Boolean satisfiability in quantum compilation.

Quantum compilation is the task of translating a quantum algorithm implemented in a high-level quantum programming language into a technology-dependent instructions flow for a physical quantum computer. To tackle the large gap between the quantum program and the low-level instructions, quantum compilation is split into a multi-stage flow consisting of several layers of abstraction. Several different individual tasks have been proposed for the layers in the flow, many of them are NP-hard. In this article, we will describe the flow and we will propose algorithms based on Boolean satisfiability, which is a good match to tackle such computationally complex problems. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

Fast Procedures for the Electrodeposition of Platinum Nanostructures on Miniaturized Electrodes for Improved Ion Sensing.

Nanostructured materials have attracted considerable interest over the last few decades to enhance sensing capabilities thanks to their unique properties and large surface area. In particular, noble metal nanostructures offer several advantages including high stability, non-toxicity and excellent electrochemical behaviour. However, in recent years the great expansion of point-of-care (POC) and wearable systems and the attempt to perform measurements in tiny spaces have also risen the need of increasing sensors miniaturization. Fast constant potential electrodeposition techniques have been proven to be an efficient way to obtain conformal platinum and gold nanostructured layers on macro-electrodes. However, this technique is not effective on micro-electrodes. In this paper, we investigate an alternative one-step deposition technique of platinum nanoflowers on micro-electrodes by linear sweep voltammetry (LSV). The effective deposition of platinum nanoflowers with similar properties to the ones deposited on macro-electrodes is confirmed by morphological analysis and by the similar roughness factor (~200) and capacitance (~18 µ F/mm 2 ). The electrochemical behaviour of the nanostructured layer is then tested in an solid-contact (SC) L i + -selective micro-electrode and compared to the case of macro-electrodes. The sensor offers Nernstian calibration with same response time (~15 s) and a one-order of magnitude smaller limit of detection (LOD) ( 2.6 × 10 - 6 ) with respect to the macro-ion-selective sensors (ISE). Finally, sensor reversibility and stability in both wet and dry conditions is proven.

A bimetallic nanocoral Au decorated with Pt nanoflowers (bio)sensor for H2O2 detection at low potential.

In this work, we have developed for the first time a method to make novel gold and platinum hybrid bimetallic nanostructures differing in shape and size. Au-Pt nanostructures were prepared by electrodeposition in two simple steps. The first step consists of the electrodeposition of nanocoral Au onto a gold substrate using hydrogen as a dynamic template in an ammonium chloride solution. After that, the Pt nanostructures were deposited onto the nanocoral Au organized in pores. Using Pt (II) and Pt (IV), we realized nanocoral Au decorated with Pt nanospheres and nanocoral Au decorated with Pt nanoflowers, respectively. The bimetallic nanostructures showed better capability to electrochemically oxidize hydrogen peroxide compared with nanocoral Au. Moreover, Au-Pt nanostructures were able to lower the potential of detection and a higher performance was obtained at a low applied potential. Then, glucose oxidase was immobilized onto the bimetallic Au-Pt nanostructure using cross-linking with glutaraldehyde. The biosensor was characterized by chronoamperometry at +0.15V vs. Ag pseudo-reference electrode (PRE) and showed good analytical performances with a linear range from 0.01 to 2.00mM and a sensitivity of 33.66µA/mMcm2. The good value of Kmapp (2.28mM) demonstrates that the hybrid nanostructure is a favorable environment for the enzyme. Moreover, the low working potential can minimize the interference from ascorbic acid and uric acid as well as reducing power consumption to effect sensing. The simple procedure to realize this nanostructure and to immobilize enzymes, as well as the analytical performances of the resulting devices, encourage the use of this technology for the development of biosensors for clinical analysis.

Label-Free Ultrasensitive Memristive Aptasensor.

We present the very first worldwide ever-reported electrochemical biosensor based on a memristive effect and DNA aptamers. This novel device is developed to propose a completely new approach in cancer diagnostics. In this study, an affinity-based technique is presented for the detection of the prostate specific antigen (PSA) using DNA aptamers. The hysteretic properties of memristive silicon nanowires functionalized with these DNA aptamers provide a label-free and ultrasensitive biodetection technique. The ultrasensitive detection is hereby demonstrated for PSA with a limit of detection down to 23 aM, best ever published value for electrochemical biosensors in PSA detection. The effect of polyelectrolytes on our memristive devices is also reported to further show how positive or negative charges affect the memristive hysteresis. With such an approach, combining memristive nanowires and aptamers, memristive aptamer-based biosensors can be proposed to detect a wide range of cancer markers with unprecedent ultrasensitivities to also address the issue of an early detection of cancer.

Enzymatic and Nonenzymatic Electrochemical Interaction of Abiraterone (Antiprostate Cancer Drug) with Multiwalled Carbon Nanotube Bioelectrodes.

