Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions. For example, the widely used SPM software package assumes spatially homogeneous longitudinal correlations while the FSL software package assumes Compound Symmetry, the state of all equal variances and equal correlations. While some new methods have been recently pro- posed to more accurately account for such data, these methods can be difficult to specify and are based on iterative algorithms that are generally slow and failure- prone. In this thesis, we propose and investigate the use of the Sandwich Estimator method which first estimates the parameters of interest with a (non-iterative) Ordinary Least Square model and, second, estimates variances/covariances with the “so-called” Sandwich Estimator (SwE) which accounts for the within-subject covariance structure existing in longitudinal data. We introduce the SwE method in its classic form, and review existing and propose new adjustments to improve its behaviour, specifically in small samples. We compare the SwE method to other popular methods, isolating the combination of SwE adjustments that provides valid and powerful inferences. While this result provides p-values at each voxel, it does not provide spatial inferences, e.g. voxel- or cluster-wise family-wise error-corrected p-values. For this, we investigate the use of the non-parametric inference approach called Wild Bootstrap. We again identify the set of procedures and adjustments that provide valid inferences. Finally, in the third and fourth projects, we investigate two ideas to improve the statistical power of the SwE method, by using a shrinkage estimator or a covariance spatial smoothing, respectively. For all the projects, in order to assess the methods, we use intensive Monte Carlo simulations in settings important for longitudinal neuroimaging studies and, for the first two projects, we also illustrate the methods by analysing a highly unbalanced longitudinal dataset obtained from the Alzheimer’s Disease Neuroimaging Initiative

Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data

In recent years, increasing efforts have been made to collect longitudinal neuroimaging data in order to study how brains change over time. However, the popular methods used to analyse such kind of data may not always be appropriate (e.g., overly sensitive to model misspecifications, difficult to specify adequately or prohibitively slow to compute) and may sometimes lead to erroneous conclusions. Motivated by these shortcomings, in this dissertation, we have proposed and studied the use of an alternative method, referred to as “the Sandwich Estimator method”, and have demonstrated that it is a fast, easy-to-specify and accurate option to analyse longitudinal or repeated-measures neuroimaging data

XO-7 b: A Transiting Hot Jupiter with a Massive Companion on a Wide Orbit

Transiting planets orbiting bright stars are the most favorable targets for follow-up and characterization. We report the discovery of the transiting hot Jupiter XO-7 b and of a second, massive companion on a wide orbit around a circumpolar, bright, and metal rich G0 dwarf (V = 10.52, Teff = 6250±100 K, [Fe/H] = 0.432 ± 0.057 dex). We conducted photometric and radial velocity follow-up with a team of amateur and professional astronomers. XO-7 b has a period of 2.8641424±0.0000043 days, a mass of 0.709±0.034 MJ, a radius of 1.373±0.026 RJ, a density of 0.340±0.027 g cm-3 , and an equilibrium temperature of 1743 ± 23 K. Its large atmospheric scale height and the brightness of the host star make it well suited to atmospheric characterization. The wide orbit companion is detected as a linear trend in radial velocities with an amplitude of ~ 100 m s-1 over two years, yielding a minimum mass of 4 MJ; it could be a planet, a brown dwarf, or a low mass star. The hot Jupiter orbital parameters and the presence of the wide orbit companion point towards a high eccentricity migration for the hot Jupiter. Overall, this system will be valuable to understand the atmospheric properties and migration mechanisms of hot Jupiters and will help constrain the formation and evolution models of gas giant exoplanets.The XO project is supported by NASA grant NNX10AG30G. I.R., F.V. and E.H. acknowledge support by the Spanish Ministry for Science, Innovation and Universities (MCIU) and the Fondo Europeo de Desarrollo Regional (FEDER) through grant ESP2016- 80435-C2-1-R, as well as the support of the Generalitat de Catalunya/CERCA programme. The Joan Oró Telescope (TJO) of the Montsec Astronomical Observatory (OAdM) is owned by the Generalitat de Catalunya and operated by the Institute for Space Studies of Catalonia (IEEC). NCS was supported by FCT - Funda c~ao para a Ci^encia e a Tecnologia through national funds and by FEDER - Fundo Europeu de Desenvolvimento Regional through COMPETE2020 - Programa Operacional Competitividade e Internacionalizaçao by these grants: UID/FIS/04434/2019; PTDC/FISAST/ 28953/2017 & POCI-01-0145-FEDER-028953 and PTDC/FIS-AST/32113/2017 & POCI-01-0145- FEDER-032113. HPO acknowledges support from Centre National d'Etudes Spatiales (CNES) grant 131425- PLATO. This research made use of Photutils, an Astropy package for detection and photometry of astronomical sources (Bradley et al. 2019). This research has made use of the Exoplanet Orbit Database and the Exoplanet Data Explorer at exoplanets.org, the Extrasolar Planets Encyclopaedia at exoplanet.eu, and the SIMBAD and VizieR databases at simbad.ustrasbg. fr/simbad/ and http://vizier.u-strasbg.fr/vizbin/ VizieR.Peer ReviewedPostprint (author's final draft

