Machine learning based DNA melt curve profiling enables automated novel genotype detection.

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

'I don't want to have to teach every medical provider': barriers to care among non-binary people in the Canadian healthcare system.

It is well-known that trans and non-binary individuals experience worse health outcomes due to experiences of violence and discrimination. For this reason, accessible healthcare for trans and non-binary people is crucial. There is a lack of Canadian literature on the experiences of non-binary people within the healthcare system. This study sought to understand barriers to healthcare among non-binary people living in a mid-sized urban/rural region of Canada. Interviews were conducted between November 2019 to March 2020 with 12 non-binary individuals assigned female at birth, living in Waterloo Region, Ontario, Canada, as a part of a larger qualitative study exploring experiences within the community, healthcare and employment. Three broad themes were developed: erasure, barriers to access to healthcare, and assessing whether (or not) to come out. Sub-themes included institutional erasure, informational erasure, general healthcare barriers, medical transition healthcare barriers, anticipated discrimination, and assessing safety. Policy and institutional changes are needed to increase the safety and accessibility of healthcare services to non-binary individuals.

Exploring LGBT2Q+ Intracategorical Factors in Mental Health Service Utilization: Differences in Gender Modalities, Sexual Orientations, and Ethnoracial Groups in Canada.

LGBT2Q+ (lesbian, gay, bisexual, transgender, Two-Spirit, queer, plus) Canadians face minority stressors that lead to higher mental health inequalities such as worse self-reported mental health and increased risk of mental health issues when compared to their heterosexual/straight and cisgender counterparts. However, there are within-group (intracategorical) differences within a community as large as LGBT2Q+ peoples. Guided by the Andersen Model of Healthcare Utilization, we sought to explore intracategorical differences in LGBT2Q+ Canadian predisposing, enabling, and need factors in mental health service utilization within the past year. Using data from the 2020 LGBT2Q+ Health Survey (N = 1542), modified Poisson logistic regression found that more polysexual respondents and trans/gender-diverse respondents were more likely to have utilized mental health services within the past year than their gay, lesbian, and cis male counterparts. As well, compared to White respondents, Indigenous respondents were more likely to have utilized mental health services, while other racialized respondents were associated with less utilization. Backwards elimination of Andersen model of healthcare utilization factors predicting mental health service utilization retained two predisposing factors (ethnoracial groups and gender modality) and two need factors (self-reporting living with a mood disorder and self-reporting living with an anxiety disorder). Results suggest that polysexual, trans and gender-diverse, and racialized LGBT2Q+ peoples have an increased need for mental health services due to increased specific minority stressors that cisgender, White, monosexual peoples do not face. Implications for healthcare providers are discussed on how to improve service provision to LGBT2Q+ peoples.

Exploring community enabling factors associated with recent HIV testing in a regional sample of gay, bisexual, and other men who have sex with men.

HIV testing and diagnosis are the gateway into treatment and eventual viral suppression. With gay, bisexual, and other men who have sex with men (GBMSM) persistently over-representing new HIV diagnoses in Canada, combined with the evolving nature of community social connection, an exploration of factors associated with recent HIV testing is warranted. As most studies of GBMSM rely on samples obtained from larger metropolitan regions, examining HIV testing from an under-researched region is necessary. With data collected from an online survey of LGBTQ+ persons 16 or older living, working, or residing in the Region of Waterloo, Ontario, Canada, we used multinomial logistic regression to explore socio-demographic, behavioural, and psychosocial factors associated with recent HIV testing for GBMSM. In the final multivariate multinomial logistic regression model: sense of belonging was associated with more recently testing, as was having an increasing proportion of LGBT friends, app use to find sex partners in the past 12 months, access to the local AIDS service organization, and general sense of belonging to local community, among other. This analysis highlights the continued importance of enabling and need factors when accessing testing, and suggests areas for further testing promotion in physical and virtual spaces frequented by GBMSM.

Modeling relationships between rhythmic processes and neuronal spike timing.

