Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study.

BACKGROUND: As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants' responses when implementing new survey delivery methods. OBJECTIVE: This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants' reported emotional state. We examine the factors that affect participants' receptivity to EMAs in a 10-day wearable and EMA-based emotional state-sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs. METHODS: We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity. RESULTS: Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions. CONCLUSIONS: Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

Modeling the effects of hydration on viscoelastic properties of nucleus pulposus tissue in shear using the fractional Zener model.

Nucleus pulposus (NP) tissue in the intervertebral disc (IVD) is a viscoelastic material exhibiting both solid- and fluid-like mechanical behaviors. Advances in viscoelastic models incorporating fractional calculus, such as the Fractional Zener (FZ) model, have potential to describe viscoelastic behaviors. The objectives of this study were to determine whether the FZ model can accurately describe the shear viscoelastic properties of NP tissue and determine if the fractional order (α) is related to tissue hydration. 30 caudal IVDs underwent equilibrium dialysis in 5% or 25% polyethylene glycol solutions to alter tissue hydration. Excised NP tissue underwent stress relaxation testing in shear and unconfined compression. Stress relaxation data was fitted to the FZ model to obtain viscoelastic properties. In both loading modes, the initial modulus was greater for the less hydrated 25% equilibrated samples compared to 5% with no change in the equilibrium modulus. Samples with lower water content (25% samples) had shorter relaxation times in shear and longer time constants in compression, highlighting the different interactions between the fluid and solid matrix in loading modes. Samples with lower water content had α values closer to 0, indicating that less hydrated samples behaved more solid-like on the viscoelastic spectrum. Tissue hydration correlated with α values for 25% samples in shear. This study demonstrates that the FZ model may be used to describe IVD tissue behavior under both loading modes; however, the greatest utility of the FZ model is in describing flow-independent shear behaviors, and α may inform tissue hydration in shear.

Adult perioperative cardiac arrest: An overview of 684 cases reported to webAIRS.

There were 684 perioperative cardiac arrests reported to webAIRS between September 2009 and March 2022. The majority involved patients older than 60 years, classified as American Society of Anesthesiologists Physical Status 3 to 5, undergoing an emergency or major procedure. The most common precipitants included airway events, cardiovascular events, massive blood loss. medication issues, and sepsis. The highest mortality rate was 54% of the 46 cases in the miscellaneous category (this included 34 cases of severe sepsis, which had a mortality of 65%). This was followed by cardiovascular precipitants (n = 424) in which there were 147 deaths (35% mortality): these precipitants included blood loss (53%), embolism (61%) and myocardial infarction (70%). Airway and breathing events accounted for 25% and anaphylaxis 8%. A specialist anaesthetist attended the majority of these cardiac arrests. As webAIRS is a voluntary database, it is not possible to determine the incidence of perioperative cardiac arrest and only descriptive information on factors associated with cardiac arrest can be obtained. Nevertheless, the large number of reports includes a wide range of cases, precipitants, demographics and outcomes, providing ample opportunity to learn from these events. The data also provide rich scope for further research into further initiatives to prevent cardiac arrest in the perioperative period, and to improve outcomes, should a cardiac arrest occur.

ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data.

Understanding cellular metabolism is important across biotechnology and biomedical research and has critical implications in a broad range of normal and pathological conditions. Here, we introduce the user-friendly web-based platform ImmCellFie, which allows the comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. We explain how to set up a run using publicly available omics data and how to visualize the results. The ImmCellFie algorithm pushes beyond conventional statistical enrichment and incorporates complex biological mechanisms to quantify cell activity. For complete details on the use and execution of this protocol, please refer to Richelle et al. (2021).1.

Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuant® and NeuroGage® in Patients With Traumatic Brain Injury.

