390 research outputs found
Investigating the Structural Basis for Human Disease: APOBEC3A and Profilin
Analyzing protein tertiary structure is an effective method to understanding protein function. In my thesis study, I aimed to understand how surface features of protein can affect the stability and specificity of enzymes. I focus on 2 proteins that are involved in human disease, Profilin (PFN1) and APOBEC3A (A3A). When these proteins are functioning correctly, PFN1 modulates actin dynamics and A3A inhibits retroviral replication. However, mutations in PFN1 are associated with amyotrophic lateral sclerosis (ALS) while the over expression of A3A are associated with the development of cancer. Currently, the pathological mechanism of PFN1 in this fatal disease is unknown and although it is known that the sequence context for mutating DNA vary among A3s, the mechanism for substrate sequence specificity is not well understood.
To understand how the mutations in Profilin could lead to ALS, I solved the structure of WT and 2 ALS-related mutants of PFN1. Our collaborators demonstrated that ALS-linked mutations severely destabilize the native conformation of PFN1 in vitro and cause accelerated turnover of the PFN1 protein in cells. This mutation-induced destabilization can account for the high propensity of ALS-linked variants to aggregate and also provides rationale for their reported loss-of-function phenotypes in cell-based assays. The source of this destabilization was illuminated by my X-ray crystal structures of several PFN1 proteins. I found an expanded cavity near the protein core of the destabilized M114T variant. In contrast, the E117G mutation only modestly perturbs the structure and stability of PFN1, an observation that reconciles the occurrence of this mutation in the control population. These findings suggest that a destabilized form of PFN1 underlies PFN1-mediated ALS pathogenesis.
To characterize A3A’s substrate specificity, we solved the structure of apo and bound A3A. I then used a systematic approach to quantify affinity for substrate as a function of sequence context, pH and substrate secondary structure. I found that A3A preferred ssDNA binding motif is T/CTCA/G, and that A3A can bind RNA in a sequence specific manner. The affinity for substrate increased with a decrease in pH. Furthermore, A3A binds tighter to its substrate binding motif when in the loop region of folded nucleic acid compared to a linear sequence. This result suggests that the structure of DNA, and not just its chemical identity, modulates A3 affinity and specificity for substrate
Delirium: Delirious Elders, Implementing Reduction Interventions Using Mobility
INTRODUCTION: This quality improvement project involved hiring, training, and managing 3 Delirium Mobility Aids to implement a non-pharmacologic delirium prevention bundle package, including early mobility, on hospitalized patients age \u3e65.
Background: Delirium affects 20-30% of older hospitalized patients [1]. Patients with delirium have double the mortality rate [3], which increases with delirium duration [4]. Delirium worsens long term cognitive functioning [9,10,11,12]. Hospital costs increase by 6,900,000,000 in Medicare expenditures [7]. A single delirium episode increases total yearly costs by ~170,000 institutional grant for 12 months. The project residents reviewed applications, interviewed, and hired 3 CNA\u27s to fill the position 12 hr/day, 7 days/week. Physical and Occupational Therapy trained the aids for 3 weeks in delirium management and mobilization techniques. Data was collected in Epic flowsheetsand chart notes. Confusion-Assessment-Method (CAM) is a established delirium scoring system utilized on Medical A. Data from intervention year (2019) was compared to baseline data collected 2 years prior (2017, 2018) on the same hospital unit.
Results: Preliminary data collected at month 9 of 12: • No statistically significant change in total delirium burden. However there is a trend toward decreased delirium in prolonged hospitalization (measured after day 4). For these patients with LOS \u3e 6 days, there was a 4% reduction in late-stay delirium compared to 2018 and 10% from 2017. • 7.5-13% more patients were completely delirium free after day 4 • Length of Stay (LOS): no significant change (5.5 days) • Patients admitted from home experienced a 4% increase in discharge to home (rather than care-facility) approaching near significance (p-value 0.06). • There was a trend toward reduction in hospital falls: 2017-33. 2018-29. 2019 (present)-19, projected to reach 25 by year’s end. • Press-Ganey patient satisfaction scores remained stable.