Neurons are embedded in complex networks, where they participate in repetitive, coordinated interactions with other neurons. Neuronal spike timing is thus predictably constrained by a range of ionic currents that shape activity at both short (milliseconds) and longer (tens to hundreds of milliseconds) timescales, but we lack analytical tools to rigorously identify these relationships. Here, we innovate a modeling approach to test the relationship between oscillations in the local field potential (LFP) and neuronal spike timing. We use kernel density estimation to relate single neuron spike timing and the phase of LFP rhythms (in simulated and hippocampal CA1 neuronal spike trains). We then combine phase and short (3 ms) spike history information within a logistic regression framework ("phaseSH models"), and show that models that leverage refractory constraints and oscillatory phase information can effectively test whether-and the degree to which-rhythmic currents (as measured from the LFP) reliably explain variance in neuronal spike trains. This approach allows researchers to systematically test the relationship between oscillatory activity and neuronal spiking dynamics as they unfold over time and as they shift to adapt to distinct behavioral conditions.NEW & NOTEWORTHY Statistical models that incorporate neural spiking history and relationships to the phase of ongoing oscillations in the local field potential robustly capture and predict neuronal engagement in rhythmic processes. These models constitute a powerful tool to systematically test explicit hypotheses regarding the specific rhythmic currents that constrain neural spiking activity over time and during different behaviors.

Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power.

MOTIVATION: The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers. RESULTS: This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution. AVAILABILITY AND IMPLEMENTATION: Data and code available on https://github.com/lenlan/dHRM-noise-modeling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Experiences of trans patients in primary care settings: findings from The OutLook Study.

BACKGROUND: Relationships between primary care providers (PCP) and trans patients remain important, necessitating discussions about gender identity, health and their intersections. METHODS: Using an online survey, we explored socio-demographic and psycho-social factors associated with: (1) disclosing gender identity; (2) discussing gender identity-related health issues; and (3) comfort sharing gender identity with PCPs, among trans people (n =112) over 16years of age, sampled in Waterloo, Ontario, Canada. Bivariate and multivariate methods using modified Poisson regression generated effect estimates. RESULTS: Age, birth presumed gender, employment status, family support, and transphobia were significantly associated with disclosing gender identity, discussing gender identity-related health issues, and comfortability sharing gender identity with PCPs. CONCLUSION: Increasing PCPs' knowledge of trans-related health issues is stressed to improve access and quality for trans patients.

Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling.

Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors.

Wireless Heart Sensor for Capturing Cardiac Orienting Response for Prediction of Neurodevelopmental Delay in Infants.

Early identification of infants at risk of neurodevelopmental delay is an essential public health aim. Such a diagnosis allows early interventions for infants that maximally take advantage of the neural plasticity in the developing brain. Using standardized physiological developmental tests, such as the assessment of neurophysiological response to environmental events using cardiac orienting responses (CORs), is a promising and effective approach for early recognition of neurodevelopmental delay. Previous CORs have been collected on children using large bulky equipment that would not be feasible for widespread screening in routine clinical visits. We developed a portable wireless electrocardiogram (ECG) system along with a custom application for IOS tablets that, in tandem, can extract CORs with sufficient physiologic and timing accuracy to reflect the well-characterized ECG response to both auditory and visual stimuli. The sensor described here serves as an initial step in determining the extent to which COR tools are cost-effective for the early screening of children to determine who is at risk of developing neurocognitive deficits and may benefit from early interventions. We demonstrated that our approach, based on a wireless heartbeat sensor system and a custom mobile application for stimulus display and data recording, is sufficient to capture CORs from infants. The COR monitoring approach described here with mobile technology is an example of a desired standardized physiologic assessment that is a cost-and-time efficient, scalable method for early recognition of neurodevelopmental delay.

Smart Electronic Eyedrop Bottle for Unobtrusive Monitoring of Glaucoma Medication Adherence.

Glaucoma, the leading cause of irreversible blindness, affects >70 million people worldwide. Lowering intraocular pressure via topical administration of eye drops is the most common first-line therapy for glaucoma. This treatment paradigm has notoriously high non-adherence rates: ranging from 30% to 80%. The advent of smart phone enabled technologies creates promise for improving eyedrop adherence. However, previous eyedrop electronic monitoring solutions had awkward medication bottle adjuncts and crude software for monitoring the administration of a drop that adversely affected their ability to foster sustainable improvements in adherence. The current work begins to address this unmet need for wireless technology by creating a "smart drop" bottle. This medication bottle is instrumented with sensing electronics that enable detection of each eyedrop administered while maintaining the shape and size of the bottle. This is achieved by a thin electronic force sensor wrapped around the bottle and underneath the label, interfaced with a thin electronic circuit underneath the bottle that allows for detection and wireless transmission to a smart-phone application. We demonstrate 100% success rate of wireless communication over 75 feet with <1% false positive and false negative rates of single drop deliveries, thus providing a viable solution for eyedrop monitoring for glaucoma patients.

