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.

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.

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.

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.

Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors.

New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach.

A mutual information measure of phase-amplitude coupling using gamma generalized linear models.

Introduction: Cross frequency coupling (CFC) between electrophysiological signals in the brain is a long-studied phenomenon and its abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling, specifically phase-amplitude coupling (PAC), do not attempt to capture the phase and amplitude statistical relationships. Methods: In this paper, we first demonstrate a method of modeling these joint statistics with a flexible parametric approach, where we model the conditional distribution of amplitude given phase using a gamma distributed generalized linear model (GLM) with a Fourier basis of regressors. We perform model selection with minimum description length (MDL) principle, demonstrate a method for assessing goodness-of-fit (GOF), and showcase the efficacy of this approach in multiple electroencephalography (EEG) datasets. Secondly, we showcase how we can utilize the mutual information, which operates on the joint distribution, as a canonical measure of coupling, as it is non-zero and non-negative if and only if the phase and amplitude are not statistically independent. In addition, we build off of previous work by Martinez-Cancino et al., and Voytek et al., and show that the information density, evaluated using our method along the given sample path, is a promising measure of time-resolved PAC. Results: Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase-amplitude coupling through receiver operating characteristic (ROC) curve analysis. To validate our method, we test on invasive EEG recordings by generating comodulograms, and compare our method to the gold standard PAC measure, Modulation Index, demonstrating comparable performance in exploratory analysis. Furthermore, to showcase its use in joint gut-brain electrophysiology data, we generate topoplots of simultaneous high-density EEG and electrgastrography recordings and reproduce seminal work by Richter et al. that demonstrated the existence of gut-brain PAC. Using simulated data, we validate our method for different types of time-varying coupling and then demonstrate its performance to track time-varying PAC in sleep spindle EEG and mismatch negativity (MMN) datasets. Conclusions: Our new measure of PAC using Gamma GLMs and mutual information demonstrates a promising new way to compute PAC values using the full joint distribution on amplitude and phase. Our measure outperforms the most common existing measures of PAC, and show promising results in identifying time varying PAC in electrophysiological datasets. In addition, we provide for using our method with multiple comparisons and show that our measure potentially has more statistical power in electrophysiologic recordings using simultaneous gut-brain datasets.

Emerging wearable technologies for multisystem monitoring and treatment of Parkinson's disease: a narrative review.

Parkinson's disease (PD) is a chronic movement disorder characterized by a variety of motor and nonmotor comorbidities, including cognitive impairment, gastrointestinal (GI) dysfunction, and autonomic/sleep disturbances. Symptoms typically fluctuate with different settings and environmental factors and thus need to be consistently monitored. Current methods, however, rely on infrequent rating scales performed in clinic. The advent of wearable technologies presents a new avenue to track objective measures of PD comorbidities longitudinally and more frequently. This narrative review discusses and proposes emerging wearable technologies that can monitor manifestations of motor, cognitive, GI, and autonomic/sleep comorbidities throughout the daily lives of PD individuals. This can provide more wholistic insight into real-time physiological versus pathological function with the potential to better assess treatments during clinical trials and allow physicians to optimize treatment regimens. Additionally, this narrative review briefly examines novel applications of wearables as therapy for PD patients.

Exploring the aperiodic nature of parasympathetic activity during sleep in idiopathic gastroparesis.

The parasympathetic nervous system is necessary to regulate both sleep and digestion. Investigating abnormalities during the controlled setting of sleep can shed light on digestion, specifically for patients with idiopathic gastroparesis. In this study, we specifically investigate heartbeat-derived parasympathetic activity during sleep at very low frequencies, relevant to sleep cycle regulation. To do this, we adapt a method that extracts both periodic and aperiodic information from the power spectral density and recognize that the aperiodic activity may contain information relevant to very low frequencies. After testing on both synthetic noise data (pink and white) and overnight data from seven healthy controls and idiopathic gastroparetics, we find that the healthy controls' low-frequency aperiodic activity reflects pink noise structure, while the majority of the patients' aperiodic activity reflects white noise structure. At these low frequencies, these differences suggest differences in autonomic sleep cycle regulation.Clinical Relevance- This methodology can be optimized to track the health of the parasympathetic nervous system and suggest whether individual disease etiology is autonomic-related.

Exploring the Gut-Brain Connection in Gastroparesis With Autonomic and Gastric Myoelectric Monitoring.

