Understanding ten-eleven translocation-2 in hematological and nervous systems

I proposed the study of two distinct aspects of Ten-Eleven Translocation 2 (TET2) protein for understanding specific functions in different body systems. ^ In Part I, I characterized the molecular mechanisms of Tet2 in the hematological system. As the second member of Ten-Eleven Translocation protein family, TET2 is frequently mutated in leukemic patients. Previous studies have shown that the TET2 mutations frequently occur in 20% myelodysplastic syndrome/myeloproliferative neoplasm (MDS/MPN), 10% T-cell lymphoma leukemia and 2% B-cell lymphoma leukemia. Genetic mouse models also display distinct phenotypes of various types of hematological malignancies. I performed 5-hydroxymethylcytosine (5hmC) chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq) of hematopoietic stem/progenitor cells to determine whether the deletion of Tet2 can affect the abundance of 5hmC at myeloid, T-cell and B-cell specific gene transcription start sites, which ultimately result in various hematological malignancies. Subsequent Exome sequencing (Exome-Seq) showed that disease-specific genes are mutated in different types of tumors, which suggests that TET2 may protect the genome from being mutated. The direct interaction between TET2 and Mutator S Homolog 6 (MSH6) protein suggests TET2 is involved in DNA mismatch repair. Finally, in vivo mismatch repair studies show that the loss of Tet2 causes a mutator phenotype. Taken together, my data indicate that TET2 binds to MSH6 to protect genome integrity. ^ In Part II, I intended to better understand the role of Tet2 in the nervous system. 5-hydroxymethylcytosine regulates epigenetic modification during neurodevelopment and aging. Thus, Tet2 may play a critical role in regulating adult neurogenesis. To examine the physiological significance of Tet2 in the nervous system, I first showed that the deletion of Tet2 reduces the 5hmC levels in neural stem cells. Mice lacking Tet2 show abnormal hippocampal neurogenesis along with 5hmC alternations at different gene promoters and corresponding gene expression downregulation. Through the luciferase reporter assay, two neural factors Neurogenic differentiation 1 (NeuroD1) and Glial fibrillary acidic protein (Gfap) were down-regulated in Tet2 knockout cells. My results suggest that Tet2 regulates neural stem/progenitor cell proliferation and differentiation in adult brain.^

The vulnerability of aquatic systems of the Upper Napo River Basin (Ecuadorian Amazon) to human activities

Aquatic ecosystems exhibit different vulnerabilities to anthropogenic disturbances. I examined this problem in the Upper Napo River Basin (UNRB), Ecuador. I ranked from 1 to 5 aquatic ecosystem uniqueness, health and threats. I stratified the basin into five Ecological Drainage Units (EDU), 48 Aquatic Ecological Systems (AES), and 203 macrohabitats. I found main threats (habitat conversion/degradation, land development, mining, oil industries, and water diversion) cover 54% of the UNRB, but have different scores and extents in each EDU. I assessed the health of 111 AESs, under three land use treatments, by analyzing the streamside zone, physical forms, water quality, aquatic life, and hydrology. Overall, health of AESs varied from 5 to 2.58, with 5 being the highest level of health. Threats and health of AESs were inversely related (F=34.119, P

Through Wafer 3D Vertical Micro-Coaxial Probe for High Frequency Material Characterization and Millimeter Wave Packaging Systems

This work presents the development of an in-plane vertical micro-coaxial probe using bulk micromachining technique for high frequency material characterization. The coaxial probe was fabricated in a silicon substrate by standard photolithography and a deep reactive ion etching (DRIE) technique. The through-hole structure in the form of a coaxial probe was etched and metalized with a diluted silver paste. A co-planar waveguide configuration was integrated with the design to characterize the probe. The electrical and RF characteristics of the coaxial probe were determined by simulating the probe design in Ansoft’s High Frequency Structure Simulator (HFSS). The reflection coefficient and transducer gain performance of the probe was measured up to 65 GHz using a vector network analyzer (VNA). The probe demonstrated excellent results over a wide frequency band, indicating its ability to integrate with millimeter wave packaging systems as well as characterize unknown materials at high frequencies. The probe was then placed in contact with 3 materials where their unknown permittivities were determined. To accomplish this, the coaxial probe was placed in contact with the material under test and electromagnetic waves were directed to the surface using the VNA, where its reflection coefficient was then determined over a wide frequency band from dc-to -65GHz. Next, the permittivity of each material was deduced from its measured reflection coefficients using a cross ratio invariance coding technique. The permittivity results obtained when measuring the reflection coefficient data were compared to simulated permittivity results and agreed well. These results validate the use of the micro-coaxial probe to characterize the permittivity of unknown materials at high frequencies up to 65GHz.

