Evaluating the Performance of the WHO International Reference Standard for Osteoporosis Diagnosis in Postmenopausal Women of Varied Polygenic Score and Race

Background: Whether the bone mineral density (BMD) T-score performs differently in osteoporosis classification in women of different genetic profiling and race background remains unclear. Methods: The genomic data in the Women’s Health Initiative study was analyzed (n = 2417). The polygenic score (PGS) was calculated from 63 BMD-associated single nucleotide polymorphisms (SNPs) for each participant. The World Health Organization′s (WHO) definition of osteoporosis (BMD T-score ≤ −2.5) was used to estimate the cumulative incidence of fracture. Results: T-score classification significantly underestimated the risk of major osteoporotic fracture (MOF) in the WHI study. An enormous underestimation was observed in African American women (POR: 0.52, 95% CI: 0.30–0.83) and in women with low PGS (predicted/observed ratio [POR]: 0.43, 95% CI: 0.28–0.64). Compared to Caucasian women, African American, African Indian, and Hispanic women respectively had a 59%, 41%, and 55% lower hazard of MOF after the T-score was adjusted for. The results were similar when used for any fractures. Conclusions: Our study suggested the BMD T-score performance varies significantly by race in postmenopausal women

A NEW METRIC-BASED LCA METHOD FOR ASSESSING THE SUSTAINABILITY PERFORMANCE OF METALLIC AUTOMOTIVE COMPONENTS

This thesis presents a new metric-based Life-cycle Assessment (LCA) method for assessing the sustainability performance of metallic automotive components. The unique feature of this research work include the development and use of a metrics-based product sustainability index (ProdSI) methodology by considering the total life-cycle approach and the triple bottom line (TBL) with the 6R methodology. It has been shown that the manufactured product’s sustainability performance can be comprehensively assessed using this new methodology. The major focus of this research is the integration of the 6R activities (Reduce, Reuse, Recycle, Recover, Redesign and Remanufacture). Four life-cycle stages of the product, with various end-of-life (EOL) product scenarios, are modeled and analyzed. These scenarios include: reuse, remanufacturing, and recycling the products at EOL. Furthermore, a new mathematical model is developed and presented to determine the optimum percentage mix for various product EOL strategic options. By using the 6R methodology, the overall product sustainability was significantly improved. This improvement was quantitatively assessed by computing the ProdSI score. Ultimately, this research shows that a closed-loop material flow can be achieved