OBJECTIVE: The goal of this study was to identify autonomic and gastric myoelectric biomarkers from throughout the day that differentiate patients with gastroparesis, diabetics without gastroparesis, and healthy controls, while providing insight into etiology. METHODS: We collected 19 24-hour recordings of electrocardiogram (ECG) and electrogastrogram (EGG) data from healthy controls and patients with diabetic or idiopathic gastroparesis. We used physiologically and statistically rigorous models to extract autonomic and gastric myoelectric information from the ECG and EGG data, respectively. From these, we constructed quantitative indices which differentiated the distinct groups and demonstrated their application in automatic classification paradigms and as quantitative summary scores. RESULTS: We identified several differentiators that separate healthy controls from gastroparetic patient groups, specifically around sleep and meals. We also demonstrated the downstream utility of these differentiators in automatic classification and quantitative scoring paradigms. Even with this small pilot dataset, automated classifiers achieved an accuracy of 79% separating autonomic phenotypes and 65% separating gastrointestinal phenotypes. We also achieved 89% accuracy separating controls from gastroparetic patients in general and 90% accuracy separating diabetics with and without gastroparesis. These differentiators also suggested varying etiologies for different phenotypes. CONCLUSION: The differentiators we identified were able to successfully distinguish between several autonomic and gastrointestinal (GI) phenotypes using data collected while at-home with non-invasive sensors. SIGNIFICANCE: Autonomic and gastric myoelectric differentiators, obtained using at-home recording of fully non-invasive signals, can be the first step towards dynamic quantitative markers to track severity, disease progression, and treatment response for combined autonomic and GI phenotypes.

Spontaneous enteric nervous system activity precedes maturation of gastrointestinal motility.

Spontaneous neuronal network activity is essential in development of central and peripheral circuits, yet whether this is a feature of enteric nervous system development has yet to be established. Using ex vivo gastrointestinal (GI) motility assays with unbiased computational analyses, we identify a previously unknown pattern of spontaneous neurogenic GI motility. We further show that this motility is driven by cholinergic signaling, which may inform GI pharmacology for preterm patients.

Differentiation of Bolus Texture During Deglutition via High-Density Surface Electromyography: A Pilot Study.

OBJECTIVE: Swallowing is a complex neuromuscular task. There is limited spatiotemporal data on normative surface electromyographic signal during swallow, particularly across standard textures. We hypothesize the pattern of electromyographic signal of the anterior neck varies cranio-caudally, that laterality can be evaluated, and categorization of bolus texture can be differentiated by high-density surface electromyography (HDsEMG) through signal analysis. METHODS: An HDsEMG grid of 20 electrodes captured electromyographic activity in eight healthy adult subjects across 240 total swallows. Participants swallowed five standard textures: saliva, thin liquid, puree, mixed consistency, and dry solid. Data were bandpass filtered, underwent functional alignment of signal, and then placed into binary classifier receiver operating characteristic (ROC) curves. Muscular activity was visualized by creating two-dimensional EMG heat maps. RESULTS: Signal analysis results demonstrated a positive correlation between signal amplitude and bolus texture. Greater differences of amplitude in the cranial most region of the array when compared to the caudal most region were noted in all subjects. Lateral comparison of the array revealed symmetric power levels across all subjects and textures. ROC curves demonstrated the ability to correctly classify textures within subjects in 6 of 10 texture comparisons. CONCLUSION: This pilot study suggests that utilizing HDsEMG during deglutition can noninvasively differentiate swallows of varying texture noninvasively. This may prove useful in future diagnostic and behavioral swallow applications. LEVEL OF EVIDENCE: 4 Laryngoscope, 133:2695-2703, 2023.

Automated classification of sleep and wake from single day triaxial accelerometer data.

Actigraphy allows for the remote monitoring of subjects' activity for clinical and research purposes. However, most standard methods are built for proprietary measures from specific devices that are not widely used. In this study, we develop an algorithm for classifying sleep and awake using a single day of triaxial accelerometer data, which can be acquired from all smart devices. This algorithm consists of two stages, clustering and hidden Markov modeling, and outperforms standard algorithms in sensitivity (94%), specificity (93 %), and overall accuracy (93%) across seven subjects. This method can help automate actigraphy analyses at scale using widely available technology using even a single day's worth of data. Clinical Relevance- Automated monitoring of patients' activity at home can help track recovery trajectories after surgery and injury, disease progression, treatment response.

A Mutual Information Measure of Phase-Amplitude Coupling using High Dimensional Sparse Models.

Cross frequency coupling (CFC) between electrophysiological signals in the brain has been observed and it's abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling do not attempt to capture the underlying statistical relationships that give rise to this coupling. In this paper, we demonstrate a new method of calculating phase amplitude coupling by estimating the mutual information between phase and amplitude, using a flexible parametric modeling approach. Specifically, we develop an exponential generalized linear model (GLM) to model amplitude given phase, using a high dimensional basis of von-Mises function regressors and l1 regularized model selection. Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase amplitude coupling through receiver operating characteristic (ROC) curve analysis.