Study of Strategies for Genetic Variant Discrimination and Detection by Optosensing

Tesis por compendio[ES] La medicina actual se dirige hacia un enfoque más personalizado basándose en el diagnóstico molecular del paciente a través del estudio de biomarcadores específicos. Aplicando este principio molecular, el diagnóstico, pronóstico y selección de la terapia se apoyan en la identificación de variaciones específicas del genoma humano, como variaciones de un único nucleótido (SNV). Para detectar estos biomarcadores se dispone de una amplia oferta de tecnologías. Sin embargo, muchos de los métodos en uso presentan limitaciones como un elevado coste, complejidad, tiempos de análisis largos o requieren de personal y equipamiento especializado, lo que imposibilita su incorporación masiva en la mayoría de los sistemas sanitarios. Por tanto, existe la necesidad de investigar y desarrollar soluciones analíticas que aporten información sobre las variantes genéticas y que se puedan implementar en diferentes escenarios del ámbito de la salud con prestaciones competitivas y económicamente viables. El objetivo principal de esta tesis ha sido desarrollar estrategias innovadoras para resolver el reto de la detección múltiple de variantes genéticas que se encuentran en forma minoritaria en muestras biológicas de pacientes, cubriendo las demandas asociadas al entorno clínico. Las tareas de investigación se centraron en la combinación de reacciones de discriminación alélica con amplificación selectiva de DNA y el desarrollo de sistemas ópticos de detección versátiles. Con el fin de atender el amplio abanico de necesidades, en el primer capítulo, se presentan resultados que mejoran las prestaciones analíticas de la reacción en cadena de la polimerasa (PCR) mediante la incorporación de una etapa al termociclado y de un agente bloqueante amplificando selectivamente las variantes minoritarias que fueron monitorizadas mediante fluorescencia a tiempo real. En el segundo capítulo, se logró la discriminación alélica combinando la ligación de oligonucleótidos con la amplificación de la recombinasa polimerasa (RPA), que al operar a temperatura constante permitió una detección tipo point-of-care (POC). La identificación de SNV se llevó a cabo mediante hibridación en formato micromatriz, utilizando la tecnología Blu-Ray como plataforma de ensayo y detección. En el tercer capítulo, se integró la RPA con la reacción de hibridación alelo especifica en cadena (AS-HCR), en formato array para genotipar SNV a partir de DNA genómico en un chip. La lectura de los resultados se realizó mediante un smartphone. En el último capítulo, se presenta la síntesis de un nuevo reactivo bioluminiscente que se aplicó a la monitorización de biomarcadores de DNA a tiempo real y final de la RPA basada en la transferencia de energía de resonancia de bioluminiscencia (BRET), eliminando la necesidad de una fuente de excitación. Todas las estrategias permitieron un reconocimiento especifico de la variante de interés, incluso en muestras que contenían tan solo 20 copias de DNA genómico diana. Se consiguieron resultados sensibles (límite de detección 0.5% variante/total), reproducibles (desviación estándar relativa < 19%), de manera sencilla (3 etapas o menos), rápida (tiempos cortos de 30-200 min) y permitiendo el análisis simultaneo de varios genes. Como prueba de concepto, estas estrategias se aplicaron a la detección e identificación en muestras clínicas de biomarcadores asociados a cáncer colorrectal y enfermedades cardiológicas. Los resultados se validaron por comparación con los métodos de referencia NGS y PCR, comprobándose que se mejoraban los requerimientos técnicos y la relación coste-eficacia. En conclusión, las investigaciones llevadas a cabo posibilitaron desarrollar herramientas de genotipado con propiedades analíticas competitivas y versátiles, aplicables a diferentes escenarios sanitarios, desde hospitales a entornos con pocos recursos. Estos resultados son prometedores al dar respuesta a la demanda de tecnologías alternativas para el diagnóstico molecular personalizado.