Coherence and incoherence in an optical comb

We demonstrate a coexistence of coherent and incoherent modes in the optical comb generated by a passively mode-locked quantum dot laser. This is experimentally achieved by means of optical linewidth, radio frequency spectrum, and optical spectrum measurements and confirmed numerically by a delay-differential equation model showing excellent agreement with the experiment. We interpret the state as a chimera state

Physicochemical characterization of nebulized superparamagnetic iron oxide nanoparticles (SPIONs)

Abstract Background: Aerosol-mediated delivery of nano-based therapeutics to the lung has emerged as a promising alternative for treatment and prevention of lung diseases. Superparamagnetic iron oxide nanoparticles (SPIONs) have attracted significant attention for such applications due to their biocompatibility and magnetic properties. However, information is lacking about the characteristics of nebulized SPIONs for use as a therapeutic aerosol. To address this need, we conducted a physicochemical characterization of nebulized Rienso, a SPION-based formulation for intravenous treatment of anemia. Methods: Four different concentrations of SPION suspensions were nebulized with a one-jet nebulizer. Particle size was measured in suspension by transmission electron microscopy (TEM), photon correlation spectroscopy (PCS), and nanoparticle tracking analysis (NTA), and in the aerosol by a scanning mobility particle sizer (SMPS). Results: The average particle size in suspension as measured by TEM, PCS, and NTA was 9±2 nm, 27±7 nm, and 56±10 nm, respectively. The particle size in suspension remained the same before and after the nebulization process. However, after aerosol collection in an impinger, the suspended particle size increased to 159±46 nm as measured by NTA. The aerosol particle concentration increased linearly with increasing suspension concentration, and the aerodynamic diameter remained relatively stable at around 75 nm as measured by SMPS. Conclusions: We demonstrated that the total number and particle size in the aerosol were modulated as a function of the initial concentration in the nebulizer. The data obtained mark the first known independent characterization of nebulized Rienso and, as such, provide critical information on the behavior of Rienso nanoparticles in an aerosol. The data obtained in this study add new knowledge to the existing body of literature on potential applications of SPION suspensions as inhaled aerosol therapeutics

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis. A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system

Prognostic systems representation in a function-based Bayesian model during engineering design

Prognostics and Health Management (PHM) systems are usually only considered and set up in the late stage of design or even during the system’s lifetime, after the major design decision have been made. However, considering the PHM system’s impact on the system failure probabilities can benefit the system design early on and subsequently reduce costs. The identification of failure paths in the early phases of engineering design can guide the designer toward a safer, more reliable and cost-efficient design. Several functional failure modeling methods have been developed recently. One of their advantages is to allow for risk assessment in the early stages of the design. Risk and reliability functional failure analysis methods currently developed do not explicitly model the PHM equipment used to identify and prevent potential system failures. This paper proposes a framework to optimize prognostic systems selection and positioning during the early stages of a complex system design. A Bayesian network, incorporating the PHM systems, is used to analyze the functional model and failure propagation. The algorithm developed within the proposed framework returns the optimized placement of PHM hardware in the complex system, allowing the designer to evaluate the need for system improvement. A design tool was developed to automatically apply the proposed method. A generic pressurized water nuclear reactor primary coolant loop system is used to present a case study illustrating the proposed framework. The results obtained for this particular case study demonstrate the promise of the method introduced in this paper. The case study notably exhibits how the proposed framework can be used to support engineering design teams in making better informed decisions early in the design phase

Recovery from multi‐millennial natural coastal hypoxia in the Stockholm Archipelago, Baltic Sea, terminated by modern human activity

Enhanced nutrient input and warming have led to the development of low oxygen (hypoxia) in coastal waters globally. For many coastal areas, insight into redox conditions prior to human impact is lacking. Here, we reconstructed bottom water redox conditions and sea surface temperatures (SSTs) for the coastal Stockholm Archipelago over the past 3000 yr. Elevated sedimentary concentrations of molybdenum indicate (seasonal) hypoxia between 1000b.c.e.and 1500c.e. Biomarker-based (TEX86) SST reconstructions indicate that the recovery from hypoxia after 1500c.e.coincided with a period of significant cooling (similar to 2 degrees C), while human activity in the study area, deduced from trends in sedimentary lead and existing paleobotanical and archeological records, had significantly increased. A strong increase in sedimentary lead and zinc, related to more intense human activity in the 18(th)and 19(th)century, and the onset of modern warming precede the return of hypoxia in the Stockholm Archipelago. We conclude that climatic cooling played an important role in the recovery from natural hypoxia after 1500c.e., but that eutrophication and warming, related to modern human activity, led to the return of hypoxia in the 20(th)century. Our findings imply that ongoing global warming may exacerbate hypoxia in the coastal zone of the Baltic Sea

Type Inference for Deadlock Detection in a Multithreaded Polymorphic Typed Assembly Language

We previously developed a polymorphic type system and a type checker for a multithreaded lock-based polymorphic typed assembly language (MIL) that ensures that well-typed programs do not encounter race conditions. This paper extends such work by taking into consideration deadlocks. The extended type system verifies that locks are acquired in the proper order. Towards this end we require a language with annotations that specify the locking order. Rather than asking the programmer (or the compiler's backend) to specifically annotate each newly introduced lock, we present an algorithm to infer the annotations. The result is a type checker whose input language is non-decorated as before, but that further checks that programs are exempt from deadlocks

NASA's High-Resolution GEOS Forecasting and Reanalysis Products: Support for TOLNet

Stratospheric intrusions (SIs) the introduction of ozone-rich stratospheric air into the troposphere have been the interest of decades of research for their link with surface ozone air quality exceedances, especially at the high elevations in the western USA in springtime; however, the impact of SIs in the remaining seasons and over the rest of the USA is less clear. We can expect MERRA-2 to realistically represent both atmospheric dynamics and composition. The operational GEOS weather forecasting system, GEOS-FP, has a similar ozone observing system to MERRA-2, while NASA's new global high-resolution air quality forecast system, GEOS-CF, combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12), simulating a wide range of additional air pollutants and tracers which strengthens this detailed analysis of the intrusions and the sources for the high ozone concentrations. Using a multitude of observational datasets, including lidar, air craft, ozonesondes and air quality monitoring surface sites, in combination with the GEOS forecast and reanalysis products, we aim to provide the public with tools which are available in near-real time to enhance their capability to identify the impact of stratospheric air on surface ozone concentrations separate from anthropogenic sources. In particular, improved understanding of the connections between large-scale climate variability and local-scale dynamically-driven air quality events may support improved seasonal prediction of SI events