Over 40 years of research have shown that traumatic brain injury affects brain volume. However, technical and practical limitations made it difficult to detect brain volume abnormalities in patients suffering from chronic effects of mild or moderate traumatic brain injury. This situation improved in 2006 with the FDA clearance of NeuroQuant®, a commercially available, computer-automated software program for measuring MRI brain volume in human subjects. More recent strides were made with the introduction of NeuroGage®, commercially available software that is based on NeuroQuant® and extends its utility in several ways. Studies using these and similar methods have found that most patients with chronic mild or moderate traumatic brain injury have brain volume abnormalities, and several of these studies found-surprisingly-more abnormal enlargement than atrophy. More generally, 102 peer-reviewed studies have supported the reliability and validity of NeuroQuant® and NeuroGage®. Furthermore, this updated version of a previous review addresses whether NeuroQuant® and NeuroGage® meet the Daubert standard for admissibility in court. It concludes that NeuroQuant® and NeuroGage® meet the Daubert standard based on their reliability, validity, and objectivity. Due to the improvements in technology over the years, these brain volumetric techniques are practical and readily available for clinical or forensic use, and thus they are important tools for detecting signs of brain injury.

Genome-scale metabolic modeling reveals key features of a minimal gene set.

Mesoplasma florum, a fast-growing near-minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein-coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species-specific constraints to produce the functional iJL208 genome-scale model (GEM) of metabolism. Genome-wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI-syn3.0 and its parent JCVI-syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping-stone toward model-driven whole-genome engineering.

Development and feasibility of a Configurable Assessment Messaging Platform for Interventions (CAMPI).

INTRODUCTION: Short message service (SMS) is a widely accepted telecommunications approach used to support health informatics, including behavioral interventions, data collection, and patient-provider communication. However, SMS delivery platforms are not standardized and platforms are typically commercial "off-the-shelf" or developed "in-house." As a consequence of platform variability, implementing SMS-based interventions may be challenging for both providers and patients. Off-the-shelf SMS delivery platforms may require minimal development or technical resources from providers, but users are often limited in their functionality. Conversely, platforms that are developed in-house are often specified for individual projects, requiring specialized development and technical expertise. Patients are on the receiving end of programming and technical specification challenges; message delays or lagged data affect quality of SMS communications. To date, little work has been done to develop a generalizable SMS platform that can be scaled across health initiatives. OBJECTIVE: We propose the Configurable Assessment Messaging Platform for Interventions (CAMPI) to mitigate challenges associated with SMS intervention implementation (e.g., programming, data collection, message delivery). METHOD: CAMPI aims to optimize health data captured from a multitude of sources and enhance patient-provider communication through a technology that is simple and familiar to patients. Using representative examples from three behavioral intervention case studies implemented among diverse populations (pregnant women, young sexual minority men, and parents with young children), we describe CAMPI capabilities and feasibility. CONCLUSION: As a generalizable SMS platform, CAMPI can be scaled to meet the priorities of various health initiatives, while reducing unnecessary resource utilization and burden on providers and patients. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Impaired prenatal motor axon development necessitates early therapeutic intervention in severe SMA.

Gene replacement and pre-mRNA splicing modifier therapies represent breakthrough gene targeting treatments for the neuromuscular disease spinal muscular atrophy (SMA), but mechanisms underlying variable efficacy of treatment are incompletely understood. Our examination of severe infantile onset human SMA tissues obtained at expedited autopsy revealed persistence of developmentally immature motor neuron axons, many of which are actively degenerating. We identified similar features in a mouse model of severe SMA, in which impaired radial growth and Schwann cell ensheathment of motor axons began during embryogenesis and resulted in reduced acquisition of myelinated axons that impeded motor axon function neonatally. Axons that failed to ensheath degenerated rapidly postnatally, specifically releasing neurofilament light chain protein into the blood. Genetic restoration of survival motor neuron protein (SMN) expression in mouse motor neurons, but not in Schwann cells or muscle, improved SMA motor axon development and maintenance. Treatment with small-molecule SMN2 splice modifiers beginning immediately after birth in mice increased radial growth of the already myelinated axons, but in utero treatment was required to restore axonal growth and associated maturation, prevent subsequent neonatal axon degeneration, and enhance motor axon function. Together, these data reveal a cellular basis for the fulminant neonatal worsening of patients with infantile onset SMA and identify a temporal window for more effective treatment. These findings suggest that minimizing treatment delay is critical to achieve optimal therapeutic efficacy.

