A Versatile Sensor Data Processing Framework for Resource Technology

Die Erweiterung experimenteller Infrastrukturen um neuartige Sensor eröffnen die Möglichkeit, qualitativ neuartige Erkenntnisse zu gewinnen. Um diese Informationen vollständig zu erschließen ist ein Abdecken der gesamten Verarbeitungskette von der Datenauslese bis zu anwendungsbezogenen Auswertung erforderlich. Eine Erweiterung bestehender wissenschaftlicher Instrumente beinhaltet die strukturelle und zeitbezogene Integration der neuen Sensordaten in das Bestandssystem. Das hier vorgestellte Framework bietet durch seinen flexiblen Ansatz das Potenzial, unterschiedliche Sensortypen in unterschiedliche, leistungsfähige Plattformen zu integrieren. Zwei unterschiedliche Integrationsansätze zeigen die Flexibilität dieses Ansatzes, wobei einer auf die Steigerung der Sensitivität einer Anlage zur Sekundärionenmassenspektroskopie und der andere auf die Bereitstellung eines Prototypen zur Untersuchung von Rezyklaten ausgerichtet ist. Die sehr unterschiedlichen Hardwarevoraussetzungen und Anforderungen der Anwendung bildeten die Basis zur Entwicklung eines flexiblen Softwareframeworks. Um komplexe und leistungsfähige Applikationsbausteine bereitzustellen wurde eine Softwaretechnologie entwickelt, die modulare Pipelinestrukturen mit Sensor- und Ausgabeschnittstellen sowie einer Wissensbasis mit entsprechenden Konfigurations- und Verarbeitungsmodulen kombiniert.:1. Introduction 2. Hardware Architecture and Application Background 3. Software Concept 4. Experimental Results 5. Conclusion and OutlookNovel sensors with the ability to collect qualitatively new information offer the potential to improve experimental infrastructure and methods in the field of research technology. In order to get full access to this information, the entire range from detector readout data transfer over proper data and knowledge models up to complex application functions has to be covered. The extension of existing scientific instruments comprises the integration of diverse sensor information into existing hardware, based on the expansion of pivotal event schemes and data models. Due to its flexible approach, the proposed framework has the potential to integrate additional sensor types and offers migration capabilities to high-performance computing platforms. Two different implementation setups prove the flexibility of this approach, one extending the material analyzing capabilities of a secondary ion mass spectrometry device, the other implementing a functional prototype setup for the online analysis of recyclate. Both setups can be regarded as two complementary parts of a highly topical and ground-breaking unique scientific application field. The requirements and possibilities resulting from different hardware concepts on one hand and diverse application fields on the other hand are the basis for the development of a versatile software framework. In order to support complex and efficient application functions under heterogeneous and flexible technical conditions, a software technology is proposed that offers modular processing pipeline structures with internal and external data interfaces backed by a knowledge base with respective configuration and conclusion mechanisms.:1. Introduction 2. Hardware Architecture and Application Background 3. Software Concept 4. Experimental Results 5. Conclusion and Outloo

Modelling and Simulation of Multi-target Multi-sensor Data Fusion for Trajectory Tracking

An implementation of track fusion using various algorthims has been demonstrated . The sensor measurements of these targets are modelled using Kalman filter (KF) and interacting multiple models (IMM) filter. The joint probabilistic data association filter (JPDAF) and neural network fusion (NNF) algorithms were used for tracking multiple man-euvring targets. Track association and fusion algorithm are executed to get the fused track data for various scenarios, two sensors tracking a single target to three sensors tracking three targets, to evaluate the effects of multiple and dispersed sensors for single target, two targets, and multiple targets. The targets chosen were distantly spaced, closely spaced and crossing. Performance of different filters was compared and fused trajectory is found to be closer to the true target trajectory as compared to that for any of the sensor measurements of that target.Defence Science Journal, 2009, 59(3), pp.205-214, DOI:http://dx.doi.org/10.14429/dsj.59.151

Dual-function nanoparticles enzymatically conjugated with a custom-made polyurethane hydrogel for chronic wound treatment

Hydrogels are attractive drug delivery systems with the potential to protect their cargo and control its release. In particular, hydrogels based on synthetic polymers are gaining increasing interest by virtue of their controllable chemistry, ease of modification, and reproducibility. Moreover, the presence of specific side chains and pending functional groups in the polymer structure allows for the conjugation of drugs and other compounds resulting in improved control over drug release. Enzymes that catalyse reactions in a very specific way could also be used to control the conjugation of compounds to the polymeric chains to improve reproducibility and biocompatibility of the conjugation process. This contribution describes an innovative system for drug delivery comprising a bioartificial supramolecular hydrogel based on a customised polyurethane and α- cyclodextrins, and nanoparticles, for application in the treatment of chronic wounds. The system has the potential to reduce inflammation and eradicate infection by virtue of dual-function nanoparticles which incorporate cobalt as antimicrobial agent, and phenolated lignin as antioxidant. The nanoparticles are enzymatically conjugated to the hydrogel by means of the amine side groups exposed along the backbone of the ad-hoc synthesised polyurethane. The oxidase enzyme laccase is exploited to oxidize the phenol groups of lignin, to allow their interaction with the amines on the hydrogel. The effects of nanoparticles conjugation to the hydrogel are studied through gelification tests, stability tests, and rheology. Moreover, the release of nanoparticles from the hydrogel and their effects on patients’ wound fluids and against relevant bacterial strains are analysed in vitro

