Novel Analytical Methodologies for the Monitoring of Traditional and Non-traditional Pollutants in different Environmental Compartments of South Florida

Routine monitoring of environmental pollution demands simplicity and speed without sacrificing sensitivity or accuracy. The development and application of sensitive, fast and easy to implement analytical methodologies for detecting emerging and traditional water and airborne contaminants in South Florida is presented. A novel method was developed for quantification of the herbicide glyphosate based on lyophilization followed by derivatization and simultaneous detection by fluorescence and mass spectrometry. Samples were analyzed from water canals that will hydrate estuarine wetlands of Biscayne National Park, detecting inputs of glyphosate from both aquatic usage and agricultural runoff from farms. A second study describes a set of fast, automated LC-MS/MS protocols for the analysis of dioctyl sulfosuccinate (DOSS) and 2-butoxyethanol, two components of Corexit®. Around 1.8 million gallons of those dispersant formulations were used in the response efforts for the Gulf of Mexico oil spill in 2010. The methods presented here allow the trace-level detection of these compounds in seawater, crude oil and commercial dispersants formulations. In addition, two methodologies were developed for the analysis of well-known pollutants, namely Polycyclic Aromatic Hydrocarbons (PAHs) and airborne particulate matter (APM). PAHs are ubiquitous environmental contaminants and some are potent carcinogens. Traditional GC-MS analysis is labor-intensive and consumes large amounts of toxic solvents. My study provides an alternative automated SPE-LC-APPI-MS/MS analysis with minimal sample preparation and a lower solvent consumption. The system can inject, extract, clean, separate and detect 28 PAHs and 15 families of alkylated PAHs in 28 minutes. The methodology was tested with environmental samples from Miami. Airborne Particulate Matter is a mixture of particles of chemical and biological origin. Assessment of its elemental composition is critical for the protection of sensitive ecosystems and public health. The APM collected from Port Everglades between 2005 and 2010 was analyzed by ICP-MS after acid digestion of filters. The most abundant elements were Fe and Al, followed by Cu, V and Zn. Enrichment factors show that hazardous elements (Cd, Pb, As, Co, Ni and Cr) are introduced by anthropogenic activities. Data suggest that the major sources of APM were an electricity plant, road dust, industrial emissions and marine vessels.

Monitoring and modelling the morphodynamic evolution of a breached barrier beach system

Predicting the evolution of a coastal cell requires the identification of the key drivers of morphology. Soft coastlines are naturally dynamic but severe storm events and even human intervention can accelerate any changes that are occurring. However, when erosive events such as barrier breaching occur with no obvious contributory factors, a deeper understanding of the underlying coastal processes is required. Ideally conclusions on morphological drivers should be drawn from field data collection and remote sensing over a long period of time. Unfortunately, when the Rossbeigh barrier beach in Dingle Bay, County Kerry, began to erode rapidly in the early 2000’s, eventually leading to it breaching in 2008, no such baseline data existed. This thesis presents a study of the morphodynamic evolution of the Inner Dingle Bay coastal system. The study combines existing coastal zone analysis approaches with experimental field data collection techniques and a novel approach to long term morphodynamic modelling to predict the evolution of the barrier beach inlet system. A conceptual model describing the long term evolution of Inner Dingle Bay in 5 stages post breaching was developed. The dominant coastal processes driving the evolution of the coastal system were identified and quantified. A new methodology of long term process based numerical modelling approach to coastal evolution was developed. This method was used to predict over 20 years of coastal evolution in Inner Dingle Bay. On a broader context this thesis utilised several experimental coastal zone data collection and analysis methods such as ocean radar and grain size trend analysis. These were applied during the study and their suitability to a dynamic coastal system was assessed.

Multi-Scale Governance of Sustainable Natural Resource Use—Challenges and Opportunities for Monitoring and Institutional Development at the National and Global Level

In a globalized economy, the use of natural resources is determined by the demand of modern production and consumption systems, and by infrastructure development. Sustainable natural resource use will require good governance and management based on sound scientific information, data and indicators. There is a rich literature on natural resource management, yet the national and global scale and macro-economic policy making has been underrepresented. We provide an overview of the scholarly literature on multi-scale governance of natural resources, focusing on the information required by relevant actors from local to global scale. Global natural resource use is largely determined by national, regional, and local policies. We observe that in recent decades, the development of public policies of natural resource use has been fostered by an “inspiration cycle” between the research, policy and statistics community, fostering social learning. Effective natural resource policies require adequate monitoring tools, in particular indicators for the use of materials, energy, land, and water as well as waste and GHG emissions of national economies. We summarize the state-of-the-art of the application of accounting methods and data sources for national material flow accounts and indicators, including territorial and product-life-cycle based approaches. We show how accounts on natural resource use can inform the Sustainable Development Goals (SDGs) and argue that information on natural resource use, and in particular footprint indicators, will be indispensable for a consistent implementation of the SDGs. We recognize that improving the knowledge base for global natural resource use will require further institutional development including at national and international levels, for which we outline options.

