Fast Self-Healing of Polyelectrolyte Multilayer Nanocoating and Restoration of Super Oxygen Barrier.

A self-healable gas barrier nanocoating, which is fabricated by alternate deposition of polyethyleneimine (PEI) and polyacrylic acid (PAA) polyelectrolytes, is demonstrated in this study. This multilayer film, with high elastic modulus, high glass transition temperature, and small free volume, has been shown to be a super oxygen gas barrier. An 8-bilayer PEI/PAA multilayer assembly (≈700 nm thick) exhibits an oxygen transmission rate (OTR) undetectable to commercial instrumentation (<0.005 cc (m-2 d-1 atm-1 )). The barrier property of PEI/PAA nanocoating is lost after a moderate amount of stretching due to its rigidity, which is then completely restored after high humidity exposure, therefore achieving a healing efficiency of 100%. The OTR of the multilayer nanocoating remains below the detection limit after ten stretching-healing cycles, which proves this healing process to be highly robust. The high oxygen barrier and self-healing behavior of this polymer multilayer nanocoating makes it ideal for packaging (food, electronics, and pharmaceutical) and gas separation applications.

Simple Method To Prepare Oligonucleotide-Conjugated Antibodies and Its Application in Multiplex Protein Detection in Single Cells.

The diversity of nucleic acid sequences enables genomics studies in a highly multiplexed format. Since multiplex protein detection is still a challenge, it would be useful to use genomics tools for this purpose. This can be accomplished by conjugating specific oligonucleotides to antibodies. Upon binding of the oligonucleotide-conjugated antibodies to their targets, the protein levels can be converted to oligonucleotide levels. In this report we describe a simple method for preparing oligonucleotide-conjugated antibodies and discuss this method's application in oligonucleotide extension reaction (OER) for multiplex protein detection. Conjugation is based on strain-promoted alkyne-azide cycloaddition (the Cu-free click reaction), in which the antibody is activated with a dibenzocyclooctyne (DBCO) moiety and subsequently linked covalently with an azide-modified oligonucleotide. In the functional test, the reaction conditions and purification processes were optimized to achieve maximum yield and best performance. The OER assay employs a pair of antibody binders (two antibodies, each conjugated with its own oligonucleotide) developed for each protein target. The two oligonucleotides contain unique six-base complementary regions at their 3' prime ends to allow annealing and extension by DNA synthesis enzymes to form a DNA template. Following preamplification, the DNA template is detected by qPCR. Distinct oligonucleotide sequences are assigned to different antibody binders to enable multiplex protein detection. When tested using recombinant proteins, some antibody binders, such as those specific to CSTB, MET, EpCAM, and CASP3, had dynamic ranges of 5-6 logs. The antibody binders were also used in a multiplexed format in OER assays, and the binders successfully detected their protein targets in cell lysates, and in single cells in combination with the C1 system. This click reaction-based antibody conjugation procedure is cost-effective, needs minimal hands-on time, and is well-suited for the development of affordable multiplex protein assays, which provides the potential to accelerate proteomics research.

The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data.

Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6-96.8% precision and 91.6-95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/.

Clinicians' approach to predicting post-cardiac arrest outcomes for patients enrolled in a United States clinical trial.

PURPOSE: Perceived poor prognosis can lead to withdrawal of life-sustaining therapies (WLST) in patients who might otherwise recover. We characterized clinicians' approach to post-arrest prognostication in a multicenter clinical trial. METHODS: Semi-structured interviews were conducted with clinicians who treated a comatose post-cardiac arrest patient enrolled in the Influence of Cooling Duration on Efficacy in Cardiac Arrest Patients (ICECAP) trial (NCT04217551). Two authors independently analyzed each interview using inductive and deductive coding. The clinician reported how they arrived at a prognosis for the specific patient. We summarized the frequency with which clinicians reported using objective diagnostics to formulate their prognosis, and compared the reported approaches to established guidelines. Each respondent provided demographic information and described local neuroprognostication practices. RESULTS: We interviewed 30 clinicians at 19 US hospitals. Most claimed adherence to local hospital neuroprognostication protocols (n = 19). Prognostication led to WLST for perceived poor neurological prognosis in 15/30 patients, of whom most showed inconsistencies with guidelines or trial recommendations, respectively. In 10/15 WLST cases, clinicians reported relying on multimodal testing. A prevalent theme was the use of "clinical gestalt," defined as prognosticating based on a patient's overall appearance or a subjective impression in the absence of objective data. Many clinicians (21/30) reported using clinical gestalt for initial prognostication, with 9/21 expressing high confidence initially. CONCLUSION: Clinicians in our study state they follow neuroprognostication guidelines in general but often do not do so in actual practice. They reported clinical gestalt frequently informed early, highly confident prognostic judgments, and few objective tests changed initial impressions. Subjective prognostication may undermine well-designed trials.

