Model-Based Fault Tolerant Control

The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment

Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

BACKGROUND: The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. METHODS: We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20-80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. RESULTS: The predictive accuracy of ML-based algorithms (0.843 </= AU-ROC </= 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as 'near population' risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. CONCLUSION: ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening

Prevalence of CDKN2A, CDK4, POT1, BAP1, MITF, ATM, and TERT Pathogenic Variants in a Single-Center Retrospective Series of Patients With Melanoma and Personal or Family History Suggestive of Genetic Predisposition

Introduction: Approximately 20%-45% of familial melanoma (FM) cases are associated with genetic predisposition. Objectives: This single-center retrospective study aimed to assess the frequency of pathogenic variants (PV) in the main melanoma-predisposing genes in patients with cutaneous melanoma and investigate the clinical predictors of genetic predisposition. Methods: Patients included were those diagnosed with cutaneous melanoma at the Dermatology Unit of the University Hospital, Verona, Italy, from 2000 to 2022, presenting at least one of the following: multiple melanomas (≥3); personal/family history of pancreatic cancer (PC) (up to second-degree relatives); ≥2 first-degree relatives with melanoma; ≥1 first-degree relatives with early onset (<45 years) melanoma and tested for CDKN2A, CDK4, POT1, BAP1, MITF, ATM, and TERT. Results: During the study period, 35 out of 1,320 patients (2.7%) underwent genetic testing. Four patients (11.4%) harbored a PV in a melanoma-predisposing gene, 3 in CDKN2A (8.6%), and 1 in MITF (2.9%). Variants currently classified as being of unknown clinical significance (VUS) were detected in CDKN2A (n=1), MITF (n=1), and ATM (n=2). Family history of PC and ≥5 melanomas, personal history of ≥50 nevi, and ≥4 melanomas were significantly associated with PV in tested genes (P<0.05). Conclusions: The prevalence of PV in predisposing genes in FM was lower than previously reported in Italian registries. Possible reasons include deleterious variants in untested intermediate-/low-penetrance genes or yet-to-be-discovered high-penetrance genes and environmental risk factors. A family history of PC, a high number of nevi and melanomas predict a monogenic predisposition to melanoma

Determinants of genetic counseling uptake and its impact on breast cancer outcome: a population-based study

Genetic counseling and BRCA1/BRCA2 genes testing are routinely offered in a clinical setting. However, no data are available on the proportion of breast cancer patients with a positive family history undergoing genetic counseling. By linking databases of the Oncogenetics and Cancer Prevention Unit at the Geneva University Hospitals and the population-based Geneva Cancer Registry, we evaluated the uptake of genetic counseling among 1709 breast cancer patients with familial risk of breast cancer and the determinants of such a consultation process. We also studied the impact of genetic counseling on contralateral breast cancer occurrence and survival. Overall, 191 (11.2%) breast cancer patients had genetic counseling; this proportion was 25.1% within the high familial risk group. Recent period of diagnosis, early-onset breast cancer, female offspring, high familial risk, tumor size, and chemotherapy treatment were statistically significantly associated with genetic counseling uptake in multivariate analysis. More than 2% of patients had developed contralateral metachronous breast cancer. An increased risk of contralateral breast cancer of borderline significance was found for patients who had genetic counseling versus those who had not (Cox model adjusted hazard ratio 2.2, 95% confidence intervals 1.0-5.2, P=0.063). Stratification by BRCA1/BRCA2 mutation status showed that the occurrence of contralateral breast cancer was 8-fold higher among mutation carriers compared with non-carriers. Age-adjusted overall survival and breast cancer-specific survival were not significantly different between patients who underwent genetic counseling and those who did not. In conclusion, we observed a significant increase in the use of genetic counseling over time and found that breast cancer patients with high familial risk had more often genetic counseling than those with moderate familial risk. A more thorough evaluation of sociodemographic and clinical predictors to attend the cancer genetic unit may help improving the use of genetic counseling services for at-risk individuals at a population level

Machine learning techniques for personalized breast cancer risk prediction : comparison with the BCRAT and BOADICEA models

Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53-0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods-the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models.; We quantified and compared the performance of eight different ML methods to the performance of BCRAT and BOADICEA using eight simulated datasets and two retrospective samples: a random population-based sample of U.S. breast cancer patients and their cancer-free female relatives (N = 1143), and a clinical sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing (N = 2481).; Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample.; There was a striking improvement in the accuracy of classification of women with and without breast cancer achieved with ML algorithms compared to the state-of-the-art model-based approaches. High-accuracy prediction techniques are important in personalized medicine because they facilitate stratification of prevention strategies and individualized clinical management

Cancer Predisposition Cascade Screening for Hereditary Breast/Ovarian Cancer and Lynch Syndromes in Switzerland: Study Protocol