Development of a MetaPGS for Accurate Prediction of Osteoporotic Fracture

Introduction: Early identification of individuals at high-risk for osteoporotic fractures who may benefit from preventive intervention is essential. However, the predictive accuracy of the currently used fracture risk assessment tool remains suboptimal. The first aim of this research is to construct genome-wide polygenic scores for the femoral neck (???_?????ldpred) and total body BMD (???_?????ldpred) and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures. The second aim is to validate the predictive performance of two previously established PGSs (???_?????ldpred and ???_?????ldpred) in an external cohort of 9,000 postmenopausal women of European ancestry. The third aim is to develop and evaluate a novel approach called metaPGS, which combines genetic information from multiple fracture-related traits to further improve the predictive accuracy of genetic information in fracture risk assessment.Methods: The first manuscript constructed genome-wide PGS for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment. The PGSs developed in the first study were then externally validated in the second study using the Women’s Health Initiative (WHI) study data. The magnitude of the association between each PGS and Major Osteoporotic Fractures (MOF)/Hip Fractures (HF) risk was assessed by using the Cox Proportional Hazard Model. To investigate whether adding PGS would improve the predictive ability of FRAX, we formulated four models: (1) Base model: FRAX risk factors; (2) Base model + ???_?????ldpred; (3) Base model + ???_?????ldpred; (4) Base model + metaPGS. The reclassification ability of models with PGS was further assessed using the Net Reclassification Improvement (NRI) and the Integrated discrimination improvement (IDI). In the third study, to develop the novel metaPGS combining PGSs of multiple fracture-related traits/diseases, we first derived individual PGS from genome-wide association studies of 16 fracture-related traits. Then, we employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures while controlling for covariates such as age, sex, and the first four principal components. The optimal metaPGS model was chosen based on the highest area under the receiving-operating characteristic curve (AUC). The metaPGS was constructed by combining the 11 most significant individual PGSs selected using the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The ability of the models to reclassify fracture risk was also assessed using NRI and IDI. Results: In the first study, for each unit decrease in PGSs, the genome-wide PGSs were associated with up to 1.17-fold increased fracture risk. The genome-wide total body PGS (???_?????ldpred) (HR: 1.17; 95%CI 1.15-1.19, p\u3c0.0001) showed a significantly higher association with fractures compared to the restricted total body BMD (???_?????81) (HR: 1.03; 95%CI 1.01-1.05, p=0.001). In the reclassification analysis, compared to the FRAX base model, the models with ???_?????63, ???_?????81, ???_?????ldpred, and ???_?????ldpred improved the reclassification of fracture by 1.2% (95% CI, 1.0% to 1.3%), 0.2% (95% CI, 0.1% to 0.3%), 1.4% (95% CI, 1.3% to 1.5%), and 2.2% (95% CI, 2.1% to 2.4%), respectively. The second study failed to validate the findings discovered in the first study. The results showed that these PGSs were not significantly correlated with MOF or HF in the WHI cohort. Additionally, incorporating genetic information into the FRAX tool showed minimal improvement in the predicted probabilities of hip fracture risk for elderly Caucasian women. In the third study, the metaPGS had a significant association with incident fractures (HR: 1.22, 95% CI: 1.19 - 1.27), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. The metaPGS had comparable predictive power to established risk factors such as age, body weight, and early menopause. The association between the metaPGS and incident fractures remained significant after adjusting for clinical risk factors, indicating added predictive value beyond established clinical risk factors. Adding the metaPGS to the FRAX model improved the discrimination of fractures from non-fracture cases. Conclusions: The findings indicate that integrating PGS data into clinical diagnosis has the potential to enhance the efficacy of screening programs at a population level. The metaPGS approach shows promise for stratifying fracture risk in the European population, as it combines genetic data from various fracture-related traits, improving fracture risk prediction

DATA-DRIVEN PREDICTION, DESIGN, AND CONTROL OF SYSTEM BEHAVIOR USING STATISTICAL LEARNING

The goal in this dissertation is to develop new data-driven techniques for prediction, design, and control of the behavior of a variety of engineering systems. The data used can be obtained from a variety of sources, including from offline, high-fidelity system’s simulation, physical experiments, and online, sparse measurements from sensors. Three inter-related research directions are followed in this dissertation. Following the first direction, the author presents a multi-step-ahead prediction technique for evaluating a single-response (or single-output of the) system’s behavior through an integration of the data obtained offline from the system’s high-fidelity simulation, and online from single sensor measurements. With regard to the first research direction, the key contribution includes a reasonably fast and accurate prediction strategy that can be used, among others, for online, multi-step ahead forecasting of the system’s operational behavior. Building on the work from the first direction, the author follows a second research direction to present a multi-step ahead prediction technique, this time for a multi-response system’s behavior, that can be used for evaluating various system’s designs and corresponding operations. Data in this case is obtained from the offline, high-fidelity system’s simulations, and online sparse measurements from multiple sensors (or limited number of physical experiments). The main contribution for this second direction is in construction of a new data-driven, multi-response prediction framework that has a robust predictive capability. Along the third research direction, a data-driven technique is used for prediction and co-optimization of a system’s design and control. The data in this case is obtained from sensor measurements or a simulator. The main contribution achieved through the third direction is a new data-driven reinforcement learning-based prediction and co-optimization approach. The methods from this dissertation have numerous applications, including those demonstrated here: (i) assessment of safe aircraft flight conditions (Chapters 2 and 3), (ii) evaluation of design and operation of a robotic appendage (Chapter 3), and (iii) design and control of a traffic system (Chapter 4)