Quantification of Active Visual Attention using RGB camera.

Active visual attention (AVA) is the cognitive ability that helps to focus on important visual information while responding to a stimulus and is important for human-behavior and psychophysiological research. Existing eye-trackers/camera-based methods are either expensive or impose privacy issues as face videos are recorded for analysis. Proposed approach using blink-rate variability (BRV), is inexpensive, easy to implement, efficient and handles privacy issues, making it amenable to real-time applications. Our solution uses laptop camera/webcams and a single blink feature, namely BRV. First, we estimated participant's head pose to check camera alignment and detect if he is looking at the screen. Next, subject-specific threshold is computed using eye aspect ratio (EAR) to detect blinks from which BRV signal is constructed. Only EAR values are saved, and participant's face video is NOT saved or transmitted. Finally, a novel AVA score is computed. Results shows that the proposed score is robust across participants, ambient light conditions and occlusions like spectacles.

Design of a Realtime Photoplethysmogram Signal Quality Checker for Wearables and Edge Computing.

Photoplethysmogram (PPG) signal is extensively used for deducing health parameters of patients in order to infer about physiological conditions of heart, blood pressure, respiratory patterns, and so on. Such analysis and estimations can be done accurately only on high quality PPG signals with very minimal artifacts. PPG signals collected from fitness grade and smart phone scenarios are prone to muscle artifacts and hence there is a need to assess the signal quality before using the signal. Although there are approaches available in the realm of machine learning and deep learning, they are computationally expensive and may not be suitable for a wearable or edge computing scenario. In this paper, we propose the design of a quality checker to check the quality of the signal that can be directly implemented on edge devices like smartwatch. The algorithm is tested on PPG data collected from wearable, ICU and medical grade devices. In the wearable scenario where the noise levels are very high, our algorithm has performed significantly better with a Fscore of over 0.92. Further we show that by applying the proposed quality checker, the accuracy of the computed heart rate from a smart phone PPG-application significantly improves.

An approach to a wrist wearable based Covid-19 prediction system to protect Health Care Professionals.

With healthcare professionals being the frontline warriors in battling the Covid pandemic, their risk of exposure to the virus is extremely high. We present our experience in building a system, aimed at monitoring the physiology of these professionals 24/7, with the hope of providing timely detection of infection and thereby better care. We discuss a machine learning approach and model using signals from a wrist wearable device to predict infection at a very early stage. In a double-blind test on a small group of patients, our model could successfully identify the infected versus non-infected cases with near 100% accuracy. We also discuss some of the challenges we faced, both technical and non-technical, to get this system off the ground as well as offer some suggestions that could help tackle these hurdles.

Surface Potential Simulation and Electrode Design for in-Ear EEG Measurement.

The need for everyday-real-time EEG sensing has resulted in the development of minimalistic and discreet wearable EEG measuring devices. A front runner in this race is in-ear worn device. However, the position of the ear as well as its restricted accessibility poses certain challenges in the design of such devices - from the type of materials used to the placement of electrodes. These choices are important as they will determine the quality of the EEG signal available and in turn the accuracy of the application. We therefore create a simulation model of the human ear, allowing us to understand the impact of these choices on our design of an In-Ear EEG wearable. We first study the signal acquisition characteristics of a proposed gold-plated electrode against two other state-of-the-art electrode materials for in-ear EEG data collection. We further validate this electrode choice by fabricating a personalized silicone-based earpiece and collecting in-situ EEG data. This work explores the properties of using gold plated electrodes in capturing in-ear EEG signals enabling unobtrusive collection of the brain physiology data in real world setting.

Taking qPCR to a higher level: Analysis of CNV reveals the power of high throughput qPCR to enhance quantitative resolution.