Molecular probes for monitoring mitochondrial movement and function

This thesis explores two distinct parts of mitochondrial physiology: the role of mitochondria in generation of reactive oxygen species (ROS) and mitochondrial morphology and dynamics within cells. The first area of research is covered in Chapters 1-8. Mitochondrial biofunctionality and ROS production are discussed in Chapter 1, followed by the strategy of targeting bioactive compounds to mitochondria by linking them to lipophilic triphenylphosphonium cations (TPP) (Chapter 2). ROS sensors relevant to the research are reviewed in Chapter 3. Chapter 4 presents design and synthesis of novel probes for superoxide detection in mitochondria (MitoNeo-D), cytosol (Neo-D) and extracellular environment (ExCellNeo-D). The results of biological validation of MitoNeo-D and Neo-D performed in the MRC MBU in Cambridge are presented in Chapter 5. A dicationic hydrogen peroxide sensor that utilizes in situ click chemistry is discussed in Chapter 6. Preliminary work on the synthesis of mitochondria-targeted superoxide generators, which led to the development of mitochondria-targeted analogue of paraquat, MitoPQ, is presented in Chapter 7. A set of bifunctional probes (BCN-Mal, BCN-E-BCN and Mito-iTag) for assessing the redox states of protein thiols is discussed in Chapter 8 along with their biological validation. The second part of the thesis is aimed at the study of mitochondrial morphology and dynamics and is presented in Chapters 9-11. Chapter 9 provides background on the classes of fluorophores relevant to the research, the phenomenon of fluorescence quenching and the principle of photoactivation with examples of photoactivatable fluorophores. Next, the background on mitochondrial morphology and heterogeneity is presented in Chapter 10, followed by the ways of imaging and tracking mitochondria within cells by conventional fluorophores and by photoactivatable fluorophores exploiting super-resolution microscopy. Chapter 11 presents the design and synthesis of four photoactivatable fluorophores for mitochondrial tracking, MitoPhotoRhod110, MitoPhotoNIR, Photo-E+, MitoPhoto-E+, along with results of biological validation of MitoPhotoNIR. The results and discussion concludes with Chapter 12, which is a summary and suggestions for future work, followed by the chemistry experimental procedures (Chapter 13), materials and methods for biological experiments (Chapter 14) and references.

Impact of Protein Induced by Vitamin K Absence (PIVKA-II) in diagnosis and monitoring of patients with hepatocellular carcinoma

Background and Aims: Hepatocellular carcinoma (HCC) represents the second leading cause of cancer deaths worldwide. Protein induced by vitamin K absence (PIVKA-II) has been proposed as potential screening biomarker for HCC.This study has been designed to evaluate the role of PIVKA-II as diagnostic HCC marker, through the comparison between PIVKA-II and alpha-fetoprotein (AFP) serum levels on HCC patients and the two control groupsof patients with liver disease and without HCC. Methods: In an Italian prospective cohort, PIVKA-II levels were assessed on serum samplesby an automated chemiluminescent immunoassay (Abbott ARCHITECT). The study population included 65 patients with HCC (both “de novo” and recurrent), 111 with liver cirrhosis (LC) and 111 with chronic hepatitis C (CHC). Results: PIVKA-II levels were increased in patients with HCC (median 63.75, range: 12-2675 mAU/mL) compared to LC (median value: 30.95, range: 11.70–1251mAU / mL, Mann Whitney test p < 0.0001) and CHC (median value: 24.89, range: 12.98-67.68mAU / mL, p < 0.0001).The area under curve (AUC) for PIVKA-II was 0.817 (95% Confidence Interval(CI), 0.752-0.881). At the optimal threshold of 37 mAU / mL, identified by the Youden Index, the sensitivity and specificity were 79% and 76%, respectively. PIVKA-II was a better biomarker than AFP for the diagnosis of HCC, since the AUC for AFP was 0.670 (95% CI 0.585-0.754, p<0.0001) and at the best cutoff of 16.4 ng / mL AFP yielded 98% specificity but only 34% sensitivity. Conclusions:These initial data suggest the potential utility of this tool in the diagnosis of HCC.PIVKA-II alone or in combination may help to an early diagnosis of HCC and a significant optimization of patient management.