Unexplored electrochemical behavior of abiraterone, a recent and widely used prostate cancer drug, in interaction with cytochrome P450 3A4 (CYP3A4) enzyme and multiwalled carbon nanotubes (MWCNTs) is investigated in this work. The results reported in this work are significant for personalized medicine and point-of-care chemical treatment, especially to improve the life expectancy and quality of life of patients with prostate-cancer. To this purpose, enzymatic and nonenzymatic electrochemical biosensors were developed and characterized with different concentrations of abiraterone. Nonenzymatic biosensors were functionalized with MWCNTs as a catalyst for signal enhancement, while enzymatic biosensors have been obtained with CYP3A4 protein immobilized on MWCNTs as recognition biomolecule. Enzymatic electrochemical experiments demonstrated an inhibition effect on the CYP3A4, clearly observed as a diminished electrocatalytic activity of the enzyme. Electrochemical responses of nonenzymatic biosensors clearly demonstrated the direct electroactivity of abiraterone when reacting with MWCNT as well as an electrode-fouling effect.

High-performance multipanel biosensors based on a selective integration of nanographite petals.

We report the first selective growth of nanographite petals and various carbon nanomaterials onto a multipanel electrochemical platform. Different types of nanomaterials can be obtained by fine-tuning the growth parameters of the chemical vapor deposition (CVD) process. First, absolute novelty is the catalytic CVD selective growth of different carbon nanomaterials only on the working electrodes of the platform. A second novelty is the growth obtained at complementary metal-oxide-semiconductor compatible temperatures. These novel electrodes have been incorporated in sensors in which performance characteristics improve with the content of nanostructures. Unprecedented sensing parameters with respect to both direct and enzyme-mediated electrochemical biodetection have been obtained.

Image thresholding techniques for localization of sub-resolution fluorescent biomarkers.

In this article, we explore adaptive global and local segmentation techniques for a lab-on-chip nutrition monitoring system (NutriChip). The experimental setup consists of Caco-2 intestinal cells that can be artificially stimulated to trigger an immune response. The eventual response is optically monitored using immunofluoresence techniques targeting toll-like receptor 2 (TLR2). Two problems of interest need to be addressed by means of image processing. First, a new cell sample must be properly classified as stimulated or not. Second, the location of the stained TLR2 must be recovered in case the sample has been stimulated. The algorithmic approach to solving these problems is based on the ability of a segmentation technique to properly segment fluorescent spots. The sample classification is based on the amount and intensity of the segmented pixels, while the various segmenting blobs provide an approximate localization of TLR2. A novel local thresholding algorithm and three well-known spot segmentation techniques are compared in this study. Quantitative assessment of these techniques based on real and synthesized data demonstrates the improved segmentation capabilities of the proposed algorithm.

Electrochemical detection of anti-breast-cancer agents in human serum by cytochrome P450-coated carbon nanotubes.

We report on the electrochemical detection of anti-cancer drugs in human serum with sensitivity values in the range of 8-925 nA/µM. Multi-walled carbon nanotubes were functionalized with three different cytochrome P450 isoforms (CYP1A2, CYP2B6, and CYP3A4). A model used to effectively describe the cytochrome P450 deposition onto carbon nanotubes was confirmed by Monte Carlo simulations. Voltammetric measurements were performed in phosphate buffer saline (PBS) as well as in human serum, giving well-defined current responses upon addition of increasing concentrations of anti-cancer drugs. The results assert the capability to measure concentration of drugs in the pharmacological ranges in human serum. Another important result is the possibility to detect pairs of drugs present in the same sample, which is highly required in case of therapies with high side-effects risk and in anti-cancer pharmacological treatments based on mixtures of different drugs. Our technology holds potentials for inexpensive multi-panel drug-monitoring in personalized therapy.

Fully integrated biochip platforms for advanced healthcare.

Recent advances in microelectronics and biosensors are enabling developments of innovative biochips for advanced healthcare by providing fully integrated platforms for continuous monitoring of a large set of human disease biomarkers. Continuous monitoring of several human metabolites can be addressed by using fully integrated and minimally invasive devices located in the sub-cutis, typically in the peritoneal region. This extends the techniques of continuous monitoring of glucose currently being pursued with diabetic patients. However, several issues have to be considered in order to succeed in developing fully integrated and minimally invasive implantable devices. These innovative devices require a high-degree of integration, minimal invasive surgery, long-term biocompatibility, security and privacy in data transmission, high reliability, high reproducibility, high specificity, low detection limit and high sensitivity. Recent advances in the field have already proposed possible solutions for several of these issues. The aim of the present paper is to present a broad spectrum of recent results and to propose future directions of development in order to obtain fully implantable systems for the continuous monitoring of the human metabolism in advanced healthcare applications.