Solvent-free nanoparticles synthesis for encapsulation of water-soluble compounds

The increasing concerns on green manufacturing practices with low environmental impact have put pressure on the pharmaceutical industry. Of particular concern is the large number of organic solvents used in a wide range of pharmaceutical products, posing a significant risk on human and environmental health. Recently, nanoparticles(NPs) emerged as advantageous drug-delivery systems with the potential to maximize drug efficacy and minimize side effects. Unfortunately, NPs synthesis processes are still environmentally unsustainable, due to the large amount of organic solvent involved. This contribution describes the synthesis and characterization of NPs encapsulating proteins or nucleic acids for metastatic melanoma treatment using alternative synthesis methods that replace organic solvents with water-based solutions. Two different green synthesis techniques have been investigated; (i)Ionic gelation was used to prepare chitosan(CS) NPs, exploiting the electrostatic interaction between the CS amino groups and tripolyphosphate(TPP), (ii)self-assembly technique to prepare siRNA/phosphate-poly(allylamine-hydrochloride)(PAH) NPs, exploiting the interactions between primary amines in the polymer and siRNAs to form stable complexes. CS and PAH NPs with the appropriate hydrodynamic diameters (~200 nm), polydispersity index, and Z potential for high cell internalization and tissue extravasation were obtained. Preliminary in vitro tests demonstrated that particles are well tolerated by human fibroblast which has shown high viability even when treated with the highest NPs concentration (viability ~85% at 48h from the treatment). Additional tests are currently ongoing to demonstrate the efficacy of the drug- loaded system on human fibroblasts. Carlotta Mattioda acknowledges PON "Ricerca e Innovazione" 2014-2020 Azione IV.R "dottorati su tematiche green" for co-financing her Ph.D scholarship

Genetically encoded intrabody sensors report the interaction and trafficking of β-arrestin 1 upon activation of G protein-coupled receptors

Agonist stimulation of G protein-coupled receptors (GPCRs) typically leads to phosphorylation of GPCRs and binding to multifunctional proteins called β-arrestins (βarrs). The GPCR-βarr interaction critically contributes to GPCR desensitization, endocytosis, and downstream signaling, and GPCR-βarr complex formation can be used as a generic readout of GPCR and βarr activation. Although several methods are currently available to monitor GPCR-βarr interactions, additional sensors to visualize them may expand the toolbox and complement existing methods. We have previously described antibody fragments (FABs) that recognize activated βarr1 upon its interaction with the vasopressin V2 receptor C-terminal phosphopeptide (V2Rpp). Here, we demonstrate that these FABs efficiently report the formation of a GPCR-βarr1 complex for a broad set of chimeric GPCRs harboring the V2R C terminus. We adapted these FABs to an intrabody format by converting them to single-chain variable fragments (ScFvs) and used them to monitor the localization and trafficking of βarr1 in live cells. We observed that upon agonist simulation of cells expressing chimeric GPCRs, these intrabodies first translocate to the cell surface, followed by trafficking into intracellular vesicles. The translocation pattern of intrabodies mirrored that of βarr1, and the intrabodies co-localized with βarr1 at the cell surface and in intracellular vesicles. Interestingly, we discovered that intrabody sensors can also report βarr1 recruitment and trafficking for several unmodified GPCRs. Our characterization of intrabody sensors for βarr1 recruitment and trafficking expands currently available approaches to visualize GPCR-βarr1 binding, which may help decipher additional aspects of GPCR signaling and regulation

The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN)

Algorithms for sensor validation and multisensor fusion

Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single 'best sensor'. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate

Development of novel orthogonal genetic circuits, based on extracytoplasmic function (ECF) σ factors