mHealth-based Methods for Neuromotor Assessment and Rehabilitation

Recent years observed massive growth in wearable technology, everything can be smart: phones, watches, glasses, shirts, etc. These technologies are prevalent in various fields: from wellness/sports/fitness to the healthcare domain. The spread of this phenomenon led the World-Health-Organization to define the term 'mHealth' as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices". Furthermore, mHealth solutions are suitable to perform real-time wearable Biofeedback (BF) systems: sensors in the body area network connected to a processing unit (smartphone) and a feedback device (loudspeaker) to measure human functions and return them to the user as (bio)feedback signal. During the COVID-19 pandemic, this transformation of the healthcare system has been dramatically accelerated by new clinical demands, including the need to prevent hospital surges and to assure continuity of clinical care services, allowing pervasive healthcare. Never as of today, we can say that the integration of mHealth technologies will be the basis of this new era of clinical practice. In this scenario, this PhD thesis's primary goal is to investigate new and innovative mHealth solutions for the Assessment and Rehabilitation of different neuromotor functions and diseases. For the clinical assessment, there is the need to overcome the limitations of subjective clinical scales. Creating new pervasive and self-administrable mHealth solutions, this thesis investigates the possibility of employing innovative systems for objective clinical evaluation. For rehabilitation, we explored the clinical feasibility and effectiveness of mHealth systems. In particular, we developed innovative mHealth solutions with BF capability to allow tailored rehabilitation. The main goal that a mHealth-system should have is improving the person's quality of life, increasing or maintaining his autonomy and independence. To this end, inclusive design principles might be crucial, next to the technical and technological ones, to improve mHealth-systems usability.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Monitoring of Mediterranean marine benthic biodiversity through novel, standardized, and integrated approaches

Marine healthy ecosystems support life on Earth and human well-being thanks to their biodiversity, which is proven to decline mainly due to anthropogenic stressors. Monitoring how marine biodiversity changes trough space and time is needed to properly define and enroll effective actions towards habitat conservation and preservation. This is particularly needed in those areas that are very rich in species compared to their low surface extension and are characterized by strong anthropic pressures, such as the Mediterranean Sea. Subtidal rocky benthic Mediterranean habitats have a complex structural architecture, hosting a panoply of tiny organisms (cryptofauna) that inhabit crevices and caves, but that are still unknown. Different artificial standardized sampling structures (SSS) and methods have been developed and employed to characterize the cryptofauna, allowing for data replicability and comparability across regions. Organisms growing on these artificial structures can be identified coupling morphological taxonomy and DNA barcoding and metabarcoding. The metabarcoding allows for the identification of organisms in a bulk sample without morphological analysis, and it is based on comparing the genetic similarities of the assessed organisms with barcoding sequences present in online barcoding repositories. Nevertheless, barcoded species nowadays represent only a small portion of known species, and barcoding reference databases are not always curated and updated on a regular basis. In this Thesis I used an integrative approach to characterize benthic marine biodiversity, specifically coupling morphological and molecular techniques with the employment of SSS. Moreover, I upgraded the actual status of COI (cytochrome c oxidase subunit I) barcoding of marine metazoans, and I built a customized COI barcoding reference database for metabarcoding studies on temperate biogenic reefs. This work implemented the knowledge about diversity of Mediterranean marine communities, laying the groundworks for monitoring marine and environmental changes that will occur in the next future as consequences of anthropic and climate threats.

Monitoring of New Psychoactive Substances in the hair of patients with a history of addiction. Evaluation of the public health impact.

Introduction. The term New Psychoactive Substances (NPS) encompasses a broad category of drugs which have become available on the market in recent years and whose illicit use for recreational purposes has recently exploded. The analysis of NPS usually requires mass spectrometry based techniques. The aim of our study was to define the preva-lence of NPS consumption in patients with a history of drug addiction followed by Public Services for Pathological Addictions, with the purpose of highlighting the effective presence of NPS within the area of Bologna and evaluating their association with classical drugs of abuse (DOA). Materials and methods. Sustained by literature, a multi-analyte UHPLC-MS/MS method for the identification of 127 NPS (phenethylamines, arylcyclohexylamines, synthetic opioids, tryptamines, synthetic cannabinoids, synthetic cathinones, designer benzodiazepines) and 15 classic drugs of abuse (DOA) in hair samples was developed and validated according to International Guidelines [112]. Samples pretreatment consisted of washing steps and overnight incubation at 45°C in an acid mixture of methanol and water. After cooling, supernatant were injected into the chromatographic system coupled with a tandem mass spectrometry detector. Results. Successful validation was achieved for almost all of the compounds. The method met all the required technical parameters. LOQ was set from 4 to 80 pg/mg The developed method was applied to 107 cases (85 males and 22 females) of clinical interest. Out of 85 hair samples resulting positive to classical drugs of abuse, NPS were found in twelve (8 male and 4 female). Conclusion. The present methodology represents an easy, low cost, wide-panel method for the de-tection of 127 NPS and 15 DOA in hair samples. Such multi-analyte methods facilitates the study of the prevalence of drugs abused that will enable the competent control authorities to obtain evi-dence-based reports regarding the critical spread of the threat represented by NPS.

