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This paper establishes a membraneless, co-laminar flow-based approach to develop a cost-effective microfluidic paper-based analytical device for enzymatic biofuel cell ( PAD-EBFC). The developed PAD-EBFC supporting the self-capillary fluid transport action consists of Y-shaped paper microchannel with the fuel (glucose) and oxidant (O 2 ) streaming in parallel over carbon nanotube-based bucky paper electrodes modified with biocompatible electrocatalytic enzymes, such as glucose oxidase and laccase without any additional redox cofactor. The electrochemical performance for the modified bioelectrodes, i.e., electrocatalytic oxidation and reduction reaction, was carried out using linear sweep voltammetry, cyclic voltammetry, and open circuit potential. The overall performance of PAD-EBFC was evaluated using the polarization studies. Subsequently, the catalytic activity of enzymes on the electrode surface was validated by the scanning electron microscope. This simple and portable PAD-EBFC can generate the maximum power density to the order of /cm 2 ( /cm 2 ) at 0.505 V over prolonged durations of around 50 h. Hence, the presented PAD-EBFC shows good power density and stability, leading to its strong potential to power miniaturized microelectronics devices and sensors.

The idea of a systematic digital representation of the entire known human pathophysiology, which we could call the Virtual Human Twin, has been around for decades. To date, most research groups focused instead on developing highly specialised, highly focused patient-specific models able to predict specific quantities of clinical relevance. While it has facilitated harvesting the low-hanging fruits, this narrow focus is, in the long run, leaving some significant challenges that slow the adoption of digital twins in healthcare. This position paper lays the conceptual foundations for developing the Virtual Human Twin (VHT). The VHT is intended as a distributed and collaborative infrastructure, a collection of technologies and resources (data, models) that enable it, and a collection of Standard Operating Procedures (SOP) that regulate its use. The VHT infrastructure aims to facilitate academic researchers, public organisations, and the biomedical industry in developing and validating new digital twins in healthcare solutions with the possibility of integrating multiple resources if required by the specific context of use. Healthcare professionals and patients can also use the VHT infrastructure for clinical decision support or personalised health forecasting. As the European Commission launched the EDITH coordination and support action to develop a roadmap for the development of the Virtual Human Twin, this position paper is intended as a starting point for the consensus process and a call to arms for all stakeholders.

This paper demonstrates a simple-in-struct- ure, cost-effective, and environment-friendly Microfluidic Paper-based Analytical Device for Microbial Biofuel Cell ( μ PAD-MBFC). It consists of a microchannel with biofuel cell (Escherichia.Coli) and an oxidant (aerated tap water) flowing co-parallelly over Multiwalled Carbon Nanotube (MWCNT)-based Bucky Paper (BP) electrodes using a self-capillary and co-laminar flow mechanism. The electrochemical studies, such as open circuit potential (OCP) and polarization were evaluated using a potentiostat. Various volumetric bacterial studies were also carried out to find out the best suitable optimal bacterial volume. Subsequently, the morphological and detailed element composition study of electrode surface was performed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) techniques. This well-designed portable μ PAD-MBFC yields a maximum power density of 4 [Formula: see text]/cm 2 ( [Formula: see text]/cm 2 ) at 0.405 V over [Formula: see text] of culture and leveraging to its long-lasting potential to operate miniaturized microelectronics sensors and portable devices.

In the above article [1], the caption of Fig. 2 should read "Fig. 2. Images of untreated (bare) and surface modified BPs. (a) and (b) SEM images of bare BP (different magnification). (c) and (d) SEM images of GOx immobilized BP (different magnification). (e) and (f) SEM images of Laccase immobilized BP (different magnification). Copyright Permission from IEEE [2]."

