Newton-type methods under generalized self-concordance and inexact oracles

Many modern applications in machine learning, image/signal processing, and statistics require to solve large-scale convex optimization problems. These problems share some common challenges such as high-dimensionality, nonsmoothness, and complex objectives and constraints. Due to these challenges, the theoretical assumptions for existing numerical methods are not satisfied. In numerical methods, it is also impractical to do exact computations in many cases (e.g. noisy computation, storage or time limitation). Therefore, new approaches as well as inexact computations to design new algorithms should be considered. In this thesis, we develop fundamental theories and numerical methods, especially second-order methods, to solve some classes of convex optimization problems, where first-order methods are inefficient or do not have a theoretical guarantee. We aim at exploiting the underlying smoothness structures of the problem to design novel Newton-type methods. More specifically, we generalize a powerful concept called \mbox{self-concordance} introduced by Nesterov and Nemirovski to a broader class of convex functions. We develop several basic properties of this concept and prove key estimates for function values and its derivatives. Then, we apply our theory to design different Newton-type methods such as damped-step Newton methods, full-step Newton methods, and proximal Newton methods. Our new theory allows us to establish both global and local convergence guarantees of these methods without imposing unverifiable conditions as in classical Newton-type methods. Numerical experiments show that our approach has several advantages compared to existing works. In the second part of this thesis, we introduce new global and local inexact oracle settings, and apply them to develop inexact proximal Newton-type schemes for optimizing general composite convex problems equipped with such inexact oracles. These schemes allow us to measure errors theoretically and systematically and still lead to desired convergence results. Moreover, they can be applied to solve a wider class of applications arising in statistics and machine learning.Doctor of Philosoph

The interaction between miR160 and miR165/166 in the control of leaf development and drought tolerance in Arabidopsis

MicroRNAs (miRNAs) are a class of non-coding RNAs that play important roles in plant development and abiotic stresses. To date, studies have mainly focused on the roles of individual miRNAs, however, a few have addressed the interactions among multiple miRNAs. In this study, we investigated the interplay and regulatory circuit between miR160 and miR165/166 and its effect on leaf development and drought tolerance in Arabidopsis using Short Tandem Target Mimic (STTM). By crossing STTM160 Arabidopsis with STTM165/166, we successfully generated a double mutant of miR160 and miR165/166. The double mutant plants exhibited a series of compromised phenotypes in leaf development and drought tolerance in comparison to phenotypic alterations in the single STTM lines. RNA-seq and qRT-PCR analyses suggested that the expression levels of auxin and ABA signaling genes in the STTM-directed double mutant were compromised compared to the two single mutants. Our results also suggested that miR160-directed regulation of auxin response factors (ARFs) contribute to leaf development via auxin signaling genes, whereas miR165/166- mediated HD-ZIP IIIs regulation confers drought tolerance through ABA signaling. Our studies further indicated that ARFs and HD-ZIP IIIs may play opposite roles in the regulation of leaf development and drought tolerance that can be further applied to other crops for agronomic traits improvement

The interaction between miR160 and miR165/166 in the control of leaf development and drought tolerance in Arabidopsis

MicroRNAs (miRNAs) are a class of non-coding RNAs that play important roles in plant development and abiotic stresses. To date, studies have mainly focused on the roles of individual miRNAs, however, a few have addressed the interactions among multiple miRNAs. In this study, we investigated the interplay and regulatory circuit between miR160 and miR165/166 and its effect on leaf development and drought tolerance in Arabidopsis using Short Tandem Target Mimic (STTM). By crossing STTM160 Arabidopsis with STTM165/166, we successfully generated a double mutant of miR160 and miR165/166. The double mutant plants exhibited a series of compromised phenotypes in leaf development and drought tolerance in comparison to phenotypic alterations in the single STTM lines. RNA-seq and qRT-PCR analyses suggested that the expression levels of auxin and ABA signaling genes in the STTM-directed double mutant were compromised compared to the two single mutants. Our results also suggested that miR160-directed regulation of auxin response factors (ARFs) contribute to leaf development via auxin signaling genes, whereas miR165/166- mediated HD-ZIP IIIs regulation confers drought tolerance through ABA signaling. Our studies further indicated that ARFs and HD-ZIP IIIs may play opposite roles in the regulation of leaf development and drought tolerance that can be further applied to other crops for agronomic traits improvement

In Situ X-ray Absorption Spectroscopy of Metal/Nitrogen-doped Carbons in Oxygen Electrocatalysis

Metal/nitrogen-doped carbons (M−N−C) are promising candidates as oxygen electrocatalysts due to their low cost, tunable catalytic activity and selectivity, and well-dispersed morphologies. To improve the electrocatalytic performance of such systems, it is critical to gain a detailed understanding of their structure and properties through advanced characterization. In situ X-ray absorption spectroscopy (XAS) serves as a powerful tool to probe both the active sites and structural evolution of catalytic materials under reaction conditions. In this review, we firstly provide an overview of the fundamental concepts of XAS and then comprehensively review the setup and application of in situ XAS, introducing electrochemical XAS cells, experimental methods, as well as primary functions on catalytic applications. The active sites and the structural evolution of M−N−C catalysts caused by the interplay with electric fields, electrolytes and reactants/intermediates during the oxygen evolution reaction and the oxygen reduction reaction are subsequently discussed in detail. Finally, major challenges and future opportunities in this exciting field are highlighted.</p

Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease.

Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome's causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

A Meta-analysis of Gene Expression Signatures of Blood Pressure and Hypertension

Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p&lt;0.05). Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%–9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2). Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension