DigiDrum:A Haptic-based Virtual Reality Musical Instrument and a Case Study

This paper presents DigiDrum – a novel virtual reality musical instrument (VRMI) which consists of a physical drum augmented by virtual reality (VR) to produce enhanced auditory and haptic feedback. The physical drum membrane is driven by a simulated membrane of which the parameters can be changed on the fly. The design and implementation of the instrument setup are detailed together with the preliminary results of a user study which investigates users’ haptic perception of the material stiffness of the drum membrane. The study tests whether the tension in the membrane simulation and the sound damping (how fast the sound dies out) changes users’ perception of drum membrane stiffness. Preliminary results show that higher values for both tension and damping give the illusion of higher material stiffness in the drum membrane, where the damping appears to be the more important factor. The goal and contribution of this work is twofold: on the one hand it introduces a musical instrument which allows for enhanced musical expression possibilities through VR. On the other hand, it presents an early investigation on how haptics influence users’ interaction in VRMIs by presenting a preliminary study

Which digit is larger? Brain responses to number and size interactions in a numerical Stroop task

When comparing the digits of different physical sizes, the processing of numerical value interacts with the processing of physical size. Given the universal use of Arabic numbers in mathematics and daily life, this study aims to elucidate the cognitive processes involved in the interactions of task-relevant and task-irrelevant features during information processing. We investigated this question by examining event-related potential (ERP) using a modified version of the size congruity comparison, which is a Stroop-like task. Numerical value and physical size were varied independently under task-relevant and task-irrelevant conditions. To better examine how the task-irrelevant features modulated the processing of the task-relevant attributes, a neutral condition was included in both tasks. For the physical task, congruent trials showed a less negative N200 response than neutral trials (indicating a facilitation effect), and incongruent trials elicited a larger N450 and smaller late positive complex (LPC) response than neutral trials (indicating an interference effect). For the numerical task, congruent trials showed a larger LPC response than neutral trials (indicating a facilitation effect). These ERP findings indicate that the sources of the facilitation and interference effects appear in different cognitive processes for each task. We further suggest that language characteristics may be a factor in the superior numerical processing exhibited in this study

Machine learning approaches for personalized medicine

The work carried out during the Ph.D. in Medical Sciences and Biotechnology course tested the application of machine learning models in several precision medicine topics. In bioinformatic biomarker analysis, the publications focused on discretizing the gene expression levels to obtain a manageable and insightful granularity. The works demonstrated novel analysis pipelines to detect survival and tumor stages from oncologic patients’ biomarkers. The same chapter presented a procedure for a public health decision support system based on machine learning, which has also been demonstrated on the same dataset. The chemoinformatics numerical experiments for drug toxicity, bioaccumulation prediction, or P450 enzyme bioactivity evaluation all exploited spiking neural networks, showing the ability of this technique to handle structural information of the compounds for predictive analysis. For clinical precision medicine, an algorithm has been tested fusing clinical variables (ordinal and binary) from nearly 300 patients to forecast the risk of developing lymphedema after breast cancer therapy. Moreover, free software has been released to measure the volumetry of the affected limb in case of edema or other pathologies requiring tracking of body parts over time. Another chapter reported the development of a free Python library to run equivalence tests in the biomedical sector, focusing on advanced visualization of the statistical outcomes. This library also fills a gap in the biostatistical tools available to Python users requiring biomedical equivalence analysis. Regarding regenerative medicine, a study has been introduced to track octacalcium phosphate synthesis through a machine-learning methodology centered on a novel algorithm exploiting an ad-hoc solution on merged XRD and FTIR peak descriptors. Octacalcium phosphate is found in biological systems, particularly in the early bone formation and mineralization stages. It is a precursor to hydroxyapatite, the main mineral component of bones and teeth. The last chapter introduced a mass spectrometry proteomic analysis sequence to detect aberrant protein expression levels. The procedure has been tested on mesenchymal stem cells’ extracellular vesicle protein content cultured on biomaterials doped or not with metallic ions

Cut 3D, Slice 3D, Edit 3D

This software kit is a collection of computer programs to provide practical tools for limb volume calculation. Limb could be acquired using augmented reality devices (for example, 3D laser-scanner or perometer). In addition to editing features, these programs allow researchers to estimate perimeter and area of limb sections. The project was originally conceived for BCRL quantification (breast cancer-related lymphedema), but not restricted to this application

Visually Enhanced Python Functions for Clinical Equality of Measurement Assessment

Equivalence testing requires specific procedures usually provided by specialized statistical software. The proposed package includes customized methods to assess biomedical equivalence and focuses on translating the outcomes into visual reports. The functions are coded in an object-oriented framework, contain improved plots or novel graphs to facilitate interpretation of the results, and are accompanied by console textual outputs to support users with additional explanations. Special attention has been devoted to verifying the preliminary assumptions of the statistical tests with automatic routines. The current module covers four aspects of biomedical statistics (equivalence, Bland--Altman and ROC analyses, effect size, and confidence intervals interpretation), offering these methodologies to the biomedical community as accessible stand-alone functions. The manuscript defines software's functions and innovations with examples and theoretical explanations