Monitoring time-dependent deformation patterns through geodetic techniques

In this thesis we focus on the analysis and interpretation of time dependent deformations recorded through different geodetic methods. Firstly, we apply a variational Bayesian Independent Component Analysis (vbICA) technique to GPS daily displacement solutions, to separate the postseismic deformation that followed the mainshocks of the 2016-2017 Central Italy seismic sequence from the other, hydrological, deformation sources. By interpreting the signal associated with the postseismic relaxation, we model an afterslip distribution on the faults involved by the mainshocks consistent with the co-seismic models available in literature. We find evidences of aseismic slip on the Paganica fault, responsible for the Mw 6.1 2009 L’Aquila earthquake, highlighting the importance of aseismic slip and static stress transfer to properly model the recurrence of earthquakes on nearby fault segments. We infer a possible viscoelastic relaxation of the lower crust as a contributing mechanism to the postseismic displacements. We highlight the importance of a proper separation of the hydrological signals for an accurate assessment of the tectonic processes, especially in cases of mm-scale deformations. Contextually, we provide a physical explanation to the ICs associated with the observed hydrological processes. In the second part of the thesis, we focus on strain data from Gladwin Tensor Strainmeters, working on the instruments deployed in Taiwan. We develop a novel approach, completely data driven, to calibrate these strainmeters. We carry out a joint analysis of geodetic (strainmeters, GPS and GRACE products) and hydrological (rain gauges and piezometers) data sets, to characterize the hydrological signals in Southern Taiwan. Lastly, we apply the calibration approach here proposed to the strainmeters recently installed in Central Italy. We provide, as an example, the detection of a storm that hit the Umbria-Marche regions (Italy), demonstrating the potential of strainmeters in following the dynamics of deformation processes with limited spatio-temporal signature

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

Evaluation Of Different Treatment Methods Against Denture Stomatitis: A Randomized Clinical Study.

The aim of this clinical study was to determine the efficacy of Uncaria tomentosa (cat's claw) against denture stomatitis (DS). Fifty patients with DS were randomly assigned into 3 groups to receive 2% miconazole, placebo, or 2% U tomentosa gel. DS level was recorded immediately, after 1 week of treatment, and 1 week after treatment. The clinical effectiveness of each treatment was measured using Newton's criteria. Mycologic samples from palatal mucosa and prosthesis were obtained to determinate colony forming units per milliliter (CFU/mL) and fungal identification at each evaluation period. Candida species were identified with HiCrome Candida and API 20C AUX biochemical test. DS severity decreased in all groups (P < .05). A significant reduction in number of CFU/mL after 1 week (P < .05) was observed for all groups and remained after 14 days (P > .05). C albicans was the most prevalent microorganism before treatment, followed by C tropicalis, C glabrata, and C krusei, regardless of the group and time evaluated. U tomentosa gel had the same effect as 2% miconazole gel. U tomentosa gel is an effective topical adjuvant treatment for denture stomatitis.

[health Protection For Rural Workers: The Need To Standardize Techniques For Quantifying Dermal Exposure To Pesticides].

Quantification of dermal exposure to pesticides in rural workers, used in risk assessment, can be performed with different techniques such as patches or whole body evaluation. However, the wide variety of methods can jeopardize the process by producing disparate results, depending on the principles in sample collection. A critical review was thus performed on the main techniques for quantifying dermal exposure, calling attention to this issue and the need to establish a single methodology for quantification of dermal exposure in rural workers. Such harmonization of different techniques should help achieve safer and healthier working conditions. Techniques that can provide reliable exposure data are an essential first step towards avoiding harm to workers' health.

Petroleomics By Electrospray Ionization Ft-icr Mass Spectrometry Coupled To Partial Least Squares With Variable Selection Methods: Prediction Of The Total Acid Number Of Crude Oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.