The synthetic biology field aims to apply the engineering 'design-build-test-learn' cycle for the implementation of synthetic genetic circuits modifying the behavior of biological systems. In order to reach this goal, synthetic biology projects use a set of fully characterized biological parts that subsequently are assembled into complex synthetic circuits following a rational, model-driven design. However, even though the bottom-up design approach represents an optimal starting point to assay the behavior of the synthetic circuits under defined conditions, the rational design of such circuits is often restricted by the limited number of available DNA building blocks. These usually consist only of a handful of transcriptional regulators that additionally are often borrowed from natural biological systems. This, in turn, can lead to cross-reactions between the synthetic circuit and the host cell and eventually to loss of the original circuit function. Thus, one of the challenges in synthetic biology is to design synthetic circuits that perform the designated functions with minor cross-reactions (orthogonality). To overcome the restrictions of the widely used transcriptional regulators, this project aims to apply extracytoplasmic function (ECF) σ factors in the design novel orthogonal synthetic circuits. ECFs are the smallest and simplest alternative σ factors that recognize highly specific promoters. ECFs represent one of the most important mechanisms of signal transduction in bacteria, indeed, their activity is often controlled by anti-σ factors. Even though it was shown that the overexpression of heterologous anti-σ factors can generate an adverse effect on cell growth, they represent an attractive solution to control ECF activity. Finally, to date, we know thousands of ECF σ factors, widespread among different bacterial phyla, that are identifiable together with the cognate promoters and anti-σ factors, using bioinformatic approaches. All the above-mentioned features make ECF σ factors optimal candidates as core orthogonal regulators for the design of novel synthetic circuits. In this project, in order to establish ECF σ factors as standard building blocks in the synthetic biology field, we first established a high throughput experimental setup. This relies on microplate reader experiments performed using a highly sensitive luminescent reporter system. Luminescent reporters have a superior signal-to-noise ratio when compared to fluorescent reporters since they do not suffer from the high auto-fluorescence background of the bacterial cell. However, they also have a drawback represented by the constant light emission that can generate undesired cross-talk between neighboring wells on a microplate. To overcome this limitation, we developed a computational algorithm that corrects for luminescence bleed-through and estimates the “true” luminescence activity for each well of a microplate. We show that the correcting algorithm preserves low-level signals close to the background and that it is universally applicable to different experimental conditions. In order to simplify the assembly of large ECF-based synthetic circuits, we designed an ECF toolbox in E. coli. The toolbox allows for the combinatorial assembly of circuits into expression vectors, using a library of reusable genetic parts. Moreover, it also offers the possibility of integrating the newly generated synthetic circuits into four different phage attachment (att) sites present in the genome of E. coli. This allows for a flawless transition between plasmid-encoded and chromosomally integrated genetic circuits, expanding the possible genetic configurations of a given synthetic construct. Moreover, our results demonstrate that the four att sites are orthogonal in terms of the gene expression levels of the synthetic circuits. With the purpose of rationally design ECF-based synthetic circuits and taking advantage of the ECF toolbox, we characterized the dynamic behavior of a set of 15 ECF σ factors, their cognate promoters, and relative anti-σs. Overall, we found that ECFs are non-toxic and functional and that they display different binding affinities for the cognate target promoters. Moreover, our results show that it is possible to optimize the output dynamic range of the ECF-based switches by changing the copy number of the ECFs and target promoters, thus, tuning the input/output signal ratio. Next, by combining up to three ECF-switches, we generated a set of “genetic-timer circuits”, the first synthetic circuits harboring more than one ECF. ECF-based timer circuits sequentially activate a series of target genes with increasing time delays, moreover, the behavior of the circuits can be predicted by a set of mathematical models. In order to improve the dynamic response of the ECF-based constructs, we introduced anti-σ factors in our synthetic circuits. By doing so we first confirmed that anti-σ factors can exert an adverse effect on the growth of E. coli, thus we explored possible solutions. Our results demonstrate that anti-σ factors toxicity can be partially alleviated by generating truncated, soluble variants of the anti-σ factors and, eventually, completely abolished via chromosomal integration of the anti-σ factor-based circuits. Finally, after demonstrating that anti-σ factors can be used to generate a tunable time delay among ECF expression and target promoter activation, we designed ECF/AS-suicide circuits. Such circuits allow for the time-delayed cell-death of E. coli and will serve as a prototype for the further development of ECF/AS-based lysis circuits

Synthetic biology tools for environmental protection

Synthetic biology transforms the way we perceive biological systems. Emerging technologies in this field affect many disciplines of science and engineering. Traditionally, synthetic biology approaches were commonly aimed at developing cost-effective microbial cell factories to produce chemicals from renewable sources. Based on this, the immediate beneficial impact of synthetic biology on the environment came from reducing our oil dependency. However, synthetic biology is starting to play a more direct role in environmental protection. Toxic chemicals released by industries and agriculture endanger the environment, disrupting ecosystem balance and biodiversity loss. This review highlights synthetic biology approaches that can help environmental protection by providing remediation systems capable of sensing and responding to specific pollutants. Remediation strategies based on genetically engineered microbes and plants are discussed. Further, an overview of computational approaches that facilitate the design and application of synthetic biology tools in environmental protection is presented