[CA] La medicina actual es dirigeix cap a un enfocament més personalitzat basant-se en el diagnòstic molecular del pacient a través de l'estudi de biomarcadors específics. Aplicant aquest principi molecular, el diagnòstic, pronòstic i selecció de la teràpia es recolzen en la identificació de variacions específiques del genoma humà, com variacions d'un únic nucleòtid (SNV). Per a detectar aquests biomarcadors, es disposa d'una àmplia oferta de tecnologies. No obstant això, molts dels mètodes en ús presenten limitacions com un elevat cost, complexitat, temps d'anàlisis llargues o requereixen de personal i equipament especialitzat, la qual cosa impossibilita la seua incorporació massiva en la majoria dels sistemes sanitaris. Per tant, existeix la necessitat d'investigar i desenvolupar solucions analítiques que aporten informació sobre les variants genètiques i que es puguen implementar en diferents escenaris de l'àmbit de la salut amb prestacions competitives i econòmicament viables. L'objectiu principal d'aquesta tesi ha sigut desenvolupar estratègies innovadores per a resoldre el repte de la detecció múltiple de variants genètiques que es troben en forma minoritària en mostres biològiques de pacients, cobrint les demandes associades a l'entorn clínic. Les tasques d'investigació es van centrar en la combinació de reaccions de discriminació al·lèlica amb amplificació selectiva de DNA i al desenvolupament de sistemes òptics de detecció versàtils. Amb la finalitat d'atendre l'ampli ventall de necessitats, en el primer capítol, es presenten resultats que milloren les prestacions analítiques de la reacció en cadena de la polimerasa (PCR) mitjançant la incorporació d'una etapa al termociclat i d'un agent bloquejant amplificant selectivament les variants minoritàries que van ser monitoritzades mitjançant fluorescència a temps real. En el segon capítol, es va aconseguir la discriminació al·lèlica combinant el lligament d'oligonucleòtids amb l'amplificació de la recombinasa polimerasa (RPA), que en operar a temperatura constant va permetre una detecció tipus point-of-care (POC). La identificació de SNV es va dur a terme mitjançant hibridació en format micromatriu, utilitzant la tecnologia Blu-Ray com a plataforma d'assaig i detecció. En el tercer capítol, es va integrar la RPA amb la reacció d'hibridació al·lel específica en cadena (AS-HCR), en format matriu per a genotipar SNV a partir de DNA genòmic en un xip. La lectura dels resultats es va realitzar mitjançant un telèfon intel·ligent. En l'últim capítol, es presenta la síntesi d'un nou reactiu bioluminescent que es va aplicar al monitoratge de biomarcadors de DNA a temps real i final de la RPA basada en la transferència d'energia de ressonància de bioluminescència (BRET), eliminant la necessitat d'una font d'excitació. Totes les estratègies van permetre un reconeixement específic de la variant d'interès, fins i tot en mostres que només contenien 20 còpies de DNA genòmic diana. Es van aconseguir resultats sensibles (límit de detecció 0.5% variant/total), reproduïbles (desviació estàndard relativa < 19%), de manera senzilla (3 etapes o menys), ràpida (temps curts de 30-200 min) i permetent l'anàlisi simultània de diversos gens. Com a prova de concepte, aquestes estratègies es van aplicar a la detecció i identificació en mostres clíniques de biomarcadors associats a càncer colorectal i a malalties cardiològiques. Els resultats es van validar per comparació amb els mètodes de referència NGS i PCR, comprovant-se que es milloraven els requeriments tècnics i la relació cost-eficàcia. En conclusió, les investigacions dutes a terme van possibilitar desenvolupar eines de genotipat amb propietats analítiques competitives i versàtils, aplicables a diferents escenaris sanitaris, des d'hospitals a entorns amb pocs recursos. Aquests resultats són prometedors en donar resposta a la demanda de tecnologies alternatives per al diagnòstic molecular personalitzat.