DIGITAL HISTORY: DESIGNING VIRTUAL ENVIRONMENTS THAT SUPPORT CRITICAL THINKING AND IDEA EXCHANGE, MOTIVATE AND ENGAGE USERS

Thesis (Master, Education) -- Queen's University, 2016-08-29 15:56:53.748

MARS (Media Asset Retrieval System): Puget Sound’s Digital Archive

The MARS (Media Asset Retrieval System) Project is the collaborative effort of public broadcasters,libraries and schools in the Puget Sound region to create a digital online resource that provides access to content produced by public broadcasters via the public libraries. Convergence ConsortiumThe Convergence Consortium is a model for community collaboration, including organizations such as public broadcasters, libraries, museums, and schools in the Puget Sound region to assess the needs of their constituents and pool resources to develop solutions to meet those needs. Specifically, the archives of public broadcasters have been identified as significant resources for the local communities and nationally. These resources can be accessed on the broadcasters websites, and through libraries and used by schools, and integrated with text and photographic archives from other partners.MARS’ goalCreate an online resource that provides effective access to the content produced locally by KCTS (Seattle PBS affiliate) and KUOW (Seattle NPR affiliate). The broadcasts will be made searchable using the CPB Metadata Element Set (under development) and controlled vocabularies (to be developed). This will ensure a user friendly search and navigation mechanism and user satisfaction.Furthermore, the resource can search the local public library’s catalog concurrently and provide the user with relevant TV material, radio material, and books on a given subject.The ultimate goal is to produce a model that can be used in cities around the country.The current phase of the project assesses the community’s need, analyzes the current operational systems, and makes recommendations for the design of the resource.Deliverables• Literature review of the issues surrounding the organization, description and representation of media assets• Needs assessment report of internal and external stakeholders• Profile of the systems in the area of managing and organizing media assetsfor public broadcasting nationwideActivities• Analysis of information seeking behavior• Analysis of collaboration within the respective organizations• Analysis of the scope and context of the proposed system• Examining the availability of information resources and exchangeof resources among users

Above ground biomass functions with vegetation indices for multiple use systems of two evergreen oaks

Remote sensing is a promising approach for above ground biomass estimation, as forest parameters can be obtained indirectly. The analysis in space and time is quite straight forward due to the flexibility of the method to determine forest crown parameters with remote sensing. It can be used to evaluate and monitoring for example the development of a forest area in time and the impact of disturbances, such as silvicultural practices or deforestation. The vegetation indices, which condense data in a quantitative numeric manner, have been used to estimate several forest parameters, such as the volume, basal area and above ground biomass. The objective of this study was the development of allometric functions to estimate above ground biomass using vegetation indices as independent variables. The vegetation indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Soil-Adjusted Vegetation Index (SAVI). QuickBird satellite data, with 0.70 m of spatial resolution, was orthorectified, geometrically and atmospheric corrected, and the digital number were converted to top of atmosphere reflectance (ToA). Forest inventory data and published allometric functions at tree level were used to estimate above ground biomass per plot. Linear functions were fitted for the monospecies and multispecies stands of two evergreen oaks (Quercus suber and Quercus rotundifolia) in multiple use systems, montados. The allometric above ground biomass functions were fitted considering the mean and the median of each vegetation index per grid as independent variable. Species composition as a dummy variable was also considered as an independent variable. The linear functions with better performance are those with mean NDVI or mean SR as independent variable. Noteworthy is that the two better functions for monospecies cork oak stands have median NDVI or median SR as independent variable. When species composition dummy variables are included in the function (with stepwise regression) the best model has median NDVI as independent variable. The vegetation indices with the worse model performance were EVI and SAVI.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Smart Farming in Italian agriculture: essays on adoption and diffusion dynamics shaping the agricultural digital transition