This paper assesses the quantitative resolution of qPCR using copy number variation (CNV) as a paradigm. An error model is developed for real-time qPCR data showing how the precision of CNV determination varies with the number of replicates. Using samples with varying numbers of X chromosomes, experimental data demonstrates that real-time qPCR can readily distinguish four copes from five copies, which corresponds to a 1.25-fold difference in relative quantity. Digital PCR is considered as an alternative form of qPCR. For digital PCR, an error model is shown that relates the precision of CNV determination to the number of reaction chambers. The quantitative capability of digital PCR is illustrated with an experiment distinguishing four and five copies of the human gene MRGPRX1. For either real-time qPCR or digital PCR, practical application of these models to achieve enhanced quantitative resolution requires use of a high throughput PCR platform that can simultaneously perform thousands of reactions. Comparing the two methods, real-time qPCR has the advantage of throughput and digital PCR has the advantage of simplicity in terms of the assumptions made for data analysis.

A Novel Optimization Algorithm Leveraging a Three-Dimensional Approach of Periscopic, Pheromonic and Fractal Search.

This paper focuses on a new algorithm for solving optimization problems using the nature of food search behaviour of caterpillars. The paper describes how the periscopic, pheromonic and fractal search properties analogous to the caterpillars, can aid in designing a new optimization algorithm. The performance characteristics of the new method is compared using 26 standard test functions and the area under the curve of the fitness evaluations is used to validate and compare the proposed algorithms against existing related works. The proposed algorithm is found to be efficient when compared with the existing methods. The proposed algorithm is then tested on a real world problem to remove signal noise from eye gaze data, effectively.

Person and Stressor Independent Generic Model for Stress Detection Using GSR.

Stress detection is a widely researched topic and is important for overall well-being of an individual. Several approaches are used for prediction/classification of stress. Most of these approaches perform well for subject and activity specific scenarios as stress is highly subjective. So, it is difficult to create a generic model for stress prediction. Here, we have proposed an approach for creating a generic stress prediction model by utilizing knowledge from three different datasets. Proposed model has been validated using two open datasets as well as on a set of data collected in our lab. Results show that the proposed generic model performs well across studies conducted independently and hence can be used for monitoring stress in real life scenarios and to create mass-market stress prediction products.

Cost Effective Real-time System for cognitive computing using Personalized Eye Blink Detection from Camera.

Eye blink is indicative of various mental states. Generally, vision based approaches are used for detecting eye blinks. However, performance of such approaches varies across participants. Standard eye tracker or eye glasses used for detecting blinks, are very costly. Here, we are proposing a personalized vision based eye blink detector system. Proposed approach is ubiquitous and unobtrusive in nature and can be implemented using standard webcams/mobile camera, making it deployable for real world scenarios. Our approach has been validated on a set of data collected from our lab and on an open data set. Results show that in both cases, our system performs well for various conditions like natural/artificial light, with or without spectacles. We achieved a Fscore of 0.98 for own collected data and 0.91 for open dataset, which outperform state of the art approaches.

Affective response to tunes synthesized with musical pitch curves.

Tunes perceived as happy may help a user reach an affective state of positive valence. However, a user with negative valence may not be ready to listen to such a tune immediately. In this paper, we consider nudging a user from their current affective state to a target affective state in small steps. We propose a technique to generate a gradation of tunes between an initial-reference tune and a target-reference tune, to achieve the affect transition. The two-dimensional gradation is realized in time and in pitch, respectively, by varying the tempo and by the use of musical pitch curves, i.e. pitch transients or simply 'transients'. We exploit the duration and scaling of transients observed in South Indian music (Carnatic) to introduce transients into existing tunes. In our experiment, we have introduced the transients into Western music tunes. The results of perceptual evaluation show that the affective response to transients is likely to be higher at slow tempos than at fast tempos. Further, when felt, transient-tunes are twice as likely to be associated with positive valence than with negative valence, irrespective of tempo.

Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This can be useful in applications such as optimal work allocation, increasing efficiency in the workplace and ensuring safety in difficult work environments. In order for such systems to be realistically deployable, easy acquisition and processing of the data on a wearable device is imperative. Current techniques primarily perform offline processing to analyse a multi-channel EEG to make a post facto assessment. This work focusses on building a new deep learning architecture that performs a single feature based spatio-temporal analysis of EEG data. This is achieved by creating a brain topographic map based on a single feature followed by spatio-temporal analysis using the developed network architecture. Data from two cognitive load experiments on the Physionet EEGMAT dataset were used to validate the performance. The network achieves an accuracy of 98.3% which is better than similar state-of-the-art approaches. Moreover, the proposed approach facilitates analysis of the spatial propagation of a signal, which is not possible through conventional EEG signal representations.