[EN] Current medicine is moving towards a more personalized approach based on the patients' molecular diagnosis through the study of specific biomarkers. Diagnosis, prognosis and therapy selection, applying this molecular principle, rely on identifying specific variations in the human genome, such as single nucleotide variations (SNV). A wide range of technologies is available to detect these biomarkers. However, many of the employed methods have limitations such as high cost, complexity, long analysis times, or requiring specialized personnel and equipment, making their massive incorporation in most healthcare systems impossible. Therefore, there is a need to research and develop analytical solutions that provide information on genetic variants that can be implemented in different health scenarios with competitive and economically feasible performances. The main objective of this thesis has been to develop innovative strategies to solve the challenge of multiple detection of genetic variants that are found in a minority amount in patient samples, covering the demands associated with the clinical setting. Research tasks focused on the combination of allelic discrimination reactions with selective DNA amplification and the development of versatile optical detection systems. In order to meet the wide range of needs, in the first chapter, the analytical performances of the polymerase chain reaction (PCR) were improved by incorporating a thermocycling step and a blocking agent to amplify selectively minority variants that were monitored by real-time fluorescence. In the second chapter, allelic discrimination was achieved by combining oligonucleotide ligation with recombinase polymerase amplification (RPA), which operates at a constant temperature, allowing point-of-care (POC) detection. SNV identification was carried out by hybridization in microarray format, using Blu-Ray technology as the assay platform and detector. RPA was integrated with allele-specific hybridization chain reaction (AS-HCR), in an array format to genotype SNV from genomic DNA on a chip in the third chapter. The reading of the results was performed using a smartphone. In the last chapter, a new bioluminescent reagent was synthesized. It was applied to real-time and endpoint DNA biomarker monitoring based on bioluminescence resonance energy transfer (BRET), eliminating the need for an excitation source. All the strategies allowed specific recognition of the target variant, even in samples containing as few as 20 copies of target genomic DNA. Sensitive (limit of detection 0.5% variant/total), reproducible (relative standard deviation < 19%), simple (3 steps or less), fast (short times of 30-200 min) results were achieved, allowing simultaneous analysis of several genes. As proof of concept, these strategies were applied to detect and identify biomarkers associated with colorectal cancer and cardiological diseases in clinical samples. The results were validated by comparison with reference methods such as NGS and PCR, proving that the technical requirements and cost-effectiveness were improved. In conclusion, the developed research made it possible to develop genotyping tools with competitive analytical properties and versatile, applicable to different healthcare scenarios, from hospitals to limited-resource environments. These results are promising since they respond to the demand for alternative technologies for personalized molecular diagnostics.The authors acknowledge the financial support received from the Generalitat Valenciana PROMETEO/2020/094, GRISOLIA/2014/024 PhD Grant and GVA-FPI-2017 PhD grant, the Spanish Ministry of Economy and Competitiveness MINECO projects CTQ2016-75749-R and PID2019-110713RB-I00 and European Regional Development Fund (ERDF).Lázaro Zaragozá, A. (2022). Study of Strategies for Genetic Variant Discrimination and Detection by Optosensing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185216TESISCompendi