Smart Farming Technologies (SFT) is a term used to define the set of digital technologies able not only to control and manage the farm system, but also to connect it to the many disruptive digital applications posed at multiple links along the value chain. The adoption of SFT has been so far limited, with significant differences at country-levels and among different types of farms and farmers. The objective of this thesis is to analyze what factors contributes to shape the agricultural digital transition and to assess its potential impacts in the Italian agri-food system. Specifically, this overall research objective is approached under three different perspectives. Firstly, we carry out a review of the literature that focuses on the determinants of adoption of farm-level Management Information Systems (MIS), namely the most adopted smart farming solutions in Italy. Secondly, we run an empirical analysis on what factors are currently shaping the adoption of SFT in Italy. In doing so, we focus on the multi-process and multi-faceted aspects of the adoption, by overcoming the one-off binary approach often used to study adoption decisions. Finally, we adopt a forward-looking perspective to investigate what the socio-ethical implications of a diffused use of SFT might be. On the one hand, our results indicate that bigger, more structured farms with higher levels of commercial integration along the agri-food supply chain are those more likely to be early adopters. On the other hand, they highlight the need for the institutional and organizational environment around farms to more effectively support farmers in the digital transition. Moreover, the role of several other actors and actions are discussed and analyzed, by highlighting the key role of specific agri-food stakeholders and ad-hoc policies, with the aim to propose a clearer path towards an efficient, fair and inclusive digitalization of the agrifood sector.

Numerical and experimental analysis of 3D printed smart structures and systems

Three dimensional (3D) printers of continuous fiber reinforced composites, such as MarkTwo (MT) by Markforged, can be used to manufacture such structures. To date, research works devoted to the study and application of flexible elements and CMs realized with MT printer are only a few and very recent. A good numerical and/or analytical tool for the mechanical behavior analysis of the new composites is still missing. In addition, there is still a gap in obtaining the material properties used (e.g. elastic modulus) as it is usually unknown and sensitive to printing parameters used (e.g. infill density), making the numerical simulation inaccurate. Consequently, the aim of this thesis is to present several work developed. The first is a preliminary investigation on the tensile and flexural response of Straight Beam Flexures (SBF) realized with MT printer and featuring different interlayer fiber volume-fraction and orientation, as well as different laminate position within the sample. The second is to develop a numerical analysis within the Carrera' s Unified Formulation (CUF) framework, based on component-wise (CW) approach, including a novel preprocessing tool that has been developed to account all regions printed in an easy and time efficient way. Among its benefits, the CUF-CW approach enables building an accurate database for collecting first natural frequencies modes results, then predicting Young' s modulus based on an inverse problem formulation. To validate the tool, the numerical results are compared to the experimental natural frequencies evaluated using a digital image correlation method. Further, we take the CUF-CW model and use static condensation to analyze smart structures which can be decomposed into a large number of similar components. Third, the potentiality of MT in combination with topology optimization and compliant joints design (CJD) is investigated for the realization of automated machinery mechanisms subjected to inertial loads.

User scheduling for low earth orbit multi-user multiple-input-multiple-output non-terrestrial network systems

In rural and isolated areas without cellular coverage, Satellite Communication (SatCom) is the best candidate to complement terrestrial coverage. However, the main challenge for future generations of wireless networks will be to meet the growing demand for new services while dealing with the scarcity of frequency spectrum. As a result, it is critical to investigate more efficient methods of utilizing the limited bandwidth; and resource sharing is likely the only choice. The research community’s focus has recently shifted towards the interference management and exploitation paradigm to meet the increasing data traffic demands. In the Downlink (DL) and Feedspace (FS), LEO satellites with an on-board antenna array can offer service to numerous User Terminals (UTs) (VSAT or Handhelds) on-ground in FFR schemes by using cutting-edge digital beamforming techniques. Considering this setup, the adoption of an effective user scheduling approach is a critical aspect given the unusually high density of User terminals on the ground as compared to the on-board available satellite antennas. In this context, one possibility is that of exploiting clustering algorithms for scheduling in LEO MU-MIMO systems in which several users within the same group are simultaneously served by the satellite via Space Division Multiplexing (SDM), and then these different user groups are served in different time slots via Time Division Multiplexing (TDM). This thesis addresses this problem by defining a user scheduling problem as an optimization problem and discusses several algorithms to solve it. In particular, focusing on the FS and user service link (i.e., DL) of a single MB-LEO satellite operating below 6 GHz, the user scheduling problem in the Frequency Division Duplex (FDD) mode is addressed. The proposed State-of-the-Art scheduling approaches are based on graph theory. The proposed solution offers high performance in terms of per-user capacity, Sum-rate capacity, SINR, and Spectral Efficiency.

