The dissertation has a methodological and a substantive contribution. First, it develops a method of measurement that serves to directly test specific hypothesis about the simultaneous interaction of classes in a multivariate regression environment. I show how compositional analysis can be applied to the same data that are used to construct the Gini coefficient serving to further investigate and adjudicate the class effects that are only suggested in the Gin results. I show that because identical Gini calculations result from drastically different land distributions it is wrong to narrow its interpretation to theories relating only the very rich and the very poor while ignoring the middle class. Second, it shows that municipalities in Colombia with relatively large numbers of middle-sized farmers versus large landowners provide larger proportions of their citizens with piped water, electricity, and other public services. I argue that middle-sized farmers play this role because they have the income and other resources needed to travel to local urban areas to gather relevant information and to lead local Communal Action Boards in bargaining with local elected officials, but they are poor enough to need the public provision of such services. On the other hand, large landowners live in urban areas, and find more efficient to pay privately for their own services than paying for everyone’s via local taxes.

The elucidation of a protein’s interaction/association network is important for defining itsbiological function. Mass spectrometry-based proteomic approaches have emerged as powerful tools for identifying protein-protein interactions (PPIs) and protein-protein associations (PPAs). However, interactome/association experiments are difficult to interpret considering the complexity and abundance of data that is generated. Although tools have been developed to quantitatively identify protein interactions/associations, there is still a pressing need for easy-to-use tools that allow users to contextualize their results.

To address this, we developed CANVS, a computational pipeline that cleans, analyzes,and visualizes mass spectrometry-based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard, allowing users to easily interface with the pipeline. With simple requirements, users can analyze complex experimental data and create PPI/A networks. The application integrates systems biology databases like BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. As examples, we recently used the analytical framework included in CANVS to study the PPI/A networks of DUSP7, which helped to define its regulation of ERK2 during mitosis and also to analyze the PPA networks of core spindle assembly checkpoint proteins. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.

Additionally, we describe a supervised machine learning method that incorporatesannotated data from the contaminant repository for affinity purification data (CRAPome) that predicts contaminants in affinity and proximity purification data. The method involves first calculating amino acid content, sequence order, hydrophobicity and hydrophilicity from protein sequence. Then balancing data using data augmentation methods. Finally, measuring precision and accuracy using protein-protein interaction/association data. The results suggest that our supervised method can predict with 90% accuracy contaminants in protein-protein interaction/association data.

Mango (Mangifera indica L) is a climacteric tropical fruit that has become popular around the world. US is the largest imported of mango in America, importing mangos from Mexico, Guatemala, Peru, Ecuador, Brazil, Haiti, and Puerto Rico. Mangos most often spend several days (15 to 36) in transit from their country of production to the wholesale markets in the US. Upon arrival at the East Coast (Philadelphia) and West Coast (Los Angeles), mangos are transferred to distribution centers prior to retail stores. Presently 85 to 90 % of imported mangos to the US receive a hot water treatment (HWT) to control Mediterranean and Mexican fruit fly since late 1980s (USDA-APHIS-PPQ, 2022). There are limited postharvest studies of imported mangos to the US available and most of them were carried out using non hot water treated (NHWT) mangos. Over the years, US Mango consumption has increased from 1.7 to 3.6 pounds per capita during the last two decades based on HWT contribution. However, the US mango per capita consumption is still lower than other commodities available in similar yearly cycle. In 2020, bananas, apples, pineapples, and table grapes had a 27.4, 17.6, 7.3, and 8.4 per capita respectively. We believe that for imported mangos to compete with the wide variety of fruits and vegetables available to purchase and satisfy consumers, a clear understanding on consumer quality and postharvest quality deterioration at arrival and distribution in the US is important to propose postharvest handling strategies during transportation, distribution centers, retail stores and consumer education.As a first step of our work, we surveyed mango quality at arrival to stores across US for one season. Based on these results, we hypothesized that the main barriers to increase US mango consumption further were chilling injury incidence, consumer sensory quality, softening problems, low availability of high flavor ‘ready to eat’, and lack of handlers-consumer education. Therefore, two studies were developed following our previous assessment of arriving quality of HWT mangos imported to the US. These chapters deal with modifications of the current ‘ready to eat’ ripening protocol (Testing the Performance of Exogenous Ethylene Application on Ripening of Hot Water Treated Imported Mangos (Mangifera indica, L.) – Chapter 1) and Understanding the Role of the Physiological Maturity and Shipping Temperature on HWT Mango Arrival Quality (market life potential-chilling injury– Chapter 2). Our results, working with HWT mangos, revealed current potential mango limitations and pointed out at which step(s) during handling quality, problems were occurring. This new information allows assisting postharvest handling changes and establishing strong educational programs to reduce these problems and increase US mango consumption. Chapter 1 Exogenous ethylene gas treatment is part of the ripening protocol to deliver ‘ready to eat’ mangos that satisfies consumers preferences and increase mango consumption in the US. In the first chapter, the hypothesis stated that ethylene effectiveness would vary depending on the maturity ripening category of HWT mangos at arrival because mature mangos were already producing ethylene or being exposed to exogenous ethylene from other mangos during handling and transportation to market area. The performance of ethylene was assessed in three batches of ‘Tommy Atkins’ mango, one batch of ‘Ataulfo’, one batch of ‘Keitt’ mangos, all from Mexico, and a batch of ‘Kent’ mango from Peru. All mangos were treated with hot water expect for ‘Keitt’ that came from a fruit fly free zone. Data collected during this study supported the hypothesis and suggested that exogenous ethylene treatment is not necessary to achieve the ‘ready to eat’ stage for HWT mangos. Furthermore, ethylene treatments add handling, cost and delays to the mango postharvest chain that can result in lower fruit quality due to physical damage. The second chapter was a study in cooperation with CIAD (Culiacan, MX) to find the major defects that impact quality of hot water treated mangos due to low temperature, and temperature exposure during shipment or storage. ‘Tommy Atkins’, ‘Kent’, and ‘Ataulfo’ mangos were collected from a packinghouse in Escuinapa, Culiacan, Mexico. They were classified into three harvest maturity stages using a non-destructive DA meter, and evaluating shape, skin color, shoulder shape, shoulder location in relation to peduncle insertion and skin texture. Quality was assessed at harvest and during cold storage; the initial destructive quality measurements include flesh firmness, flesh color, soluble solids concentration (SSC), and dry matter percentage. Cold storage evaluation was conducted on ripe mangos and assessed external chilling injury (CI) symptoms such as lenticel discoloration, skin pitting, grayish scald, and surface scald. Internal decay was also part of this evaluation and included loss of flesh color, and in severe cases flesh browning. Our results highlighted skin discoloration and fast softening during transportation as the main barriers to deliver high quality mangos at arrival after 30 days.

