On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres

This paper focuses on the modeling of musical melodies as networks. Notes of a melody can be treated as nodes of a network. Connections are created whenever notes are played in sequence. We analyze some main tracks coming from different music genres, with melodies played using different musical instruments. We find out that the considered networks are, in general, scale free networks and exhibit the small world property. We measure the main metrics and assess whether these networks can be considered as formed by sub-communities. Outcomes confirm that peculiar features of the tracks can be extracted from this analysis methodology. This approach can have an impact in several multimedia applications such as music didactics, multimedia entertainment, and digital music generation.Comment: accepted to Multimedia Tools and Applications, Springe

Extraction of dynamic patterns from static rna expression data: an application to hematological neoplasms

Within the study of pathological conditions from the analysis of high-throughput data, the usual approach consists in using supervised classification algorithms. Such approach frequently fails due to the initial bias of class definition uncertainty. We used an unsupervised approach to arrange samples according to the progression state of a disease by using a tool, Sample Progression Discovery, developed by Peng Qiu et al. After evaluating its functionality and how to handle its critical aspects, we applied it to two pathologies: chronic lymphocytic leukemia and Waldenström’s macroglobulinemia. The progressions found show good correspondence with clinical parameters under some constraints on the inpu

Revealing chronic disease progression patterns using Gaussian process for stage inference.

OBJECTIVE: The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development. MATERIALS AND METHODS: We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA). RESULTS: First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets. CONCLUSION: Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients

Role of appetitive phenotype trajectory groups on child body weight during a family-based treatment for children with overweight or obesity

OBJECTIVE:Emerging evidence suggests that individual appetitive traits may usefully explain patterns of weight loss in behavioral weight loss treatments for children. The objective of this study was to identify trajectories of child appetitive traits and the impact on child weight changes over time.METHODS:Secondary data analyses of a randomized noninferiority trial conducted between 2011 and 2015 evaluated children's appetitive traits and weight loss. Children with overweight and obesity (mean age = 10.4; mean BMI z = 2.0; 67% girls; 32% Hispanic) and their parent (mean age = 42.9; mean BMI = 31.9; 87% women; 31% Hispanic) participated in weight loss programs and completed assessments at baseline, 3, 6,12, and 24 months. Repeated assessments of child appetitive traits, including satiety responsiveness, food responsiveness and emotional eating, were used to identify parsimonious grouping of change trajectories. Linear mixed-effects models were used to identify the impact of group trajectory on child BMIz change over time.RESULTS:One hundred fifty children and their parent enrolled in the study. The three-group trajectory model was the most parsimonious and included a high satiety responsive group (HighSR; 47.4%), a high food responsive group (HighFR; 34.6%), and a high emotional eating group (HighEE; 18.0%). Children in all trajectories lost weight at approximately the same rate during treatment, however, only the HighSR group maintained their weight loss during follow-ups, while the HighFR and HighEE groups regained weight (adjusted p-value < 0.05).CONCLUSIONS:Distinct trajectories of child appetitive traits were associated with differential weight loss maintenance. Identified high-risk subgroups may suggest opportunities for targeted intervention and maintenance programs

Probability state modeling of memory CD8+ T-cell differentiation

AbstractFlow cytometric analysis enables the simultaneous single-cell interrogation of multiple biomarkers for phenotypic and functional identification of heterogeneous populations. Analysis of polychromatic data has become increasingly complex with more measured parameters. Furthermore, manual gating of multiple populations using standard analysis techniques can lead to errors in data interpretation and difficulties in the standardization of analyses. To characterize high-dimensional cytometric data, we demonstrate the use of probability state modeling (PSM) to visualize the differentiation of effector/memory CD8+ T cells. With this model, four major CD8+ T-cell subsets can be easily identified using the combination of three markers, CD45RA, CCR7 (CD197), and CD28, with the selection markers CD3, CD4, CD8, and side scatter (SSC). PSM enables the translation of complex multicolor flow cytometric data to pathway-specific cell subtypes, the capability of developing averaged models of healthy donor populations, and the analysis of phenotypic heterogeneity. In this report, we also illustrate the heterogeneity in memory T-cell subpopulations as branched differentiation markers that include CD127, CD62L, CD27, and CD57