Emerging Technologies for Molecular Diagnosis of Sepsis.

Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.

Direct and Indirect Effects-An Information Theoretic Perspective.

Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g., neuroscience and climate science) domains. While these causal measures are desirable in that they are model agnostic and can capture non-linear interactions, they are fundamentally different from common statistical notions of causal influence in that they (1) compare distributions over the effect rather than values of the effect and (2) are defined with respect to random variables representing a cause rather than specific values of a cause. We here present IT measures of direct, indirect, and total causal effects. The proposed measures are unlike existing IT techniques in that they enable measuring causal effects that are defined with respect to specific values of a cause while still offering the flexibility and general applicability of IT techniques. We provide an identifiability result and demonstrate application of the proposed measures in estimating the causal effect of the El Niño-Southern Oscillation on temperature anomalies in the North American Pacific Northwest.

Spatial Patterns From High-Resolution Electrogastrography Correlate With Severity of Symptoms in Patients With Functional Dyspepsia and Gastroparesis.

BACKGROUND & AIMS: Invasive gastric electrical mapping has revealed spatial abnormalities of the slow wave in subjects with gastroparesis and functional gastrointestinal disorders. Cutaneous high-resolution electrogastrography (HR-EGG) is a non-invasive method that can detect spatial features of the gastric slow wave. We performed HR-EGG in subjects with active foregut symptoms to evaluate associations between gastric myoelectric abnormalities, symptoms (based on a validated questionnaire), and gastric emptying. METHODS: We performed a case-control study of 32 subjects, including 7 healthy individuals (controls), 7 subjects with functional dyspepsia and normal gastric emptying, and 18 subjects with gastroparesis, from a tertiary care program. All subjects were assessed by computed tomography imaging of the abdomen and HR-EGG and completed the PAGI-SYM questionnaire on foregut symptoms, which includes the gastroparesis cardinal symptom index. We performed volume reconstruction of the torso and stomach from computed tomography images to guide accurate placement of the HR-EGG array. RESULTS: Spatial slow-wave abnormalities were detected in 44% of subjects with foregut symptoms. Moreover, subjects with a higher percentage of slow waves with aberrant propagation direction had a higher total gastroparesis cardinal symptom index score (r = 0.56; P < .001) and more severe abdominal pain (r = 0.46; P = .009). We found no correlation between symptoms and traditional EGG parameters. CONCLUSIONS: In case-control study, we found that the genesis of symptoms of functional dyspepsia and gastroparesis is likely multifactorial, including possible contribution from gastric myoelectric dysfunction. Abnormal spatial parameters, detected by cutaneous HR-EGG, correlated with severity of upper gastrointestinal symptoms, regardless of gastric emptying. This noninvasive, repeatable approach might be used to identify patients for whom gastric myoelectric dysfunction contributes to functional dyspepsia and gastroparesis.

A Distributed Framework for the Construction of Transport Maps.

The need to reason about uncertainty in large, complex, and multimodal data sets has become increasingly common across modern scientific environments. The ability to transform samples from one distribution P to another distribution Q enables the solution to many problems in machine learning (e.g., Bayesian inference, generative modeling) and has been actively pursued from theoretical, computational, and application perspectives across the fields of information theory, computer science, and biology. Performing such transformations in general still leads to computational difficulties, especially in high dimensions. Here, we consider the problem of computing such "measure transport maps" with efficient and parallelizable methods. Under the mild assumptions that P need not be known but can be sampled from and that the density of Q is known up to a proportionality constant, and that Q is log-concave, we provide in this work a convex optimization problem pertaining to relative entropy minimization. We show how an empirical minimization formulation and polynomial chaos map parameterization can allow for learning a transport map between P and Q with distributed and scalable methods. We also leverage findings from nonequilibrium thermodynamics to represent the transport map as a composition of simpler maps, each of which is learned sequentially with a transport cost regularized version of the aforementioned problem formulation. We provide examples of our framework within the context of Bayesian inference for the Boston housing data set and generative modeling for handwritten digit images from the MNIST data set.

The activity of discrete sets of neurons in the posterior insula correlates with the behavioral expression and extinction of conditioned fear.