Chronic kidney disease (CKD) patients on hemodialysis (HD) suffer elevated mortality from cardiovascular disease (CVD) linked to accumulating uremic toxicity. Recent findings in cardiac MRI indicate earlier fibrosis and hypertrophy in CKD patients, challenging the traditional belief that cardiorenal syndrome develops secondary to uremia and hypervolemia, inciting the need to re-explore CVD pathogenesis as a result of CKD. Platelets present a promising link between CKD and CVD as rich sources of tissue regulators that are abnormally stimulated during HD. However, while activation of circulating platelets by receptor binding and subsequent coagulation events are defined by a well characterized physiological response, the mechanobiology of platelet degranulation is comparatively unexplored. I hypothesized that the observed early cardiac remodeling in HD patients is in part caused by HD-aggravated platelet dysregulation leading to non-uremic circulatory imbalances. To test this, I proposed 1) characterizing the effects of HD on platelets and the circulating proteome, 2) mechanistically studying platelet activation in response to the fluidic stimulus of depressurization experienced by frictional loss along the HD flow circuit, and 3) visually observing fibrotic developments in cardiac tissues in vitro as a result of contact with platelets of varying dysregulation.

In the first study, plasma and platelets from end stage renal disease (ESRD) patients before and after HD were profiled for proteome and transcriptome growth factors (PDGF, FGF, EGF), proteolytic regulators (MMPs, TIMPs), coagulative factors (vWF, PAF, PF4), cardiac indicators (BNP, TPO, TXA2), and inflammatory markers (CRP, TNFα, IL1b) to examine HD-modulation of platelet effects beyond thrombotic activation. Elevation of plasma proteolytic regulators, fibrotic factors, and corresponding platelet RNA transcripts revealed uniquely pro-fibrotic platelet phenotypes in ESRD patients during HD. Correlations against prior time on dialysis (HD vintage) and blood flow rate (BFR) indicated worsening dysfunction with longer HD vintage and faster prescribed BFR, particularly characterized by preferentially enhanced translation and secretion of matrix metalloproteinases (MMP), tissue inhibitors of MMPs (TIMP), and inflammatory cytokines. Compensatory mechanisms of increased platelet growth factor synthesis with acute plasma MMP and TIMP increases show short-term mode-switching between dialysis sessions leading to long-term pro-fibrotic bias. Chronic pro-fibrotic adaptation of platelet synthesis was observed through changes in differential secretory kinetics of heterogeneous granule subtypes. Chronic and acute platelet responses to HD describe contributions to a pro-fibrotic milieu in ESRD.

In the second study, platelets from healthy and HD (pre- and post-treatment) donors were cyclically depressurized in static suspension to mimic the precipitous and repetitive pressure drops across the hemodialysis flow circuit. Platelet activation was measured by integrin complexing of the traditional coagulative indicator glycoprotein αIIbβ3 (PAC1) and expression of the granule surface receptor P-selectin (CD62P). The progressive increase in CD62P with no changes in PAC1 over pressure-cycling duration regardless of uremia signifies that hydrostatic depressurization involves a novel agonist-free mechanism leading to platelet degranulation as a unique case in which CD62P and PAC1 do not interchangeably indicate platelet activation. Subsequent stimulation using ADP further suggests that sustained depressurization regimens desensitize integrin αIIbβ3 activation. Variability in platelet response caused by uremia and CKD were observed by elevated baseline PAC1 in pre-dialysis samples, PAC1 retention after ADP exposure, and maximum CD62P with ADP independent of pressure. Theory for hydrostatic pressure-induced degranulation circumventing integrin-initiated signal transduction is presented based on the Starling Equation.