Background : Breast, colorectal, ovarian, and endometrial cancers constitute approximately 30% of newly diagnosed cancer cases in Switzerland, affecting more than 12,000 individuals annually. Hundreds of these patients are likely to carry germline pathogenic variants associated with hereditary breast ovarian cancer (HBOC) or Lynch syndrome (LS). Genetic services (counseling and testing) for hereditary susceptibility to cancer can prevent many cancer diagnoses and deaths through early identification and risk management. Objective : Cascade screening is the systematic identification and testing of relatives of a known mutation carrier. It determines whether asymptomatic relatives also carry the known variant, needing management options to reduce future harmful outcomes. Specific aims of the CASCADE study are to (1) survey index cases with HBOC or LS from clinic-based genetic testing records and determine their current cancer status and surveillance practices, needs for coordination of medical care, psychosocial needs, patient-provider and patient-family communication, quality of life, and willingness to serve as advocates for cancer genetic services to blood relatives, (2) survey first- and second-degree relatives and first-cousins identified from pedigrees or family history records of HBOC and LS index cases and determine their current cancer and mutation status, cancer surveillance practices, needs for coordination of medical care, barriers and facilitators to using cancer genetic services, psychosocial needs, patient-provider and patient-family communication, quality of life, and willingness to participate in a study designed to increase use of cancer genetic services, and (3) explore the influence of patient-provider communication about genetic cancer risk on patient-family communication and the acceptability of a family-based communication, coping, and decision support intervention with focus group(s) of mutation carriers and relatives. Methods: CASCADE is a longitudinal study using surveys (online or paper/pencil) and focus groups, designed to elicit factors that enhance cascade genetic testing for HBOC and LS in Switzerland. Repeated observations are the optimal way for assessing these outcomes. Focus groups will examine barriers in patient-provider and patient-family communication, and the acceptability of a family-based communication, coping, and decision-support intervention. The survey will be developed in English, translated into three languages (German, French, and Italian), and back-translated into English, except for scales with validated versions in these languages. Results: Descriptive analyses will include calculating means, standard deviations, frequencies, and percentages of variables and participant descriptors. Bivariate analyses (Pearson correlations, chi-square test for differences in proportions, and t test for differences in means) will assess associations between demographics and clinical characteristics. Regression analyses will incorporate generalized estimating equations for pairing index cases with their relatives and explore whether predictors are in direct, mediating, or moderating relationship to an outcome. Focus group data will be transcribed verbatim and analyzed for common themes. Conclusions: Robust evidence from basic science and descriptive population-based studies in Switzerland support the necessity of cascade screening for genetic predisposition to HBOC and LS. CASCADE is designed to address translation of this knowledge into public health interventions. Trial Registration: ClinicalTrials.gov NCT03124212; https://clinicaltrials.gov/ct2/show/NCT03124212 (Archived by WebCite at http://www.webcitation.org/6tKZnNDBt

Performance of BOADICEA and BRCAPRO genetic models and of empirical criteria based on cancer family history for predicting BRCA mutation carrier probabilities: A retrospective study in a sample of Italian cancer genetics clinics

Abstract Purpose To evaluate in current practice the performance of BOADICEA and BRCAPRO risk models and empirical criteria based on cancer family history for the selection of individuals for BRCA genetic testing. Patients and methods The probability of BRCA mutation according to the three tools was retrospectively estimated in 918 index cases consecutively undergone BRCA testing at 15 Italian cancer genetics clinics between 2006 and 2008. Results 179 of 918 cases (19.5%) carried BRCA mutations. With the strict use of the criteria based on cancer family history 173 BRCA (21.9%) mutations would have been detected in 789 individuals. At the commonly used 10% threshold of BRCA mutation carrier probability, the genetic models showed a similar performance [PPV (38% and 37%), sensitivity (76% and 77%) and specificity (70% and 69%)]. Their strict use would have avoided around 60% of the tests but would have missed approximately 1 every 4 carriers. Conclusion Our data highlight the complexity of BRCA testing referral in routine practice and question the strict use of genetic models for BRCA risk assessment

Design and implementation of periodic digital controllers

Three control problems, related to the design and the implementation of digital controllers, are studied. The overall objective is to develop efficient methods for the computation of solutions. The approach relies on translating the control problems into optimization problems with linear matrix inequality constraints—which can be solved efficiently. The problems and the contributions are summarized next. A digital controller is connected to an analog plant through analog-to-digital and digital-to-analog converters. In practice, these converters have finite range and finite precision. Finite precision causes quantization errors and finite range leads to signal saturation. The controller scaling problem consists in the computation of scaling matrices to guarantee that the input and output signals of the controller do not saturate and to ensure that the performance degradation due to quantization errors is minimized. Traditionally, these scaling matrices are computed only to guarantee that the controller signals do not saturate, and in this case, diagonal matrices suffice. However, diagonal scaling matrices do not necessarily minimize the performance degradation. A method to efficiently compute full matrices in order to prevent signal saturation and to minimize the performance degradation is described. It is shown that full scaling matrices perform better than diagonal scaling matrices. Multirate output controllers (MROCs) use a fast rate to sample the plant measurement outputs and a slow rate to update the plant control inputs. These controllers can replace observer-based controllers to solve a number of control problems, like the arbitrary pole-placement problem. MROCs present advantages, such as controller dynamics that are low-order and can be specified. However, these controllers tend to show high sensitivity to measurement noise. Two algorithms to compute an MROC with order equal to the number of control inputs and low noise sensitivity to place the closed-loop poles in a specified region are given. In addition, these algorithms allow us to specify the MROC dynamics. One of the algorithms comprises two steps. First, a state-feedback gain is computed to achieve the specified pole-placement. Second, an MROC is computed to implement this state-feedback gain and to minimize the noise sensitivity. The other algorithm utilizes an iterative procedure to improve the noise sensitivity of a given MROC that achieves the specified pole-placement. In addition to the algorithms, other contributions are a δ-operator parameterization for MROCs and a technique for the implementation of a given MROC through a single-rate periodic system. The simultaneous stabilization problem consists in the computation of a single controller to stabilize all plants in a given set. This problem is open for linear time-invariant controllers while it can be solved with linear periodic controllers. A method to compute a linear periodic controller that optimizes the worst-case closed-loop performance over all plants in a given set is described. The method comprises two steps. First, a simultaneous stabilizing linear periodic controller is obtained using already known algorithms. Second, the worst-case performance of this controller is improved through an iterative algorithm. This two-step method is extended to guarantee that the solution controller is stable or decentralized

Los falsos hombres de bien : drama en cinco actos

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