Conclusion: Non-pharmacologic multicomponent prevention protocols, which include mobilization, implemented by specialized CNA’s, are a potentially viable treatment of delirium in elderly patients with prolonged hospitalization. This may increase rate of discharge to home, without worsening falls, LOS, or patient experience, and has a cost-savings benefit.https://digitalcommons.psjhealth.org/psv_internal/1004/thumbnail.jp
Systematic hyperparameter selection in Machine Learning-based engine control to minimize calibration effort
For automotive powertrain control systems, the calibration effort is exploding due to growing system complexity and increasingly strict legal requirements for greenhouse gas and real-world pollutant emissions. These powertrain systems are characterized by their highly dynamic operation, so transient performance is key. Currently applied control methods require tuning of an increasing number of look-up tables and of parameters in the applied models. Especially for transient control this state-of-the-art calibration process is unsystematic and requires a large development effort. Also, embedding models in a controller can set challenging requirements to production control hardware. In this work, we assess the potential of Machine Learning to dramatically reduce the calibration effort in transient air path control development. This is not only done for the existing benchmark controller, but also for a new preview controller. In order to efficiently realize preview, a strategy is proposed where the existing reference signal is shifted in time. These reference signals are then modeled as a function of engine torque demand using a Long Short-Term Memory (LSTM) neural network, which can capture the dynamic input–output relationship. A multi-objective optimization problem is defined to systematically select hyperparameters that optimize the trade-off between model accuracy, system performance, calibration effort and computational requirements. This problem is solved using an exhaustive search approach. The control system performance is validated over a transient driving cycle. For the LSTM-based controllers, the proposed calibration approach achieves a significant reduction of 71% in the control calibration effort compared to the benchmark process. The expert effort and turbocharger experiments used in calibrating transient compensation maps in physics-based feedforward controller are replaced by little simulation time and parametrization effort in ML-based controller, which requires significantly less expert effort and system knowledge compared to benchmark process. The best trade-off between multi-objective cost terms is achieved with one layer and 32 cells LSTM neural network for both non-preview and preview control. For non-preview control, a comparable control system performance is achieved with the LSTM-based controller, while 5% reduction in cumulative NOx emissions and similar fuel consumption is achieved with preview controller
Étude des mécanismes cellulaires et humoraux impliqués dans l'orchite auto-immune (OAI) spontanée
Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal
Optimization-based Fault Mitigation for Safe Automated Driving
With increased developments and interest in cooperative driving and higher
levels of automation (SAE level 3+), the need for safety systems that are
capable to monitor system health and maintain safe operations in faulty
scenarios is increasing. A variety of faults or failures could occur, and there
exists a high variety of ways to respond to such events. Once a fault or
failure is detected, there is a need to classify its severity and decide on
appropriate and safe mitigating actions. To provide a solution to this
mitigation challenge, in this paper a functional-safety architecture is
proposed and an optimization-based mitigation algorithm is introduced. This
algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle,
suffering from a severe fault, such as a power steering failure, to a
safe-state. The internal model of the NMPC uses the information from the fault
detection, isolation and identification to optimize the tracking performance of
the controller, showcasing the need of the proposed architecture. Given a
string of ACC vehicles, our results demonstrate a variety of tactical
decision-making approaches that a fault-affected vehicle could employ to manage
any faults. Furthermore, we show the potential for improving the safety of the
affected vehicle as well as the effect of these approaches on the duration of
the manoeuvre.Comment: Accepted for the 2023 IFAC World Conferenc
What do managers think about the success potential of CRM campaigns?
This research analyzes cause-related marketing (CrM) from the perspective of companies. The study aims to achieve a better understanding about what managers think about CrM by analyzing the level of acceptance and usage of this marketing tool, based on the UTAUT model. Using in-depth interviews as research method, we conclude that managers see the benefit of company partaken in initiatives as such, but not necessarily CrM. The reasons why managers choose to participate in Cause-related Marketing initiatives originate from the mix obtained through improving the firm by doing something considered socially positive. CrM was well evaluated by the participants and considered well positioned in terms of acceptance and usage, based on four factors: performance and effort expectancy, social influence, and facilitating conditions. Interviewees expressed excitement towards CrM and believe in it as a powerful tool to improve the firms’ image and consumers feeling towards it. While the literature uses several concepts (Corporate Social Responsibility or Social Marketing), the interviewees emphasize genuine caring and showing interest, time and funds to support consumers social concerns.info:eu-repo/semantics/publishedVersio
- …