Association of race and ethnicity to incident epilepsy, or epileptogenesis, after subdural hematoma.

OBJECTIVE: To determine whether race is associated with the development of epilepsy after subdural hematoma (SDH), we identified adult survivors of SDH in a statewide administrative dataset and followed them up for at least 1 year for revisits associated with epilepsy. METHODS: We performed a retrospective cohort study using claims data on all discharges from emergency departments (EDs) and hospitals in California. We identified adults (age ≥18 years) admitted from 2005 to 2011 with first-time traumatic and nontraumatic SDH. We used validated diagnosis codes to identify a primary outcome of ED or inpatient revisit for epilepsy. We used multivariable Cox regression for survival analysis to identify demographic and medical risk factors for epilepsy. RESULTS: We identified 29,342 survivors of SDH (mean age 71.2 [SD 16.4] years, female sex 11,954 [41.1%]). Three thousand two hundred thirty (11.0%) patients had revisits to EDs or hospitals with a diagnosis of epilepsy during the study period. Black patients (n = 1,684 [5.7%]) had significantly increased risk compared to White patients (n = 16,945 [57.7%]; hazard ratio [HR] 1.45, 95% confidence interval [CI] 1.28-1.64, p < 0.001). Status epilepticus during the index SDH admission, although infrequent (n = 94 [0.3%]), was associated with a nearly 4-fold risk of epilepsy (HR 3.75, 95% CI 2.80-5.03, p < 0.001). Alcohol use, drug use, smoking, renal disease, and markers of injury severity (i.e., intubation, surgical intervention, length of stay, disposition other than home) were also associated with epilepsy (all p < 0.05). CONCLUSIONS: We found an association between Black race and ED and hospital revisits for epilepsy after SDH, establishing the presence of a racial subgroup that is particularly vulnerable to post-SDH epileptogenesis.

The Bitome: digitized genomic features reveal fundamental genome organization.

A genome contains the information underlying an organism's form and function. Yet, we lack formal framework to represent and study this information. Here, we introduce the Bitome, a matrix composed of binary digits (bits) representing the genomic positions of genomic features. We form a Bitome for the genome of Escherichia coli K-12 MG1655. We find that: (i) genomic features are encoded unevenly, both spatially and categorically; (ii) coding and intergenic features are recapitulated at high resolution; (iii) adaptive mutations are skewed towards genomic positions with fewer features; and (iv) the Bitome enhances prediction of adaptively mutated and essential genes. The Bitome is a formal representation of a genome and may be used to study its fundamental organizational properties.

Escher-Trace: a web application for pathway-based visualization of stable isotope tracing data.

BACKGROUND: Stable isotope tracing has become an invaluable tool for probing the metabolism of biological systems. However, data analysis and visualization from metabolic tracing studies often involve multiple software packages and lack pathway architecture. A deep understanding of the metabolic contexts from such datasets is required for biological interpretation. Currently, there is no single software package that allows researchers to analyze and integrate stable isotope tracing data into annotated or custom-built metabolic networks. RESULTS: We built a standalone web-based software, Escher-Trace, for analyzing tracing data and communicating results. Escher-Trace allows users to upload baseline corrected mass spectrometer (MS) tracing data and correct for natural isotope abundance, generate publication quality graphs of metabolite labeling, and present data in the context of annotated metabolic pathways. Here we provide a detailed walk-through of how to incorporate and visualize 13C metabolic tracing data into the Escher-Trace platform. CONCLUSIONS: Escher-Trace is an open-source software for analysis and interpretation of stable isotope tracing data and is available at https://escher-trace.github.io/ .

Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity.

BACKGROUND: Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. RESULTS: Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. CONCLUSIONS: The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

Visualizing metabolic network dynamics through time-series metabolomic data.

BACKGROUND: New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine. RESULTS: The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures. CONCLUSIONS: The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user's guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Racial/ethnic disparities in the risk of intracerebral hemorrhage recurrence.

OBJECTIVE: To estimate the risk of intracerebral hemorrhage (ICH) recurrence in a large, diverse, US-based population and to identify racial/ethnic and socioeconomic subgroups at higher risk. METHODS: We performed a longitudinal analysis of prospectively collected claims data from all hospitalizations in nonfederal California hospitals between 2005 and 2011. We used validated diagnosis codes to identify nontraumatic ICH and our primary outcome of recurrent ICH. California residents who survived to discharge were included. We used log-rank tests for unadjusted analyses of survival across racial/ethnic groups and multivariable Cox proportional hazards regression to determine factors associated with risk of recurrence after adjusting for potential confounders. RESULTS: We identified 31,355 California residents with first-recorded ICH who survived to discharge, of whom 15,548 (50%) were white, 6,174 (20%) were Hispanic, 4,205 (14%) were Asian, and 2,772 (9%) were black. There were 1,330 recurrences (4.1%) over a median follow-up of 2.9 years (interquartile range 3.8). The 1-year recurrence rate was 3.0% (95% confidence interval [CI] 2.8%-3.2%). In multivariable analysis, black participants (hazard ratio [HR] 1.22; 95% CI 1.01-1.48; p = 0.04) and Asian participants (HR 1.29; 95% CI 1.10-1.50; p = 0.001) had a higher risk of recurrence than white participants. Private insurance was associated with a significant reduction in risk compared to patients with Medicare (HR 0.60; 95% CI 0.50-0.73; p < 0.001), with consistent estimates across racial/ethnic groups. CONCLUSIONS: Black and Asian patients had a higher risk of ICH recurrence than white patients, whereas private insurance was associated with reduced risk compared to those with Medicare. Further research is needed to determine the drivers of these disparities.

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree.

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

The Escherichia coli transcriptome mostly consists of independently regulated modules.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Can CPAP Therapy in Pediatric OSA Ever Be Stopped?

STUDY OBJECTIVES: Continuous positive airway pressure (CPAP) has been increasingly used in children with obstructive sleep apnea (OSA), though it is unclear whether it can ever be ceased. We describe the clinical, demographic, and polysomnographic (PSG) characteristics of a cohort of children with OSA who were successfully weaned off CPAP. METHODS: From a pediatric cohort on CPAP for OSA at the Queensland Children's Hospital between January 2016 and December 2017, a subgroup of children who were taken off CPAP were retrospectively studied. RESULTS: CPAP therapy was stopped for 53 children over a 2-year period; 29 of these were excluded from analysis due to change to bilevel support (n = 2), transition to adult care (n = 12), or cessation due to poor adherence (n = 15). A total of 24 children [median (interquartile range, IQR) age 4.1 years (1.0-10.5); 18 males] were successfully weaned off CPAP therapy based on improvement in clinical and PSG parameters; and were included in the analysis. These children had a median (IQR) apnea-hypopnea index (AHI) of 9.8 (5.7-46.0) at CPAP initiation, which improved to 3.3 (0.4-2.2) at CPAP cessation after a median (IQR) duration of 1.0 (0.5-2.0) year. The reasons for CPAP cessation included improved symptoms and/or PSG parameters with time (n = 11); improvement after airway surgery (n = 7), and improvement of body mass index (n = 2). In four children, CPAP therapy was ceased after initial trial due to low physician perceived clinical benefit. CONCLUSIONS: This is the first study describing the characteristics of children and likely reasons for successful CPAP cessation. Children on CPAP should be regularly screened for ongoing CPAP need.

Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism.

Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.