PFE: Linear Active Security, Double-Shuffle Proofs, and Low-Complexity Communication

We consider private function evaluation (PFE) in malicious adversary model. Current state-of-the-art in PFE from Valiant\u27s universal circuits (Liu, Yu, et al., CRYPTO 2021) does not avoid the logarithmic factor in circuit size. In constructing linear active PFE, one essential building block is to prove the correctness of an extended permutation (EP, Mohassel and Sadeghian at EUROCRYPT 2013) by zero-knowledge protocols with linear complexity. The linear instantiation $\mathcal{ZK}_{EP}$ by Mohassel, Sadeghian, and Smart (ASIACRYPT 2014) is a three-phase protocol, and each phase (dummy placement, replication, and permutation) is of size $2g$. Its overhead thus seems really outrageous, reducing its practicability. We present in this paper a novel and efficient framework $\mathcal{ZK}_{DS}$ for proving the correct EP. We show that \underline{d}ouble \underline{s}huffles suffice for EP (exponentiations and communication overheads are about 27% and 31% of $\mathcal{ZK}_{EP}$, respectively). Data owner(s) generates the randomness for the first shuffle whose outputs determine outgoing wires of the circuit defined by the function. Function owner reuses and extends the randomness in the second shuffle whose outputs determine the incoming wires. From $\mathcal{ZK}_{DS}$, we build an online/offline PFE framework with linear active security. The online phase could be instantiated by any well-studied secure function evaluation (SFE) with linear active security (e.g., Tiny-OT at CRYPTO 2012). The offline phase depends only on the private function $f$ and uses $\mathcal{ZK}_{DS}$ to prove the EP relationship between outgoing wires and incoming wires in the circuit $\mathcal{C}_f$ derived from $f$

CLKS: Certificateless Keyword Search on Encrypted Data

Keyword search on encrypted data enables one to search keyword ciphertexts without compromising keyword security. We further investigate this problem and propose a novel variant, dubbed certificateless keyword search on encrypted data (CLKS). CLKS not only supports keyword search on encrypted data, but also brings promising features due to the certificateless cryptography. In contrast to the certificated-based keyword search, CLKS requires no validation on the trustworthy of the public key before encrypting keywords; in contrast to the identity-based keyword search, CLKS prevents the key issuer (e.g., key generator center) from penetrating any information on keyword ciphertexts by leveraging the capability of accessing all data users’ (partial) private keys. Specifically, we rigorously define the syntax and security definitions for CLKS, and present the construction that is provably secure in the standard model under the Decisional Linear assumption. We implemented the proposed CLKS scheme and evaluated its performance. To the best of our knowledge, this is the first attempt to integrate certificateless cryptography with keyword search on encrypted data

Associations between Different Ozone Indicators and Cardiovascular Hospital Admission:A Time-Stratified Case-Crossover Analysis in Guangzhou, China

Epidemiological studies reported that ozone (O3) is associated with cardiovascular diseases. However, only few of these studies examined the impact of multiple O3 indicators on cardiovascular hospital admissions. This study aimed to explore and compare the impacts of different O3 indicators on cardiovascular hospital admissions in Guangzhou, China. Based upon the data on daily cardiovascular hospital admissions, air pollution, and meteorological factors in Guangzhou from 2014 to 2018, a time-stratified case-crossover design model was used to analyze the associations between different O3 indicators and cardiovascular hospital admissions. Moreover, the sensitivities of different age and gender groups were analyzed for the whole year and different seasons (i.e., warm and cold). During the warm season, for the single-pollutant model, the odds ratio (OR) value of cardiovascular hospital admissions was 1.0067 (95% confidence interval (CI): 1.0037, 1.0098) for every IQR increase in MDA8 O3 at a lag of five days. The effect of O3 on people over 60 year was stronger than that on the 15–60 years age group. Females were more sensitive than males to O3 exposure. These results provided valuable references for further scientific research and environmental improvement in Guangzhou. Given that short-term O3 exposure poses a threat to human health, the government should therefore pay attention to prevention and control policies to reduce and eliminate O3 pollution and protect human health.</p