Selective detection and quantification of biomarkers related to human diseases are essential for preventive healthcare. Surface-enhanced Raman scattering (SERS) spectroscopy is a powerful analytical tool offering high sensitivity. However, the success of this promising analytical tool relies on the ability to effectively fabricate SERS substrate. Herein we have demonstrated a plasmonic paper-based flexible substrate (PPFS) for SERS sensing. In situ growth of silver nanostructures (AgNS) on the paper-based substrate was achieved by using a simple one-step silver mirror reaction (SMR). FESEM and TEM results depicts that the increasing silver ion content influences the morphology (growth of multifacets), as well as size of AgNS. Further, the PPFS substrate was tested with Rhodamine-6G (Rh-6G) dye and an attomole sensitivity with a LOD of 4.54 × 10 -18 M was achieved. Further, two biomarkers, lactic acid (LA) and uric acid (UA) were detected on the PPFS substrate, with [Formula: see text] and pM sensitivity, having LOD values of 0.6 × 10 -6 and 0.3 × 10 -12 M respectively. Above detection levels for UA on PPFS is two orders better than reported values, whereas for LA it is comparable with reported substrates. Finally, UA, LA and their mixtures were tested on PPFS and results compared with commercial substrate. The performance of PPFS were found better in all cases, thus, multifaceted AgNS paper based PPFS offers the potential to be used as a biosensor for detection of various biomarkers from body fluids, responsible for the detection of the critical disease for preventive health care.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application., Methods and Procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients' demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared., Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86-0.99 and 0.79-0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively., Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Objective: Early stage diagnosis of sepsis without overburdening health services is essential to improving patient outcomes., Methods: A fast and simple-to-use platform that combines an integrated circuit with paper microfluidics for simultaneous detection of multiple-metabolites appropriate for diagnostics was presented. Paper based sensors are a primary candidate for widespread deployment of diagnostic or test devices. However, the majority of devices today use a simple paper strip to detect a single marker using the reflectance of light. However, for many diseases such as sepsis, one biomarker is not sufficient to make a unique diagnosis. In this work multiple measurements are made on patterned paper simultaneously. Using laser ablation to fabricate microfluidic channels on paper provides a flexible and direct approach for mass manufacture of disposable paper strips. A reusable photodiode array on a complementary metal oxide semiconductor chip is used as the transducer., Results: The system measures changes in optical absorbance in the paper to achieve a cost-effective and easily implemented system that is capable of multiple simultaneous assays. Potential sepsis metabolite biomarkers glucose and lactate have been studied and quantified with the platform, achieving sensitivity within the physiological range in human serum., Conclusion: We have detailed a disposable paper-based CMOS photodiode sensor platform for real-time simultaneous detection of metabolites for diseases such as sepsis., Significance: A combination of a low-cost paper strip with microfluidic channels and a sensitive CMOS photodiode sensor array makes our platform a robust portable and inexpensive biosensing device for multiple diagnostic tests in many different applications.

This paper describes the need for technologies that improve analytical sensitivity to proteins to better define and monitor the progression from heath to disease over the course of an individual's life. These technologies have the potential to allow the early diagnosis of disease, and trigger treatments at the time when they have the greatest opportunity to be effective. We will describe a technology that we have developed for high sensitivity protein detection, namely, single molecule arrays (Simoa). Simoa is based on the capture of protein molecules on magnetic beads, labeling each protein with an enzyme, and counting of single enzyme labels on beads that are isolated in arrays of femtoliter wells. Simoa has enabled the detection of proteins at subfemtomolar concentrations in a variety of biological fluids. We describe the impact of higher sensitivity of proteins using Simoa on: less invasive testing; earlier detection of disease; providing biomarker baseline profiles for healthy individuals; testing of small sample volumes; monitoring of therapeutic efficacy; faster tests; and detection of proteins in complex samples. We also provide a perspective of how new technologies that allow the low-cost manufacture and miniaturization of Simoa could drive the next wave of analytical devices, including wearables.

Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affect an individual, we are developing targeted therapies to personalize treatments. There is a need for technologies like Artificial Intelligence (AI) to be able to support predictions for personalized treatments. In order to mainstream AI in healthcare we will need to address issues such as explainability, liability and privacy. Developing explainable algorithms and including AI training in medical education are many of the solutions that can help alleviate these concerns.

Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form., Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal., Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results., Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/-0.021 (p-value < 0.001), QRS interval 1+/- SD (p-value < 0.001), QT interval 0.981 +/- 0.023 p-value < 0.001, and RR interval 1 +/- 0.001 p-value < 0.001., Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease., (2168-2372 © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.)

Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment.

Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce networks that either have undirected and unweighted edges, or else are constrained to contain no cycles, both of which are biologically unrealistic. In the present paper we propose a new algorithm, based on a concept from probability theory known as the ϕ -mixing coefficient, that produces networks whose edges are weighted and directed, and are permitted to contain cycles. Specifically, we inferred networks for two subtypes of lung cancer small cell (SCLC) and non-small cell (NSCLC) as well as normal lung tissue. Then we compared with the outcomes of siRNA screening of 19,000+ genes on 11 NSCLC cell lines, and found that the higher the degree of a gene in the inferred network, the more essential it is to the survival of a cell. We also analyzed data from a ChIP-Seq experiment to determine putative downstream targets of ASCL1. The SCLC network was enriched for ChIP-seq neighbors of this oncogenic transcription factor, but not in the NSCLC network. We also reverse-engineered whole-genome interaction networks for two distinct subtypes of breast cancer, namely Luminal-A and Basal (also known as triple negative).

Parallel test paper generation is a biobjective distributed resource optimization problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified assessment criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in both of the collective objective functions. In this paper, we propose a submodular memetic approximation algorithm for solving this problem. The proposed algorithm is an adaptive memetic algorithm (MA), which exploits the submodular property of the collective objective functions to design greedy-based approximation algorithms for enhancing steps of the multiobjective MA. Synergizing the intensification of submodular local search mechanism with the diversification of the population-based submodular crossover operator, our algorithm can jointly optimize the total quality maximization objective and the fairness quality maximization objective. Our MA can achieve provable near-optimal solutions in a huge search space of large datasets in efficient polynomial runtime. Performance results on various datasets have shown that our algorithm has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.

Every year, hundreds of thousands of children worldwide are born with sickle cell disease, a genetic disorder that impacts the hemoglobin molecules in blood. If left undiagnosed and untreated, most affected children will die before reaching the age of five. However, highly accurate diagnostic methods and effective treatment regimens for sickle cell disease have been known for many years, and children who receive early diagnosis and subsequent comprehensive care survive well into adulthood-as evidenced by the tremendous success of universal newborn screening programs in North America and Europe.

When I started graduate school, I had no idea that it would take me halfway around the world to a small clinic in Cabinda, Angola, but I'm glad that it did. Since 2011, I have been a part of the team of engineers and clinicians, led by Dr. Sergey Shevkoplyas, working to develop a simple, low-cost, paper-based test for sickle cell disease-a common inherited blood disorder that is most prevalent in sub-Saharan Africa (Figure S1).

Infectious diseases are a major cause of death and economic impact worldwide. A more robust, adaptable, and scalable infrastructure would improve the capability to respond to epidemics. Because engineers contribute to the design and implementation of infrastructure, there are opportunities for innovative solutions to infectious disease response within existing systems that have utility, and therefore resources, before a public health emergency. Examples of innovative leveraging of infrastructure, technologies to enhance existing disease management strategies, engineering approaches to accelerate the rate of discovery and application of scientific, clinical, and public health information, and ethical issues that need to be addressed for implementation are presented., (© IEEE 2015. This article is free to access and download, along with rights for full text and data mining, re-use and analysis.)

Let us consider that someone is starting a research on a topic that is unfamiliar to them. Which seminal papers have influenced the topic the most? What is the genealogy of the seminal papers in this topic? These are the questions that they can raise, which we try to answer in this paper. First, we propose an algorithm that finds a set of seminal papers on a given topic. We also address the performance and scalability issues of this sophisticated algorithm. Next, we discuss the measures to decide how much a paper is influenced by another paper. Then, we propose an algorithm that constructs a genealogy of the seminal papers by using the influence measure and citation information. Finally, through extensive experiments with a large volume of a real-world academic literature data, we show the effectiveness and efficiency of our approach.

