of the dissertation focused on the discovery and analysis of rhythmic patterns from audio recordings. A representation in the form of a map of rhythmic patterns based on spectral features was devised. The type of analyses that can be conducted with the proposed methods is illustrated with some experiments. The dissertation also systematically approached (to the best of our knowledge, for the first time) the study and characterization of the micro-rhythmical properties of candombe drumming. The ndings suggest that micro-timing is a structural component of the rhythm, producing a sort of characteristic "swing". The rest of the dissertation was devoted to the automatic inference and tracking of the metric structure from audio recordings. A supervised Bayesian scheme for rhythmic pattern tracking was proposed, of which a software implementation was publicly released. The results give additional evidence of the generalizability of the Bayesian approach to complex rhythms from diferent music traditions. Finally, the downbeat detection task was formulated as a data compression problem. This resulted in a novel method that proved to be e ective for a large part of the dataset and opens up some interesting threads for future research.La mayoría de la investigación realizada en tecnologías de la información aplicadas a la música se ha limitado en gran medida a algunos estilos particulares de la así llamada música `occidental'. Las herramientas resultantes a menudo no generalizan adecuadamente o no se pueden extender fácilmente a otras tradiciones musicales. Por lo tanto, recientemente se han propuesto enfoques culturalmente específicos como forma de construir modelos computacionales más ricos y más generales. Esta tesis tiene como objetivo contribuir al estudio del ritmo asistido por computadora, desde una perspectiva cultural específica, considerando el candombe Afro-Uruguayo como caso de estudio. Esto está motivado principalmente por sus características rítmicas, problemáticas para la mayoría de los métodos de análisis existentes. Así , intenta superar los límites actuales de estas tecnologías. La tesis ofrece una visión general del contexto histórico, social y cultural en el que el candombe está integrado, junto con una descripción de su ritmo. Una de las contribuciones específicas de la tesis es la creación de conjuntos de datos adecuados para el análisis computacional del ritmo. Se llevaron adelante sesiones de grabación y se generaron anotaciones de información métrica, ubicación de eventos y secciones. Se disponibilizó públicamente un conjunto de grabaciones anotadas para el seguimiento de pulso e inicio de compás, y se generó un registro audiovisual que sirve tanto para fines documentales como de investigación. Parte de la tesis se centró en descubrir y analizar patrones rítmicos a partir de grabaciones de audio. Se diseñó una representación en forma de mapa de patrones rítmicos basada en características espectrales. El tipo de análisis que se puede realizar con los métodos propuestos se ilustra con algunos experimentos. La tesis también abordó de forma sistemática (y por primera vez) el estudio y la caracterización de las propiedades micro rítmicas del candombe. Los resultados sugieren que las micro desviaciones temporales son un componente estructural del ritmo, dando lugar a una especie de "swing" característico. El resto de la tesis se dedicó a la inferencia automática de la estructura métrica a partir de grabaciones de audio. Se propuso un esquema Bayesiano supervisado para el seguimiento de patrones rítmicos, del cual se disponibilizó públicamente una implementación de software. Los resultados dan evidencia adicional de la capacidad de generalización del enfoque Bayesiano a ritmos complejos. Por último, la detección de inicio de compás se formuló como un problema de compresión de datos. Esto resultó en un método novedoso que demostró ser efectivo para una buena parte de los datos y abre varias líneas de investigación

Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity

Using a large database (~ 215 000 records) of relevant articles, we empirically study the "complex systems" field and its claims to find universal principles applying to systems in general. The study of references shared by the papers allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific "trading zones", ie sub-communities that create an interface around specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and Technology (2012) 10.1002/asi.2264

Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects.

The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively

Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version