A trial study of using DSST to evaluate cognitive impairment in older adults.

Non-invasive means of monitoring mild cognitive impairments (MCI) is recently gaining popularity. With the advent of easy to use physiological sensors, there have been an outburst of studies from the last decade which aim at detecting a target's mental health condition. However, not many studies present the experience or insights gained from carrying out such in-situ research work, particularly when working with older adults. Such insights could not only assist researchers in related areas when designing their study but also avoid potential pitfalls. Clinical trials were conducted by our organization in collaboration with the Geriatric Educational Research Institute, Singapore (GERI) and Singapore Management University (SMU) for detecting mild cognitive impairments in a geriatric population. Digitized versions of the standard pen & paper psychological tests were used along with gaze tracking technologies for MCI detection. Details of our user study and it's outcomes are discussed as well as a generic approach of digitizing any given psychological test battery is highlighted.

Extending and evaluating a warfarin dosing algorithm that includes CYP4F2 and pooled rare variants of CYP2C9.

OBJECTIVE: Warfarin dosing remains challenging because of its narrow therapeutic window and large variability in dose response. We sought to analyze new factors involved in its dosing and to evaluate eight dosing algorithms, including two developed by the International Warfarin Pharmacogenetics Consortium (IWPC). METHODS: we enrolled 108 patients on chronic warfarin therapy and obtained complete clinical and pharmacy records; we genotyped single nucleotide polymorphisms relevant to the VKORC1, CYP2C9, and CYP4F2 genes using integrated fluidic circuits made by Fluidigm. RESULTS: When applying the IWPC pharmacogenetic algorithm to our cohort of patients, the percentage of patients within 1 mg/d of the therapeutic warfarin dose increases from 54% to 63% using clinical factors only, or from 38% using a fixed-dose approach. CYP4F2 adds 4% to the fraction of the variability in dose (R) explained by the IWPC pharmacogenetic algorithm (P<0.05). Importantly, we show that pooling rare variants substantially increases the R for CYP2C9 (rare variants: P=0.0065, R=6%; common variants: P=0.0034, R=7%; rare and common variants: P=0.00018; R=12%), indicating that relatively rare variants not genotyped in genome-wide association studies may be important. In addition, the IWPC pharmacogenetic algorithm and the Gage (2008) algorithm perform best (IWPC: R=50%; Gage: R=49%), and all pharmacogenetic algorithms outperform the IWPC clinical equation (R=22%). VKORC1 and CYP2C9 genotypes did not affect long-term variability in dose. Finally, the Fluidigm platform, a novel warfarin genotyping method, showed 99.65% concordance between different operators and instruments. CONCLUSION: CYP4F2 and pooled rare variants of CYP2C9 significantly improve the ability to estimate warfarin dose.

Quantifying EGFR alterations in the lung cancer genome with nanofluidic digital PCR arrays.

BACKGROUND: The EGFR [epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)] gene is known to harbor genomic alterations in advanced lung cancer involving gene amplification and kinase mutations that predict the clinical response to EGFR-targeted inhibitors. Methods for detecting such molecular changes in lung cancer tumors are desirable. METHODS: We used a nanofluidic digital PCR array platform and 16 cell lines and 20 samples of genomic DNA from resected tumors (stages I-III) to quantify the relative numbers of copies of the EGFR gene and to detect mutated EGFR alleles in lung cancer. We assessed the relative number of EGFR gene copies by calculating the ratio of the number of EGFR molecules (measured with a 6-carboxyfluorescein-labeled Scorpion assay) to the number of molecules of the single-copy gene RPP30 (ribonuclease P/MRP 30kDa subunit) (measured with a 6-carboxy-X-rhodamine-labeled TaqMan assay) in each panel. To assay for the EGFR L858R (exon 21) mutation and exon 19 in-frame deletions, we used the ARMS and Scorpion technologies in a DxS/Qiagen EGFR29 Mutation Test Kit for the digital PCR array. RESULTS: The digital array detected and quantified rare gefitinib/erlotinib-sensitizing EGFR mutations (0.02%-9.26% abundance) that were present in formalin-fixed, paraffin-embedded samples of early-stage resectable lung tumors without an associated increase in gene copy number. Our results also demonstrated the presence of intratumor molecular heterogeneity for the clinically relevant EGFR mutated alleles in these early-stage lung tumors. CONCLUSIONS: The digital PCR array platform allows characterization and quantification of oncogenes, such as EGFR, at the single-molecule level. Use of this nanofluidics platform may provide deeper insight into the specific roles of clinically relevant kinase mutations during different stages of lung tumor progression and may be useful in predicting the clinical response to EGFR-targeted inhibitors.