Real-Time Multi-Ion-Monitoring Front-End With Interference Compensation by Multi-Output Support Vector Regressor.

Ion-sensors play a major role in physiology and healthcare monitoring since they are capable of continuously collecting biological data from body fluids. Nevertheless, ion interference from background electrolytes present in the sample is a paramount challenge for a precise multi-ion-monitoring. In this work, we propose the first system combining a battery-powered portable multi-channel electronic front-end, and an embedded Multi-output Support Vector Regressor (M-SVR), that supplies an accurate, continuous, and real-time monitoring of sodium, potassium, ammonium, and calcium ions. These are typical analytes tracked during physical exercise. The front-end interface was characterized through a sensor array built with screen-printed electrodes. Nernstian sensitivity and limit of detection comparable to a bulky laboratory potentiometer were achieved in both water and artificial sweat. The multivariate calibration model was deployed on a Raspberry Pi where the activity of the target ions were locally computed. The M-SVR model was trained, optimized, and tested on an experimental dataset acquired following a design of experiments. We demonstrate that the proposed multivariate regressor is a compact, low-complexity, accurate, and unbiased estimator of sodium and potassium ions activity. A global normalized root mean-squared error improvement of 6.97%, and global mean relative error improvement of 10.26%, were achieved with respect to a standard Multiple Linear Regressor (MLR). Within a real-time multi-ion-monitoring task, the overall system enabled the continuous monitoring and accurate determination of the four target ions activity, with an average accuracy improvement of 27.73% compared to a simple MLR, and a prediction latency of [Formula: see text].

Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier.

We present a new method for electrochemical sensing, which compensates the fouling effect of propofol through machine learning (ML) model. Direct and continuous monitoring of propofol is crucial in the development of automatic systems for control of drug infusion in anaesthesiology. The fouling effect on electrodes discourages the possibility of continuous online monitoring of propofol since polymerization of the surface produces sensor drift. Several approaches have been proposed to limit the phenomenon at the biochemical interface; instead, here, we present a novel ML-based calibration procedure. In this paper, we analyse a dataset of 600 samples acquired through staircase cyclic voltammetry (SCV), resembling the scenario of continuous monitoring of propofol, both in PBS and in undiluted human serum, to demonstrate that ML-based model solves electrode fouling of anaesthetics. The proposed calibration approach is based on Gaussian radial basis function support vector classifier (RBF-SVC) that achieves classification accuracy of 98.9% in PBS, and 100% in undiluted human serum. The results prove the ability of the ML-based model to correctly classify propofol concentration in the therapeutic range between 1µM and 60µM with levels of 10µM, continuously up to ten minutes, with one sample every 30s.

Modeling stochasticity and robustness in gene regulatory networks.

MOTIVATION: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. RESULTS: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. AVAILABILITY: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Highly-stable Li+ ion-selective electrodes based on noble metal nanostructured layers as solid-contacts.

Nowadays the development of stable and highly efficient Solid-Contact Ion-Selective Electrodes (SC-ISEs) attracts much attention in the research community because of the great expansion of portable analytical devices. In this work, we present highly stable Li+ all-solid-state ISEs exploiting noble metals nanostructures as ion-to-electron transducers. The detection of lithium is essential for therapeutic drug monitoring of bipolar patients. In addition, greater environmental exposure to this ion is occurring due to the large diffusion of lithium-ion batteries. However, only a limited number of SC Li+ ISEs already exists in literature based on Conductive Polymers (CPs) and carbon nanotubes. The use of noble metals for ion-to-electron transduction offers considerable advantages over CPs and carbon materials, including fast and conformal one-step deposition by electrochemical means, non-toxicity and high stability. We investigate for the first time the use of gold nanocorals obtained by means of a one-step electrodeposition process to improve sensor performance and we compare it to all-solid-state ISEs based on electrodeposited platinum nanoflowers. In addition, the effect of substrate electrode material, membrane thickness and conditioning concentration on the potentiometric response is carefully analysed. Scanning Electron Microscopy (SEM) and Current Reversal Chronopotentiometry (CRC) techniques are used to characterize the morphology and the electrochemical behaviour of the different ISEs. The use of nanostructured gold and platinum contacts allows the increase of the SC capacitance by one or two orders of magnitude, respectively, with respect to the flat metal, while the SC resistance is significantly reduced. We show that the microfabricated sensors offer Nernstian behaviour (58.7±0.8 mV/decade) in the activity range from 10-5 to 0.1 M, with short response time (∼15 s) and small potential drift during CRC measurements (dEdt=3×10-5±2×10-5 V/s). The exceptional response stability is verified also when no potential is applied. The sensor shows high selectivity towards all clinically important ions, with values very similar to conventional ISEs. Furthermore, to our knowledge, the selectivity towards Ca+2 is the best ever reported for SC-ISEs. In conclusion, the present study opens up new interesting perspectives towards the development of simple and reproducible fabrication protocols to obtain high-quality and high-stability all-solid-state ISEs.

