Mitochondrial Involvement in Pancreatic Beta Cell Glucolipotoxicity

High circulating glucose and non-esterified free fatty acid (NEFA) levels can cause pancreatic β-cell failure. The molecular mechanisms of this β-cell glucolipotoxicity are yet to be established conclusively. In this thesis by exploring mitochondrial energy metabolism in INS-1E insulinoma cells and isolated pancreatic islets, a role of mitochondria in pancreatic β-cell glucolipotoxicity is uncovered. It is reported that prolonged palmitate exposure at high glucose attenuates glucose-stimulated mitochondrial respiration which is coupled to ADP phosphorylation. These mitochondrial defects coincide with an increased level of mitochondrial reactive oxygen species (ROS), impaired glucose-stimulated insulin secretion (GSIS) and decreased cell viability. Palmitoleate, on the other hand, does not affect mitochondrial ROS levels or cell viability and protects against the adverse effects of palmitate on these phenotypes. Interestingly, palmitoleate does not significantly protect against mitochondrial respiratory or insulin secretion defects and in pancreatic islets tends to limit these functions on its own. Furthermore, strong evidence suggests that glucolipotoxic-induced ROS are of a mitochondrial origin and these ROS are somehow linked with NEFA-induced loss in cell viability. To explore the mechanism of glucolipotxic-induced mitochondrial ROS and associated cell loss, uncoupling protein-2 (UCP2) protein levels and activity were probed in NEFA exposed INS-1E cells. It is concluded that UCP2 neither mediates palmitate-induced mitochondrial ROS production and the related cell loss, nor protects against these deleterious effects. Instead, UCP2 dampens palmitoleate protection against palmitate toxicity. Collectively, these data shed important new light on the area of glucolipotoxicity in pancreatic β-cells and provide novel insights into the pathogenesis of Type 2 diabetes

Data-Based Predictive Control with Multirate Prediction Step

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function

Staphylococcus aureus bloodstream infection: A pooled analysis of five prospective, observational studies

Objectives: Staphylococcus aureus bacteraemia is a common, often fatal infection. Our aim was to describe how its clinical presentation varies between populations and to identify common determinants of outcome. Methods: We conducted a pooled analysis on 3395 consecutive adult patients with S. aureus bacteraemia. Patients were enrolled between 2006 and 2011 in five prospective studies in 20 tertiary care centres in Germany, Spain, United Kingdom, and United States. Results: The median age of participants was 64 years (interquartile range 50–75 years) and 63.8% were male. 25.4% of infections were associated with diabetes mellitus, 40.7% were nosocomial, 20.6% were caused by methicillin-resistant S. aureus (MRSA), although these proportions varied significantly across studies. Intravenous catheters were the commonest identified infective focus (27.7%); 8.3% had endocarditis. Crude 14 and 90-day mortality was 14.6% and 29.2%, respectively. Age, MRSA bacteraemia, nosocomial acquisition, endocarditis, and pneumonia were independently associated with death, but a strong association was with an unidentified infective focus (adjusted hazard ratio for 90-day mortality 2.92; 95% confidence interval 2.33 to 3.67, p < 0.0001). Conclusion: The baseline demographic and clinical features of S. aureus bacteraemia vary significantly between populations. Mortality could be reduced by assiduous MRSA control and early identification of the infective focus.Junta de Andalucía PI 0185/201

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture

Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft

Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs

M-MRAC for SPHERES

This paper presents application of the modified reference model MRAC (M-MRAC) method to control the relative position and orientation of a cluster of satellites known collectively as the Synchronized Position Hold, Engage, Reorient Experimental Satellites (SPHERES). The approach uses fast estimation algorithms to achieve guaranteed tracking of reference commands for both input and output signals in the presence of uncertainties in mass and inertia data and external disturbances. The tracking errors can be systematically decreased by the proper selection of the design parameters in the identification model. The generated control signals have acceptable magnitudes and exhibit no oscillations. The benefits of the method are demonstrated in numerical simulations

Socio-economic impact of the Covid-19 pandemic

This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.Published versio

Effect of Pacifier Design on Nonnutritive Suck Maturation and Weight Gain in Preterm Infants: A Pilot Study

Background: Pacifiers are effective in promoting oral feeding by increasing the maturation of nonnutritive sucking to nutritive suck in preterm neonates. It is unclear whether pacifier design can influence suck dynamics and weight loss during the first week of life. Objectives: This pilot study examined the feasibility of studying the effect of pacifier design on suck maturation and weight loss in preterm neonates. Methods: Twenty-five preterm neonates (mean [SD] birth weight 1791 [344.9] grams, mean [SD] gestational age 33.1 [1.2] weeks) were studied in a single newborn intensive care unit. Neonates were assigned to either an orthodontic pacifier (n = 13) or a bulb-shaped pacifier (n = 12) immediately after birth. Suck dynamics (cycles per minute, total compressions per minute, cycle bursts, and amplitude) were assessed with an NTrainer (Innara Health, Olathe, Kansas). Weight was recorded during the first week of life on day 1.2 ( ±2.5 days) and day 6.0 ( ±2.1 days). Descriptive statistics were applied to analyze data. Results: No significant differences were seen between groups with respect to birth weight and gestational age. Reproducible nonnutritive sucking measurements could be obtained with the NTrainer, with both types of pacifiers. No differences were detected in nonnutritive sucking dynamics or weight loss over time within each group or between groups. Conclusions: Data indicate that it is feasible to measure nonnutritive sucking dynamics and associated weight loss in relation to pacifier design in preterm neonates. Larger trials over longer time periods are needed to determine whether pacifier design influences suck dynamics and maturation, oromotor function, feeding/weight loss, and dental formation in preterm neonates. ( Curr Ther Res Clin Exp. 2020; 81:XXX–XXX