Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.

Importance: Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation. Objective: To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus. Design, Setting, and Participants: The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated. Exposures: Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay. Main Outcomes and Measures: The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Results: A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC). Conclusions and Relevance: This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Expectation-propagation for weak radionuclide identification at radiation portal monitors.

We propose a sparsity-promoting Bayesian algorithm capable of identifying radionuclide signatures from weak sources in the presence of a high radiation background. The proposed method is relevant to radiation identification for security applications. In such scenarios, the background typically consists of terrestrial, cosmic, and cosmogenic radiation that may cause false positive responses. We evaluate the new Bayesian approach using gamma-ray data and are able to identify weapons-grade plutonium, masked by naturally-occurring radioactive material (NORM), in a measurement time of a few seconds. We demonstrate this identification capability using organic scintillators (stilbene crystals and EJ-309 liquid scintillators), which do not provide direct, high-resolution, source spectroscopic information. Compared to the EJ-309 detector, the stilbene-based detector exhibits a lower identification error, on average, owing to its better energy resolution. Organic scintillators are used within radiation portal monitors to detect gamma rays emitted from conveyances crossing ports of entry. The described method is therefore applicable to radiation portal monitors deployed in the field and could improve their threat discrimination capability by minimizing "nuisance" alarms produced either by NORM-bearing materials found in shipped cargoes, such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients.

A Genomic Signature of Influenza Infection Shows Potential for Presymptomatic Detection, Guiding Early Therapy, and Monitoring Clinical Responses.

Early, presymptomatic intervention with oseltamivir (corresponding to the onset of a published host-based genomic signature of influenza infection) resulted in decreased overall influenza symptoms (aggregate symptom scores of 23.5 vs 46.3), more rapid resolution of clinical disease (20 hours earlier), reduced viral shedding (total median tissue culture infectious dose [TCID50] 7.4 vs 9.7), and significantly reduced expression of several inflammatory cytokines (interferon-γ, tumor necrosis factor-α, interleukin-6, and others). The host genomic response to influenza infection is robust and may provide the means for early detection, more timely therapeutic interventions, a meaningful reduction in clinical disease, and an effective molecular means to track response to therapy.

Grand challenges in interfacing engineering with life sciences and medicine.

This paper summarizes the discussions held during the First IEEE Life Sciences Grand Challenges Conference, held on October 4-5, 2012, at the National Academy of Sciences, Washington, DC, and the grand challenges identified by the conference participants. Despite tremendous efforts to develop the knowledge and ability that are essential in addressing biomedical and health problems using engineering methodologies, the optimization of this approach toward engineering the life sciences and healthcare remains a grand challenge. The conference was aimed at high-level discussions by participants representing various sectors, including academia, government, and industry. Grand challenges were identified by the conference participants in five areas including engineering the brain and nervous system; engineering the cardiovascular system; engineering of cancer diagnostics, therapeutics, and prevention; translation of discoveries to clinical applications; and education and training. A number of these challenges are identified and summarized in this paper.

Semi-blind sparse image reconstruction with application to MRFM.

We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

Identification and characterization of Hoxa9 binding sites in hematopoietic cells.

The clustered homeobox proteins play crucial roles in development, hematopoiesis, and leukemia, yet the targets they regulate and their mechanisms of action are poorly understood. Here, we identified the binding sites for Hoxa9 and the Hox cofactor Meis1 on a genome-wide level and profiled their associated epigenetic modifications and transcriptional targets. Hoxa9 and the Hox cofactor Meis1 cobind at hundreds of highly evolutionarily conserved sites, most of which are distant from transcription start sites. These sites show high levels of histone H3K4 monomethylation and CBP/P300 binding characteristic of enhancers. Furthermore, a subset of these sites shows enhancer activity in transient transfection assays. Many Hoxa9 and Meis1 binding sites are also bound by PU.1 and other lineage-restricted transcription factors previously implicated in establishment of myeloid enhancers. Conditional Hoxa9 activation is associated with CBP/P300 recruitment, histone acetylation, and transcriptional activation of a network of proto-oncogenes, including Erg, Flt3, Lmo2, Myb, and Sox4. Collectively, this work suggests that Hoxa9 regulates transcription by interacting with enhancers of genes important for hematopoiesis and leukemia.

Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling.

