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Abstract Background Professional voice users often experience stigma associated with voice disorders and are reluctant to seek medical help. This study deployed empirical and computational tools to (1) quantify the experience of vocal stigma and help-seeking behaviors in performers; and (2) predict their modulations with peer influences in social networks. Methods Experience of vocal stigma and information-motivation-behavioral (IMB) skills were prospectively profiled using online surveys from a total of 403 Canadians (200 singers and actors and 203 controls). Data were used to formulate an agent-based network model of social interactions on vocal stigma (self-stigma and social-stigma) and help-seeking behaviors. Network analysis was performed to evaluate the effect of social network structure on the flow of IMB among virtual agents. Results Larger social networks are more likely to contribute to an increase in vocal stigma. For small social networks, total stigma is reduced with higher total IMB but not much so for large networks. For agents with high social-stigma and risk for voice disorder, their vocal stigma is resistant to large changes in IMB ( > 2 standard deviations). Agents with extreme IMB and stigma values are likely to polarize their networks faster in larger social groups. Conclusions We integrated empirical surveys and computational techniques to contextualize vocal stigma and IMB beyond theory and to quantify the interaction among stigma, health-seeking behavior and influence of social interactions. This work establishes an effective, predictable experimental platform to provide scientific evidence in developing interventions to reduce health stigma in voice disorders and other medical conditions.

Abstract Background Cystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways. Methods We propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes. Results Based on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotypes were found to be consistent with CF pathology. Conclusion Analysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.

Abstract Background Canada’s immigrants and refugees have often settled in large Canadian cities, but this is changing with rising costs of living and rural settlement initiatives. However, little consideration is made regarding systemic changes needed to accommodate this distribution, particularly in healthcare in medium-sized cities or smaller communities. For most Canadians, primary care is an entry point into the healthcare system but immigrants and refugees face unique barriers to accessing care compared to the general Canadian population. This project aimed to better understand the barriers to accessing primary care among newcomers in Peterborough, Ontario from the perspective of newcomer service providers. Methodology Participants were recruited from community organizations identified by the local settlement agency, the New Canadians Centre, as having regular interactions with newcomer clients including clinics, not-for-profit organizations, and volunteer groups. Four focus groups were completed, each with three participants (n=12). A coding grid was deductively developed to guide thematic analysis by adapting Levesque et al.’s conceptual framework defining access to healthcare with five specific dimensions: approachability, acceptability, availability and accommodation, affordability, and appropriateness. Results Participants identified lack of awareness of the healthcare system, stigma, competing priorities, and direct costs as some of the barriers for newcomers. Participants highlighted barriers unique to Peterborough including proximity to services, social isolation, and a shortage of family physicians. The results also highlighted strengths in the community such as its maternal-child health programming. Conclusion The results provide a glimpse of the challenges to accessing primary care among newcomers in medium-sized communities and identify opportunities to prepare for changing settlement patterns.

The 2019 Coronavirus (COVID-19) pandemic severely limited the availability of community resources within the Regional Municipality of Durham, Ontario, Canada. It disrupted the lives of persons experiencing homelessness and other vulnerable populations. To address the gaps in resources, community stakeholders developed two pilot community hubs to respond to the unmet health, housing, and support needs of those impacted. This research utilized a mixed-methods research design to determine the effectiveness of the community hubs in responding to the unmet needs of patrons utilizing the services and the scalability of the community hub model as a viable regional service approach. Surveys were administered in person with seventy-five community hub patrons. Fifteen direct service staff completed self-administered online surveys. Interviews were conducted with five community hub managerial staff and two subject-matter experts who collaborated with one of the community hubs. Results from the study showed that the needs of patrons were deep and entrenched and required a progressive, co-located, integrated health and social service response model. Staff described the services as critical and lifesaving for the patrons. The descriptive statistical analysis revealed that 93% of patrons indicated that services offered by the community hubs met their needs. The main challenge for the community hubs was the lack of core funding. Implications of this study include establishing a regional, evidence-informed, integrated system of care that addresses the healthcare, social service, and housing needs of populations experiencing homelessness.

The trend of current robotic research is to develop mobile robots that can perform highly dynamic tasks, which include jumping and running. To be employed profitably, this research necessitates a significant amount of work in the development of innovative planning and control algorithms that need experimental validation on actual robots. However, the majority of robots with highly dynamic performance capabilities are currently restricted to a few expensive platforms. This is a major obstacle that ultimately restricts the amount of contributors and the advancement of the research. Thus, a cost-effective actuator solution is needed that is also able to execute very dynamic movements. With this goal in mind, we present EM-Act, a modular series elastic actuator (SEA) for legged and multimodal dynamic robots. This work focuses on the development of the actuator solution by defining jump height as the prerequisite, identifying actuator parameters through simulations, and selecting and testing mechatronic elements of the design. The work also discusses a compact integration of desired compliance to address the impact forces. Furthermore, the work also details the implementation of the actuator solution on a 2-degree-of-freedom (DOF) robotic leg and experimentally validates its jumping performance.

Abstract The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package’s capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.