In the third study, induced pluripotent stem cell derived cardiomyocyte tissues were cultured with plasma and platelets from hemodialysis donors pre- and post-filtration, healthy donor plasma and platelets, and continuation media. Spontaneously contracting tissues from all test groups were visually analyzed daily for changes in troponin muscular network and functional contractile output and lysed after 28 days of culture for matricellular protein composition. Cardiac tissues developed more fibrotic phenotypes in dialysis derived media, with amplified compositional and structural changes by refreshed post-dialysis platelets over exhausted and uremic pre-dialysis platelets, illustrating a direct link bridging platelet dysfunction to the clinical outcomes of deteriorative cardiac fibrogenesis observed in the REMODELING clinical trial. Separately, healthy plasma and platelets enhanced cardiac maturity, suggesting promising implications for the tissue engineering toolbox.

Collectively, these studies depict how platelets contribute to CVD progression in CKD. The identification of unique pro-fibrotic platelet phenotypes in ESRD independent of uremic state, a novel mechanism of platelet degranulation independent of chemical stimulus, and direct visual observation of platelet-dependent cardiovascular remodeling establish a distinct pathway explaining the manifestation of uremic cardiomyopathy independent of hemodynamic overload. Future use of platelets as therapeutic agents and diagnostic tools for left ventricular remodeling, microvascular dysfunction, and myocardial interstitial fibrosis could aid the prevention of congestive heart failure.

Electric power systems are undergoing significant transformations that have brought fundamental challenges to the system's situational awareness. This research aims to develop reliable strategies to manage such challenges for two typical anomaly-detection problems in modern power systems: cyber-attacks and faults. The growing complexity of power grids due to the penetration of intelligent monitoring devices and recent technological advances in energy management systems have rendered cyber-physical systems like electric grids increasingly vulnerable to cyber threats. These anomalies have been estimated to have a considerable economic impact during a severe cyber-attack. This work is essential for protecting modern and clean power systems because it implements effective methods for detecting and isolating faults in emerging technologies for renewable electric energy generation. Consequently, this research not only reduces the frequency of disruptions but also reduces the risk of damage, lowers maintenance costs, and extends the lifespan of equipment. This work is vital in preventing economic and power-related disruptions caused by cyber-attacks and faults.

Cyber-physical power systems (CPPS) rely on wide-area monitoring, protection, and control (WAMPAC) technologies, allowing operators to drive modern systems more efficiently and reliably. WAMPAC technologies use modern devices such as intelligent relays, digital recorders, and phasor measurement units (PMUs) to monitor the CPPS state. However, such devices produce continuous and unbounded data streams, posing significant data handling, storage, and processing challenges. Moreover, WAMPAC devices and their communication links are vulnerable to cybersecurity risks. To cope with these challenges, this study introduces two novel event detection methods for cyber and non-cyber contingencies based on the Hoeffding Adaptive Tree classifier. This robust machine learning classifier can handle high-velocity and high-volume data streams with limited memory and low computational burden. We consider several cyber and non-cyber events affecting the physics and monitoring infrastructure of CPPS, such as short-circuit faults, line maintenance, remote tripping command injection, relay setting change, and false data injection. The data are generated based on a modified IEEE 9-bus system. Simulation results show that our proposed approach outperforms the state-of-the-art method in the literature.

Fault detection is vital in ensuring the reliable and resilient operation of modern power systems. Its importance lies in swiftly identifying and isolating faults, preventing cascading failures, and enabling rapid power restoration. This work proposes a strategy based on observers and residuals for detecting internal faults in grid-forming inverters with power-sharing coordination for clean and small-scale AC microgrids. Grid-forming inverters will be the future drivers of modern and clean power systems to accelerate the adoption of renewable energy sources such as solar and wind. The dynamics of the inverters are captured through a nonlinear state space model. The design of our observers and residuals considers $H_{-}/H_{\infty}$ conditions to ensure robustness against disturbances and responsiveness to faults. The proposed design is less restrictive than existing observer-based fault detection schemes by leveraging the properties of quadratic inner-boundedness and one-sided Lipschitz conditions. Also, this work introduces a residual-based adaptive threshold for fault detection. An inequality for the upper bound on the $\ell_2$ norm of the residual is derived and used for designing the adaptive threshold. The upper bound is obtained via semidefinite programming with two linear matrix inequality constraints. The internal faults considered in this paper include actuator faults, busbar faults, and inverter bridge faults, which are modeled using vector-matrix representations that modify the state space model of the inverters. One significant advantage of the proposed methods is its cost-effectiveness, as it does not require additional sensors. Experiments are conducted on an islanded AC microgrid with three inductive lines, four inductive loads, and four grid-forming inverters to validate the merits of the proposed fault detection strategy. The results demonstrate that our designs outperform existing methods in the field.