Safety and clinical outcomes of regional anaesthesia in Chinese patients with non-small cell lung cancer undergoing non-intubated lobectomy

Purpose: To determine the safety and clinical outcomes of epidural anaesthesia (EA) relative to internal intercostal nerve block (INB) in Chinese patients with non-small cell lung cancer (NSCLC) who were undergoing non-intubated thoracoscopic lobectomy.Methods: Chinese patients with NSCLC (stage I or II) with no evidence of metastasis were given either EA or INB, with equal number of patients in both groups. The peri-operative outcomes determined were duration of anaesthesia /duration of surgery, SpO2/PaCO2 levels, cases of hypotension, and blood loss. The post-operative outcome indices measured were pain score (determined using visual analogue scale (VAS), post-operative complications, chest drainage, duration of hospital stay, and deaths/mortality.Multiple regression analysis was used to confirm the results obtained in this study by adjusting potential covariates. Peri-operative and post-operative complications were compared between the two groups. The results obtained from 220 patients were subjected to statistical analysis.Results: Peri-operative results showed that patients who underwent INB had shorter duration of anaesthesia (12.3 vs 31.4 min, p &lt; 0.05) and shorter duration of surgery (164.4 vs 197.2 min, p &lt; 0.05) than patients who underwent EA for non-intubated lobectomy. Post-operative results showed that patients who underwent INB had significantly lower number of post-operative complications than those who received EA (29 vs 44 %, p &lt; 0.05). The most common post-operative complications among patients in both treatment groups were nausea, vomiting, emphysema and pulmonary complications. Patients who underwent INB had shorter hospital stay than those who underwent EA (5.1 vs 7.5 days, p &lt; 0.05). These results were confirmed through multiple regression analysis.Conclusion: These findings favour the use of INB for regional anaesthesia in NSCLC patients undergoing non-intubated lobectomy

The Randomized Iterate Revisited - Almost Linear Seed Length PRGs from A Broader Class of One-way Functions

We revisit the randomized iterate technique that was originally used by Goldreich, Krawczyk, and Luby (SICOMP 1993) and refined by Haitner, Harnik and Reingold (CRYPTO 2006) in constructing pseudorandom generators (PRGs) from regular one-way functions (OWFs). We abstract out a technical lemma (which is folklore in leakage resilient cryptography), and use it to provide a simpler and more modular proof for the Haitner-Harnik-Reingold PRGs from regular OWFs. We introduce a more general class of OWFs called weakly-regular one-way functions from which we construct a PRG of seed length O(n*logn). More specifically, consider an arbitrary one-way function f with range divided into sets Y1, Y2, ..., Yn where each Y_i={ y:2^{i-1}<=|f^{-1}(y)|<2^{i} }. We say that f is weakly-regular if there exists a (not necessarily efficient computable) cut-off point max such that Y_max is of some noticeable portion (say n^{-c} for constant c), and Y_max+1, ..., Y_n only sum to a negligible fraction. We construct a PRG by making O(n^{2c+1}) calls to f and achieve seed length O(n*logn) using bounded space generators. This generalizes the approach of Haitner et al., where regular OWFs fall into a special case for c=0. We use a proof technique that is similar to and extended from the method by Haitner, Harnik and Reingold for hardness amplification of regular weakly-one-way functions. Our work further explores the feasibility and limits of the randomized iterate type of black-box constructions. In particular, the underlying f can have an arbitrary structure as long as the set of images with maximal preimage size has a noticeable fraction. In addition, our construction is much more seed-length efficient and security-preserving (albeit less general) than the HILL-style generators where the best known construction by Vadhan and Zheng (STOC 2012) requires seed length O(n^3)