Studying copy number variations using a nanofluidic platform.

Copy number variations (CNVs) in the human genome are conventionally detected using high-throughput scanning technologies, such as comparative genomic hybridization and high-density single nucleotide polymorphism (SNP) microarrays, or relatively low-throughput techniques, such as quantitative polymerase chain reaction (PCR). All these approaches are limited in resolution and can at best distinguish a twofold (or 50%) difference in copy number. We have developed a new technology to study copy numbers using a platform known as the digital array, a nanofluidic biochip capable of accurately quantitating genes of interest in DNA samples. We have evaluated the digital array's performance using a model system, to show that this technology is exquisitely sensitive, capable of differentiating as little as a 15% difference in gene copy number (or between 6 and 7 copies of a target gene). We have also analyzed commercial DNA samples for their CYP2D6 copy numbers and confirmed that our results were consistent with those obtained independently using conventional techniques. In a screening experiment with breast cancer and normal DNA samples, the ERBB2 gene was found to be amplified in about 35% of breast cancer samples. The use of the digital array enables accurate measurement of gene copy numbers and is of significant value in CNV studies.

Effect of Moisture Sorption on Free Volume and Relaxation of Spray Dried Dispersions: Relation to Drug Recrystallization.

The effect of vapor sorption on the free volume of drug-polymer spray-dried dispersions (SDDs) was investigated, along with the crystallization propensity of drug molecules in SDDs after exposure to humidity. Subsequently, the correlation of free volume change and relaxation time with drug recrystallization was examined. Four polymers, including polyvinylpyrrolidone, polyvinylpyrrolidone vinyl acetate copolymer, hydroxypropyl cellulose, and hydroxypropyl methylcellulose acetate succinate, and 2 drugs (indomethacin and ketoconazole) were selected for preparing SDDs. Free volume data of the exposed SDDs were obtained with positron annihilation lifetime spectroscopy, while the relaxation time was measured using a TA rheometer. Additionally, the crystallization propensity of active pharmaceutical ingredients (APIs) in the exposed SDDs was assessed using both polarized light microscopy and powder X-ray diffraction, followed by relating API crystallization inclination with expansion of holes and relaxation time. Finally, Cohen and Turnbull molecular transport model, along with its extensions by Vrentas and Duda, was qualitatively utilized for interpreting the recrystallization propensity of API molecules. In conclusion, API recrystallization is closely related to free volume change upon moisture sorption and relaxation time, but system dependent; overall, drug-hydroxypropyl methylcellulose acetate succinate SDDs appear physically stable against recrystallization due to less increase in free volume.

High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. There is a need for systems allowing medium multiplexing (ten to hundreds of SNPs) with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set (thousands of individuals). Methods that are flexible, fast, accurate and cost-effective are urgently needed. This is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed. RESULTS: We demonstrate the use of a nanofluidic Integrated Fluidic Circuit (IFC) - based genotyping system for medium-throughput multiplexing known as the Dynamic Array, by genotyping 994 individual human DNA samples on 47 different SNP assays, using nanoliter volumes of reagents. Call rates of greater than 99.5% and call accuracies of greater than 99.8% were achieved from our study, which demonstrates that this is a formidable genotyping platform. The experimental set up is very simple, with a time-to-result for each sample of about 3 hours. CONCLUSION: Our results demonstrate that the Dynamic Array is an excellent genotyping system for medium-throughput multiplexing (30-300 SNPs), which is simple to use and combines rapid throughput with excellent call rates, high concordance and low cost. The exceptional call rates and call accuracy obtained may be of particular interest to those working on validation and replication of genome- wide- association (GWA) studies.