Abstract Messenger RNA (mRNA) has an essential role in the protein production process. Predicting mRNA expression levels accurately is crucial for understanding gene regulation, and various models (statistical and neural network-based) have been developed for this purpose. A few models predict mRNA expression levels from the DNA sequence, exploiting the DNA sequence and gene features (e.g., number of exons/introns, gene length). Other models include information about long-range interaction molecules (i.e., enhancers/silencers) and transcriptional regulators as predictive features, such as transcription factors (TFs) and small RNAs (e.g., microRNAs - miRNAs). Recently, a convolutional neural network (CNN) model, called Xpresso, has been proposed for mRNA expression level prediction leveraging the promoter sequence and mRNAs’ half-life features (gene features). To push forward the mRNA level prediction, we present miREx, a CNN-based tool that includes information about miRNA targets and expression levels in the model. Indeed, each miRNA can target specific genes, and the model exploits this information to guide the learning process. In detail, not all miRNAs are included, only a selected subset with the highest impact on the model. MiREx has been evaluated on four cancer primary sites from the genomics data commons (GDC) database: lung, kidney, breast, and corpus uteri. Results show that mRNA level prediction benefits from selected miRNA targets and expression information. Future model developments could include other transcriptional regulators or be trained with proteomics data to infer protein levels.

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases.

Overexpression of the TGFβ pathway impairs the proliferation of the hematopoietic stem and progenitor cells (HSPCs) pool in Fanconi anemia (FA). TGFβ promotes the expression of NHEJ genes, known to function in a low-fidelity DNA repair pathway, and pharmacological inhibition of TGFβ signaling rescues FA HSPCs. Here, we demonstrate that genetic disruption of Smad3, a transducer of the canonical TGFβ pathway, modifies the phenotype of FA mouse models deficient for Fancd2. We observed that the TGFβ and NHEJ pathway genes are overexpressed during the embryogenesis of Fancd2-/- mice and that the Fancd2-/-Smad3-/- double knockout (DKO) mice undergo high levels of embryonic lethality due to loss of the TGFβ-NHEJ axis. Fancd2-deficient embryos acquire extensive genomic instability during gestation which is not reversed by Smad3 inactivation. Strikingly, the few DKO survivors have activated the non-canonical TGFβ-ERK pathway, ensuring expression of NHEJ genes during embryogenesis and improved survival. Activation of the TGFβ-NHEJ axis was critical for the survival of the few Fancd2-/-Smad3-/- DKO newborn mice but had detrimental consequences for these surviving mice, such as enhanced genomic instability and ineffective hematopoiesis.

BackgroundNeck surface accelerometer (NSA) wearable devices have been developed for voice and upper airway health monitoring. As opposed to acoustic sounds, NSA senses mechanical vibrations propagated from the vocal tract to neck skin, which are indicative of a person’s voice and airway conditions. NSA signals do not carry identifiable speech information and a speaker’s privacy is thus protected, which is important and necessary for continuous wearable monitoring. Our device was already tested for its durable endurance and signal processing algorithms in controlled laboratory conditions. ObjectiveThis study aims to further evaluate both instrument and analysis validity in a group of occupational vocal users, namely, voice actors, who use their voices extensively at work in an ecologically valid setting. MethodsA total of 16 professional voice actors (age range 21-50 years; 11 females and 5 males) participated in this study. All participants were mounted with an NSA on their sternal notches during the voice acting and voice assessment sessions. The voice acting session was 4-hour long, directed by a voice director in a professional sound studio. Voice assessment sessions were conducted before, during, and 48 hours after the acting session. The assessment included phonation tasks of passage reading, sustained vowels, maximum vowel phonation, and pitch glides. Clinical acoustic metrics (eg, fundamental frequency, cepstral measures) and a vocal dose measure (ie, accumulated distance dose from acting) were computed from NSA signals. A commonly used online questionnaire (Self-Administered Voice Rating questionnaire) was also implemented to track participants’ perception of vocal fatigue. ResultsThe NSA wearables stayed in place for all participants despite active body movements during the acting. The ensued body noise did not interfere with the NSA signal quality. All planned acoustic metrics were successfully derived from NSA signals and their numerical values were comparable with literature data. For a 4-hour long voice acting, the averaged distance dose was about 8354 m with no gender differences. Participants perceived vocal fatigue as early as 2 hours after the start of voice acting, with recovery 24-48 hours after the acting session. Among all acoustic metrics across phonation tasks, cepstral peak prominence and spectral tilt from the passage reading most closely mirrored trends in perceived fatigue. ConclusionsThe ecological validity of an in-house NSA wearable was vetted in a workplace setting. One key application of this wearable is to prompt occupational voice users when their vocal safety limits are reached for duly protection. Signal processing algorithms can thus be further developed for near real-time estimation of clinically relevant metrics, such as accumulated distance dose, cepstral peak prominence, and spectral tilt. This functionality will enable continuous self-awareness of vocal behavior and protection of vocal safety in occupational voice users.

Mobile health wearables are often embedded with small processors for signal acquisition and analysis. These embedded wearable systems are, however, limited with low available memory and computational power. Advances in machine learning, especially deep neural networks (DNNs), have been adopted for efficient and intelligent applications to overcome constrained computational environments. Herein, evolutionary algorithms are used to find novel DNNs that are accurate in classifying airway symptoms while allowing wearable deployment. As opposed to typical microphone‐acoustic signals, mechano‐acoustic data signals, which did not contain identifiable speech information for better privacy protection, are acquired from laboratory‐generated and publicly available datasets. The optimized DNNs had a low model file size of less than 150 kB and predicted airway symptoms of interest with 81.49% accuracy on unseen data. By performing explainable AI techniques, namely occlusion experiments and class activation maps, mel‐frequency bands up to 8,000 Hz are found as the most important feature for the classification. It is further found that DNN decisions are consistently relying on these specific features, fostering trust and transparency of the proposed DNNs. The proposed efficient and explainable DNN is expected to support edge computing on mechano‐acoustic sensing wearables for remote, long‐term monitoring of airway symptoms.