BACKGROUND: The role of flow cytometry (FCM) in diagnosing myelodysplastic syndromes (MDS) remains controversial, because analysis of myeloid maturation may involve subjective interpretation of sometimes subtle patterns on multiparameter FCM. METHODS: Using six-parameter marker combinations known to be useful in evaluating the myeloid compartment in MDS, we measured objective immunophenotypic differences between non-neoplastic (n = 25) and dysplastic (n = 17) granulopoiesis using a novel method, called Fisher information nonparametric embedding (FINE), that measures information distances among FCM datasets modeled as individual high-dimensional probability density functions, rather than as sets of two-dimensional histograms. Information-preserving component analysis (IPCA) was used to create information-optimized "rotated" two-dimensional histograms for visualizing myelopoietic immunophenotypes for each individual sample. RESULTS: There was a consistent trend of segregation of higher-grade MDS (RAEB and RCMD) from benign by FINE analysis. This difference was accentuated in cases with morphologic dysgranulopoiesis and in cases with clonal cytogenetic abnormalities. However, lower grades of MDS or cases that lacked morphologic dysgranulopoiesis showed much greater overlap with non-neoplastic cases. Two cases of reactive left shift were consistently embedded within the higher-grade MDS group. IPCA yielded two-dimensional histogram projections for each individual case by relative weighting of measured cellular characteristics, optimized for preserving information distances derived through FINE. CONCLUSIONS: Objective analysis by information geometry supports the conclusions of previous studies that there are immunophenotypic differences in the maturation patterns of benign granulopoiesis and high grade MDS, but also reinforces the known pitfalls of overlap between low-grade MDS and benign granulopoiesis and overlap between reactive granulocytic left shifts and dysplastic granulopoiesis.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Sparse image reconstruction for molecular imaging.

The application that motivates this paper is molecular imaging at the atomic level. When discretized at subatomic distances, the volume is inherently sparse. Noiseless measurements from an imaging technology can be modeled by convolution of the image with the system point spread function (psf). Such is the case with magnetic resonance force microscopy (MRFM), an emerging technology where imaging of an individual tobacco mosaic virus was recently demonstrated with nanometer resolution. We also consider additive white Gaussian noise (AWGN) in the measurements. Many prior works of sparse estimators have focused on the case when H has low coherence; however, the system matrix H in our application is the convolution matrix for the system psf. A typical convolution matrix has high coherence. This paper, therefore, does not assume a low coherence H. A discrete-continuous form of the Laplacian and atom at zero (LAZE) p.d.f. used by Johnstone and Silverman is formulated, and two sparse estimators derived by maximizing the joint p.d.f. of the observation and image conditioned on the hyperparameters. A thresholding rule that generalizes the hard and soft thresholding rule appears in the course of the derivation. This so-called hybrid thresholding rule, when used in the iterative thresholding framework, gives rise to the hybrid estimator, a generalization of the lasso. Estimates of the hyperparameters for the lasso and hybrid estimator are obtained via Stein's unbiased risk estimate (SURE). A numerical study with a Gaussian psf and two sparse images shows that the hybrid estimator outperforms the lasso.

Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: treating flow cytometry data as high-dimensional objects.

BACKGROUND: Clinical flow cytometry typically involves the sequential interpretation of two-dimensional histograms, usually culled from six or more cellular characteristics, following initial selection (gating) of cell populations based on a different subset of these characteristics. We examined the feasibility of instead treating gated n-parameter clinical flow cytometry data as objects embedded in n-dimensional space using principles of information geometry via a recently described method known as Fisher Information Non-parametric Embedding (FINE). METHODS: After initial selection of relevant cell populations through an iterative gating strategy, we converted four color (six-parameter) clinical flow cytometry datasets into six-dimensional probability density functions, and calculated differences among these distributions using the Kullback-Leibler divergence (a measurement of relative distributional entropy shown to be an appropriate approximation of Fisher information distance in certain types of statistical manifolds). Neighborhood maps based on Kullback-Leibler divergences were projected onto two dimensional displays for comparison. RESULTS: These methods resulted in the effective unsupervised clustering of cases of acute lymphoblastic leukemia from cases of expansion of physiologic B-cell precursors (hematogones) within a set of 54 patient samples. CONCLUSIONS: The treatment of flow cytometry datasets as objects embedded in high-dimensional space (as opposed to sequential two-dimensional analyses) harbors the potential for use as a decision-support tool in clinical practice or as a means for context-based archiving and searching of clinical flow cytometry data based on high-dimensional distribution patterns contained within stored list mode data. Additional studies will be needed to further test the effectiveness of this approach in clinical practice.