Objective: We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value., Methods and Procedures: To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG., Results: The validation demonstrates a correlation value of 0.85-0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8-0.9 (p < 0.05), and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer)., Conclusion: The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique., Clinical Impact: Using this type of ECG digitization tool to carry out retrospective studies on large databases, which rely on paper ECG records, studies of emerging ECG features can be performed. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient's electronic medical record.

This paper presents the prototype implementation of a Bio-Patch using fully integrated low-power System-on-Chip (SoC) sensor and paper-based inkjet printing technology. The SoC sensor is featured with programmable gain and bandwidth to accommodate a variety of bio-signals. It is fabricated in a 0.18-ìm standard CMOS technology, with a total power consumption of 20 ìW from a 1.2 V supply. Both the electrodes and interconnections are implemented by printing conductive nano-particle inks on a flexible photo paper substrate using inkjet printing technology. A Bio-Patch prototype is developed by integrating the SoC sensor, a soft battery, printed electrodes and interconnections on a photo paper substrate. The Bio-Patch can work alone or operate along with other patches to establish a wired network for synchronous multiple-channel bio-signals recording. The measurement results show that electrocardiogram and electromyogram are successfully measured in in-vivo tests using the implemented Bio-Patch prototype.

Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Video halftoning is a key technology for use in electronic paper (e-paper) or smart paper, which is an emerging display device that has received considerable attention recently. In this paper, a temporal frequency of flickering-distortion optimized video halftoning method is proposed. We first uncover three visual defects that conventional neighboring frame referencing-based video halftoning methods, due to their sequential changes of reference frames, will encounter. To deal with the problem, we then propose a reference frame update per GOP-based error diffusion video halftoning method based on a flickering sensitivity-based human visual model. To efficiently compromise between average temporal frequency of flickering (ATFoF) and visual quality, temporal frequency of flickering-distortion (TFoFD) is presented as a metric for video halftoning performance evaluation. Based on the proposed probability model of video halftoning, the TFoFD curve can be accurately estimated to optimize the tradeoff between quality and ATFoF before the video is halftoned. Our temporal frequency of flickering-distortion optimization strategy can also be applied to other video halftoning schemes for performance improvement. Experimental results and comparisons with known methods demonstrate the effectiveness of our video halftoning method.

Capacitance transducers containing a thin polymer membrane have been used to transmit ultrasonic signals with frequencies in excess of 1 MHz through various paper products such as paper and cardboard. At normal incidence, a resonance was visible in thicker samples, the frequency of which could be correlated to parameters such as the thickness of the paper sample and the moisture content. It has also been demonstrated that images can be obtained of changes in structure across paper and card samples.

This paper describes the neural-based recognition and verification techniques used in a banknote machine, recently implemented for accepting paper currency of different countries. The perception mechanism is based on low-cost optoelectronic devices which produce a signal associated with the light refracted by the banknotes. The classification and verification steps are carried out by a society of multilayer perceptrons whose operation is properly scheduled by an external controlling algorithm, which guarantees real-time implementation on a standard microcontroller-based platform. The verification relies mainly on the property of autoassociators to generate closed separation surfaces in the pattern space. The experimental results are very interesting, particularly when considering that the recognition and verification steps are based on low-cost sensors.

In this paper a new technique is proposed to improve the recognition ability and the transaction speed to classify the Japanese and US paper currency. Two types of data sets, time series data and Fourier power spectra, are used in this study. In both cases, they are directly used as inputs to the neural network. Furthermore, we also refer a new evaluation method of recognition ability. Meanwhile, a technique is proposed to reduce the input scale of the neural network without preventing the growth of recognition. This technique uses only a subset of the original data set which is obtained using random masks. The recognition ability of using large data set and a reduced data set are discussed. In addition to that the results of using a reduced data set of the Fourier power spectra and the time series data are compared.