The Internet of Things Will Thrive by 2025

This report is the latest research report in a sustained effort throughout 2014 by the Pew Research Center Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-LeeThis current report is an analysis of opinions about the likely expansion of the Internet of Things (sometimes called the Cloud of Things), a catchall phrase for the array of devices, appliances, vehicles, wearable material, and sensor-laden parts of the environment that connect to each other and feed data back and forth. It covers the over 1,600 responses that were offered specifically about our question about where the Internet of Things would stand by the year 2025. The report is the next in a series of eight Pew Research and Elon University analyses to be issued this year in which experts will share their expectations about the future of such things as privacy, cybersecurity, and net neutrality. It includes some of the best and most provocative of the predictions survey respondents made when specifically asked to share their views about the evolution of embedded and wearable computing and the Internet of Things

Face Liveness Detection under Processed Image Attacks

Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques

Sensor-Based Covert Channels on Mobile Devices

Smartphones have become ubiquitous in our daily activities, having billions of active users worldwide. The wide range of functionalities of modern mobile devices is enriched by many embedded sensors. These sensors, accessible by third-party mobile applications, pose novel security and privacy threats to the users of the devices. Numerous research works demonstrate that user keystrokes, location, or even speech can be inferred based on sensor measurements. Furthermore, the sensor itself can be susceptible to external physical interference, which can lead to attacks on systems that rely on sensor data. In this dissertation, we investigate how reaction of sensors in mobile devices to malicious physical interference can be exploited to establish covert communication channels between otherwise isolated devices or processes. We present multiple covert channels that use sensors’ reaction to electromagnetic and acoustic interference to transmit sensitive data from nearby devices with no dedicated equipment or hardware modifications. In addition, these covert channels can also transmit information between applications within a mobile device, breaking the logical isolation enforced by the operating system. Furthermore, we discuss how sensor-based covert channels can affect privacy of end users by tracking their activities on two different devices or across two different applications on the same device. Finally, we present a framework that automatically identifies covert channels that are based on physical interference between hardware components of mobile devices. As a result of the experimental evaluation, we can confirm previously known covert channels on smartphones, and discover novel sources of cross-component interference that can be used to establish covert channels. Focusing on mobile platforms in this work, we aim to show that it is of crucial importance to consider physical covert channels when assessing the security of the systems that rely on sensors, and advocate for holistic approaches that can proactively identify and estimate corresponding security and privacy risks

Biosensing by “Growing” Antennas and Error-correcting Codes

Food-borne disease outbreaks not only cause numerous fatalities every year but also contribute to significant economic losses. While end-to-end supply chain monitoring can be one of the keys to preventing these outbreaks, screening every food product in the supply chain is not feasible considering the sheer volume and prohibitive test costs. Fortunately, two converging economic trends promise to make this end-to-end supply chain monitoring possible. The first trend is that passive radio-frequency identification (RFID) tags and quick response (QR) codes are now widely accepted for food packaging. The second trend is that smartphones are now equipped with the capability to interrogate RFID tags or to decode QR codes. Together, they have opened up the possibility of monitoring food quality by endowing these tags and error-correcting codes with the capability to detect pathogenic contaminants. This dissertation investigates a biosensing paradigm of growing\u27\u27 transducer structures, such as RFID tags and QR codes, which is triggered only when analytes of interest are present in the sample. This transducer growth or self-assembly process relies on a silver enhancement technique through which silver ions reduce into metallic form in the presence of a target analyte, which in turn leads to changes in electrical or optical properties. By exploiting this, we first demonstrate two remote biosensor platforms, a RFID tag-based biosensor and a QR code-based biosensor, respectively. For the RFID-based biosensor, a chain of silver-shelled particles is assembled during the analyte detection process, which directly modulates the antenna\u27s effective impedance, and hence leads to an improvement in the tag\u27s reflection efficiency. For the QR code-based biosensor, the operating principle relies on the optical absorption changes resulting from silver enhancement. The target detection process assembles an invalid code-word into a valid QR code. This self-assembly sensing approach should produce few false positives since it is a process which transits from a high entropy state (disassembled transducer) to a low entropy state (assembled transducer). While there can be numerous states of a disassembled transducer structure, there are only a few configurations representing the assembled transducer state. Given that there are no active power sources on the RFID tag or the QR code, it is challenging for the proposed biosensors to perform sample acquisition and pre-processing since they are envisioned to be embedded inside food packages eventually. Paper-based microfluidics have been explored and integrated on the biosensors to provide a self-powered approach for reagent sampling and processing. One use case is to trigger target detection remotely by an end consumer. Thermal absorption properties of graphite have been exploited such that the end user can initiate the process of analyte sampling in paper-based biosensors by shining a beam of light on the sensor