The interoceptive insular cortex is known to be involved in the perception of bodily states and emotions. Increasing evidence points to an additional role for the insula in the storage of fear memories. However, the activity of the insula during fear expression has not been studied. We addressed this issue by recording single units from the posterior insular cortex (pIC) of awake behaving rats expressing conditioned fear during its extinction. We found a set of pIC units showing either significant increase or decrease in activity during high fear expression to the auditory cue ("freezing units"). Firing rate of freezing units showed high correlation with freezing and outlasted the duration of the auditory cue. In turn, a different set of units showed either significant increase or decrease in activity during low fear state ("extinction units"). These findings show that expression of conditioned freezing is accompanied with changes in pIC neural activity and suggest that the pIC is important to regulate the behavioral expression of fear memory. NEW & NOTEWORTHY Here, we show novel single-unit data from the interoceptive insula underlying the behavioral expression of fear. We show that different populations of neurons in the insula codify expression and extinction of conditioned fear. Our data add further support for the insula as an important player in the regulation of emotions.

The Use of Cardiac Orienting Responses as an Early and Scalable Biomarker of Alcohol-Related Neurodevelopmental Impairment.

BACKGROUND: Considered the leading cause of developmental disabilities worldwide, fetal alcohol spectrum disorders (FASD) are a global health problem. To take advantage of neural plasticity, early identification of affected infants is critical. The cardiac orienting response (COR) has been shown to be sensitive to the effects of prenatal alcohol exposure and is an inexpensive, easy to administer assessment tool. The purpose of this study was to evaluate the COR effectiveness in assessing individual risk of developmental delay. METHODS: As part of an ongoing longitudinal cohort study in Ukraine, live-born infants of women with some to heavy amounts of alcohol consumption in pregnancy were recruited and compared to infants of women who consumed low or no alcohol. At 6 and 12 months, infants were evaluated with the Bayley Scales of Infant Development-II. CORs were also collected during a habituation/dishabituation learning paradigm. Using a supervised logistic regression classifier, we compared the predictive utility of the COR indices to that of the 6-month Bayley scores for identification of developmental delay based on 12-month Bayley scores. Heart rate collected at each second (Standard COR) was compared to key features (Key COR) extracted from the response. RESULTS: Negative predictive values (NPV) were 85% for Standard COR, 82% for Key COR, and 77% for the Bayley, and positive predictive values (PPV) were 66% for Standard COR, 62% for Key COR, and 43% for the Bayley. CONCLUSIONS: Predictive analysis based on the COR resulted in better NPV and PPV than the 6-month Bayley score. As the resources required to obtain a Bayley score are substantially more than in a COR-based paradigm, the findings are suggestive of its utility as an early scalable screening tool based on the COR. Further work is needed to test its long-term predictive accuracy.

HIV-related syndemic pathways and risk subjectivities among gay and bisexual men: a qualitative investigation.

Life history interviews were conducted with 40 gay and bisexual men to identify modes of syndemic experience and risk practice. Out of the interview narratives emerged one major and two minor modes of developmental pathway whereby syndemic conditions are navigated and expressed: (1) a combination of adverse childhood events with later episodes of depression and/or substantial substance use; (2) personal disruption that led to periods of depression and anxiety associated with the stresses of migration; and (3) a disorientation and an unravelling of life trajectory in the transition from family of origin to college or work. Risk practices fell into three high-risk modes: active and frequent engagement in condomless sex; unassertive deferment to a partner's initiation of condomless sex; and episodic risk combined with a risk reduction strategy. Three low risk modes were also identified: no recent condomlessness but multiple risk history in interview; a trajectory over time from high to low risk; and consistent low risk practice. These different modes of syndemic experience and risk management may have implications for identification of the effective HIV prevention tools that work best for different sets of men.

Learning Minimal Latent Directed Information Polytrees.

We propose an approach for learning latent directed polytrees as long as there exists an appropriately defined discrepancy measure between the observed nodes. Specifically, we use our approach for learning directed information polytrees where samples are available from only a subset of processes. Directed information trees are a new type of probabilistic graphical models that represent the causal dynamics among a set of random processes in a stochastic system. We prove that the approach is consistent for learning minimal latent directed trees. We analyze the sample complexity of the learning task when the empirical estimator of mutual information is used as the discrepancy measure.