A Minimal Setup for Spontaneous Smile Quantification Applicable for Valence Detection

Tracking emotional responses as they unfold has been one of the hallmarks of applied neuroscience and related disciplines, but recent studies suggest that automatic tracking of facial expressions have low validation. In this study, we focused on the direct measurement of facial muscles involved in expressions such as smiling. We used single-channel surface electromyography (sEMG) to evaluate the muscular activity from the Zygomaticus Major face muscle while participants watched music videos. Participants were then tasked with rating each video with regard to their thoughts and responses to each of them, including their judgment of emotional tone ("Valence"), personal preference ("Liking") and rating of whether the video displayed strength and impression ("Dominance"). Using a minimal recording setup, we employed three ways to characterize muscular activity associated with spontaneous smiles. The total time spent smiling (ZygoNum), the average duration of smiles (ZygoLen), and instances of high valence (ZygoTrace). Our results demonstrate that Valence was the emotional dimension that was most related to the Zygomaticus activity. Here, the ZygoNum had higher discriminatory power than ZygoLen for Valence quantification. An additional investigation using fractal properties of sEMG time series confirmed previous studies of the Facial Action Coding System (FACS) documenting a smoother contraction of facial muscles for enjoyment smiles. Further analysis using ZygoTrace responses over time to the video events discerned "high valence" stimuli with a 76% accuracy. Additional validation of this approach came against previous findings on valence detection using features derived from a single channel EEG setup. We discuss these results in light of both the recent replication problems of facial expression measures, and in relation to the need for methods to reliably assess emotional responses in more challenging conditions, such as Virtual Reality, in which facial expressions are often covered by the equipment used

equiv_med

The repository contains Python functions to produce novel graphs for biomedical biosimilarity testing. Visualization enhances the interpretation and storytelling of statistical tests. Each function automatically checks the preliminary assumptions of the tests and is paired with console outputs. The scripts were subdivided into four macro-areas following the scheme in the figure. Users interested in running bio-similarity analysis can download the code and follow the instructions in the referenced manuscript or try the minimal working examples

Algorithm-Based Risk Identification in Patients with Breast Cancer-Related Lymphedema: A Cross-Sectional Study

Background: Breast cancer-related lymphedema (BCRL) could be one consequence of breast cancer (BC). Although several risk factors have been identified, a predictive algorithm still needs to be made available to determine the patient’s risk from an ensemble of clinical variables. Therefore, this study aimed to characterize the risk of BCRL by investigating the characteristics of autogenerated clusters of patients. Methods: The dataset under analysis was a multi-centric data collection of twenty-three clinical features from patients undergoing axillary dissection for BC and presenting BCRL or not. The patients’ variables were initially analyzed separately in two low-dimensional embeddings. Afterward, the two models were merged in a bi-dimensional prognostic map, with patients categorized into three clusters using a Gaussian mixture model. Results: The prognostic map represented the medical records of 294 women (mean age: 59.823±12.879 years) grouped into three clusters with a different proportion of subjects affected by BCRL (probability that a patient with BCRL belonged to Cluster A: 5.71%; Cluster B: 71.42%; Cluster C: 22.86%). The investigation evaluated intra- and inter-cluster factors and identified a subset of clinical variables meaningful in determining cluster membership and significantly associated with BCRL biological hazard. Conclusions: The results of this study provide potential insight for precise risk assessment of patients affected by BCRL, with implications in prevention strategies, for instance, focusing the resources on identifying patients at higher risk

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework

Spiking neural networks are biologically inspired machine learning algorithms attracting researchers’ attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural networks have been tested in a quantitative structure–activity analysis targeting the toxicity of molecules. Multiple public-domain databases of compounds have been evaluated with spiking neural networks, achieving accuracies compatible with high-quality frameworks presented in the previous literature. The numerical experiments also included an analysis of hyperparameters and tested the spiking neural networks on molecular fingerprints of different lengths. Proposing alternatives to traditional software and hardware for time- and resource-consuming tasks, such as those found in chemoinformatics, may open the door to new research and improvements in the field

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework

Spiking neural networks are biologically inspired machine learning algorithms attracting researchers’ attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural networks have been tested in a quantitative structure–activity analysis targeting the toxicity of molecules. Multiple public-domain databases of compounds have been evaluated with spiking neural networks, achieving accuracies compatible with high-quality frameworks presented in the previous literature. The numerical experiments also included an analysis of hyperparameters and tested the spiking neural networks on molecular fingerprints of different lengths. Proposing alternatives to traditional software and hardware for time- and resource-consuming tasks, such as those found in chemoinformatics, may open the door to new research and improvements in the field