Doping-Free Complementary Logic Gates Enabled by Two-Dimensional Polarity-Controllable Transistors.

Atomically thin two-dimensional (2D) materials belonging to transition metal dichalcogenides, due to their physical and electrical properties, are an exceptional vector for the exploration of next-generation semiconductor devices. Among them, due to the possibility of ambipolar conduction, tungsten diselenide (WSe2) provides a platform for the efficient implementation of polarity-controllable transistors. These transistors use an additional gate, named polarity gate, that, due to the electrostatic doping of the Schottky junctions, provides a device-level dynamic control of their polarity, that is, n- or p-type. Here, we experimentally demonstrate a complete doping-free standard cell library realized on WSe2 without the use of either chemical or physical doping. We show a functionally complete family of complementary logic gates (INV, NAND, NOR, 2-input XOR, 3-input XOR, and MAJ) and, due to the reconfigurable capabilities of the single devices, achieve the realization of highly expressive logic gates, such as exclusive-OR (XOR) and majority (MAJ), with fewer transistors than possible in conventional complementary metal-oxide-semiconductor logic. Our work shows a path to enable doping-free low-power electronics on 2D semiconductors, going beyond the concept of unipolar physically doped devices, while suggesting a road to achieve higher computational densities in two-dimensional electronics.

Towards Ultrasound Everywhere: A Portable 3D Digital Back-End Capable of Zone and Compound Imaging.

Ultrasound imaging is a ubiquitous diagnostic technique, but does not fit the requirements of the telemedicine approach, because it relies on the real-time manipulation and image recognition skills of a trained expert, called sonographer. Sonographers are only available in hospitals and clinics, negating or at least delaying access to ultrasound scans in many locales-rural areas, developing countries-as well as in medical rescue operations. Telesonography would require an advanced imager that supports three-dimensional (3-D) acquisition; this would allow untrained operators to acquire broad scans and upload them remotely for diagnosis. Such advanced imagers do exist, but do not meet several other requirements for telesonography, such as being portable, inexpensive, and sufficiently low power to enable battery operation. In this work, we present our prototype of the first portable 3-D digital ultrasound back-end system. The prototype is implemented in a single midrange Xilinx field programmable gate array (FPGA), for an estimated power consumption of 5 W. The device supports up to 1024 input channels, which is state of the art and could be scaled further, and supports multiple image reconstruction modes. We evaluate the resource utilization of the FPGA and provide various quality metrics to ascertain the output image quality.

An IoT Solution for Online Monitoring of Anesthetics in Human Serum Based on an Integrated Fluidic Bioelectronic System.

In this paper, we present the design, the implementation and the validation of a novel Internet of Things (IoT) drug monitoring system for the online continuous and simultaneous detection of two main anesthetics, e.g., propofol and paracetamol, in undiluted human serum. The described full system consists of a custom-built electronic Raspberry Pi (RPi) based Printed Circuit Board (PCB) that drives and reads out the signal from an electrochemical sensing platform integrated into a fluidic system. Thanks to the Polydimethylsiloxane (PDMS) fluidic device, the analyzed sample is automatically fluxed on the sensing site. The IoT network is supported by a Cloud system, which allows the doctor to control and share all the patient's data through a dedicated Android application and a smart watch. The validation closes with the first ever demonstration that our system successfully works for the simultaneous monitoring of propofol and paracetamol in undiluted human serum by measuring the concentration trends of these two drugs in fluxing conditions over time.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

Clustering protein environments for function prediction: finding PROSITE motifs in 3D.

BACKGROUND: Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified. RESULTS: We have prototyped a computational method for rapid clustering of millions of microenvironments in order to discover residues whose surrounding environments are similar and which may therefore share a functional or structural role. We clustered nearly 2,000,000 environments from 9,600 protein chains and defined 4,550 clusters. As a preliminary validation, we asked whether known 3D environments associated with PROSITE motifs were "rediscovered". We found examples of clusters highly enriched for residues that share PROSITE sequence motifs. CONCLUSION: Our results demonstrate that we can cluster protein environments successfully using a simplified representation and K-means clustering algorithm. The rediscovery of known 3D motifs allows us to calibrate the size and intercluster distances that characterize useful clusters. This information will then allow us to find new clusters with similar characteristics that represent novel structural or functional sites.

Co-clustering: a versatile tool for data analysis in biomedical informatics.

Co-clustering has not been much exploited in biomedical informatics, despite its success in other domains. Most of the previous applications were limited to analyzing gene expression data. We performed co-clustering analysis on other types of data and obtained promising results, as summarized in this paper.