[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.

High-resolution stratigraphy and seismic site characterization of late Quaternary paleovalley systems (Adriatic coastal plain, Italy)

In highly urbanized coastal lowlands, effective site characterization is crucial for assessing seismic risk. It requires a comprehensive stratigraphic analysis of the shallow subsurface, coupled with the precise assessment of the geophysical properties of buried deposits. In this context, late Quaternary paleovalley systems, shallowly buried fluvial incisions formed during the Late Pleistocene sea-level fall and filled during the Holocene sea-level rise, are crucial for understanding seismic amplification due to their soft sediment infill and sharp lithologic contrasts. In this research, we conducted high-resolution stratigraphic analyses of two regions, the Pescara and Manfredonia areas along the Adriatic coastline of Italy, to delineate the geometries and facies architecture of two paleovalley systems. Furthermore, we carried out geophysical investigations to characterize the study areas and perform seismic response analyses. We tested the microtremor-based horizontal-to-vertical spectral ratio as a mapping tool to reconstruct the buried paleovalley geometries. We evaluated the relationship between geological and geophysical data and identified the stratigraphic surfaces responsible for the observed resonances. To perform seismic response analysis of the Pescara paleovalley system, we integrated the stratigraphic framework with microtremor and shear wave velocity measurements. The seismic response analysis highlights strong seismic amplifications in frequency ranges that can interact with a wide variety of building types. Additionally, we explored the applicability of artificial intelligence in performing facies analysis from borehole images. We used a robust dataset of high-resolution digital images from continuous sediment cores of Holocene age to outline a novel, deep-learning-based approach for performing automatic semantic segmentation directly on core images, leveraging the power of convolutional neural networks. We propose an automated model to rapidly characterize sediment cores, reproducing the sedimentologist's interpretation, and providing guidance for stratigraphic correlation and subsurface reconstructions.

Instrument transformers with Digital outputting high voltage power networks

In recent years, energy modernization has focused on smart engineering advancements. This entails designing complicated software and hardware for variable-voltage digital substations. A digital substation consists of electrical and auxiliary devices, control and monitoring devices, computers, and control software. Intelligent measurement systems use digital instrument transformers and IEC 61850-compliant information exchange protocols in digital substations. Digital instrument transformers used for real-time high-voltage measurements should combine advanced digital, measuring, information, and communication technologies. Digital instrument transformers should be cheap, small, light, and fire- and explosion-safe. These smaller and lighter transformers allow long-distance transmission of an optical signal that gauges direct or alternating current. Cost-prohibitive optical converters are a problem. To improve the tool's accuracy, amorphous alloys are used in the magnetic circuits and compensating feedback. Large-scale voltage converters can be made cheaper by using resistive, capacitive, or hybrid voltage dividers. In known electronic voltage transformers, the voltage divider output is generally on the low-voltage side, facilitating power supply organization. Combining current and voltage transformers reduces equipment size, installation, and maintenance costs. These two gadgets cost less together than individually. To increase commercial power metering accuracy, current and voltage converters should be included into digital instrument transformers so that simultaneous analogue-to-digital samples are obtained. Multichannel ADC microcircuits with synchronous conversion start allow natural parallel sample drawing. Digital instrument transformers are created adaptable to substation operating circumstances and environmental variables, especially ambient temperature. An embedded microprocessor auto-diagnoses and auto-calibrates the proposed digital instrument transformer.