Functional Evaluation of Causal Mutations Identified in Human Genetic Studies

Human genetics has been experiencing a wave of genetic discoveries thanks to the development of several technologies, such as genome-wide association studies (GWAS), whole-exome sequencing, and whole genome sequencing. Despite the massive genetic discoveries of new variants associated with human diseases, several key challenges emerge following the genetic discovery. GWAS is known to be good at identifying the locus associated with the patient phenotype. However, the actually causal variants responsible for the phenotype are often elusive. Another challenge in human genetics is that even the causal mutations are already known, the underlying biological effect might remain largely ambiguous. Functional evaluation plays a key role to solve these key challenges in human genetics both to identify causal variants responsible for the phenotype, and to further develop the biological insights from the disease-causing mutations.

We adopted various methods to characterize the effects of variants identified in human genetic studies, including patient genetic and phenotypic data, RNA chemistry, molecular biology, virology, and multi-electrode array and primary neuronal culture systems. Chapter 1 is a broader introduction for the motivation and challenges for functional evaluation in human genetic studies, and the background of several genetics discoveries, such as hepatitis C treatment response, in which we performed functional characterization.

Chapter 2 focuses on the characterization of causal variants following the GWAS study for hepatitis C treatment response. We characterized a non-coding SNP (rs4803217) of IL28B (IFNL3) in high linkage disequilibrium (LD) with the discovery SNP identified in the GWAS. In this chapter, we used inter-disciplinary approaches to characterize rs4803217 on RNA structure, disease association, and protein translation.

Chapter 3 describes another avenue of functional characterization following GWAS focusing on the novel transcripts and proteins identified near the IL28B (IFNL3) locus. It has been recently speculated that this novel protein, which was named IFNL4, may affect the HCV treatment response and clearance. In this chapter, we used molecular biology, virology, and patient genetic and phenotypic data to further characterize and understand the biology of IFNL4. The efforts in chapter 2 and 3 provided new insights to the candidate causal variant(s) responsible for the GWAS for HCV treatment response, however, more evidence is still required to make claims for the exact causal roles of these variants for the GWAS association.

Chapter 4 aims to characterize a mutation already known to cause a disease (seizure) in a mouse model. We demonstrate the potential use of multi-electrode array (MEA) system for the functional characterization and drug testing on mutations found in neurological diseases, such as seizure. Functional characterization in neurological diseases is relatively challenging and available systematic tools are relatively limited. This chapter shows an exploratory research and example to establish a system for the broader use for functional characterization and translational opportunities for mutations found in neurological diseases.

Overall, this dissertation spans a range of challenges of functional evaluations in human genetics. It is expected that the functional characterization to understand human mutations will become more central in human genetics, because there are still many biological questions remaining to be answered after the explosion of human genetic discoveries. The recent advance in several technologies, including genome editing and pluripotent stem cells, is also expected to make new tools available for functional studies in human diseases.

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Formal Modeling and Analysis Techniques for High Level Petri Nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.

To ISDS or not to ISDS: A question of legitimacy in dispute resolution

Investor State Dispute Settlement (ISDS) has gained prominence in recent years with an explosion in the number of investor claims against states. While the evolution of this type of arbitration was expected, its focus and context was not. Investors are currently bringing actions against developed states in unanticipated policy areas. Greece, facing actions from investors challenging its debt haircut and Spain, battling investor challenges to its revamped energy policy are examples of the use of arbitration as a political as well as a dispute resolution tool. It is for this reason why the proposal for the inclusion of ISDS in the Transatlantic Trade and Investment Partnership (TTIP) has caused so much heated discussion. This paper examines the recent evolution and likely trajectory of investor state dispute settlement, reflecting on consequences for perceptions of arbitration and its links with politics and economics.

New Frontiers in Energy-Efficient Computing

Energy-efficient computing remains a critical challenge across the wide range of future data-processing engines — from ultra-low-power embedded systems to servers, mainframes, and supercomputers. In addition, the advent of cloud and mobile computing as well as the explosion of IoT technologies have created new research challenges in the already complex, multidimensional space of modern and future computer systems. These new research challenges led to the establishment of the IEEE Rebooting Computing Initiative, which specifically addresses novel low-power solutions and technologies as one of the main areas of concern.With this in mind, we thought it timely to survey the state of the art of energy-efficient computing.

Travel, back and forth, to the myth. Cinema´s contribution to the formation of the tourist iconography of Mallorca

Mallorca, the largest of the Balearic Islands, is a well-known summer holidays destination; an ideal place to relax and enjoy the sun and the sea. That tourist gaze reflected on postcards results from advertising campaigns, where cinema played an important role with documentaries and fiction films. The origins of that iconography started in the decades of the 1920’s and 1930’s, reflecting the so-called myth of the “island of calm”. On the other hand, the films of the 1950’s and 1960’s created new stereotypes related to the mass tourism boom. Busy beaches and the white bodies of tourists replaced white sandy beaches, mountains and landscapes shown up in the movies of the early decades of the 20th century. Besides, hotels and nightclubs also replaced monuments, rural landscapes and folk exhibitions. These tourist images mirror the social and spatial transformations of Mallorca, under standardization processes like other seaside mass tourist destinations. The identity was rebuilt on the foundations of "modernity". Although "balearization" has not ceased, nowadays filmmaking about Mallorca is advertising again a stereotype close to that one of the 1920s and 1930s, glorifying the myth of the "island of calm". This singular identity makes the island more profitable for capital that searches socio-spatial differentiation in post-fordist times.

Mental logic and its difficulties with disjunction

The mental logic theory does not accept the disjunction introduction rule of standard propositional calculus as a natural schema of the human mind. In this way, the problem that I want to show in this paper is that, however, that theory does admit another much more complex schema in which the mentioned rule must be used as a previous step. So, I try to argue that this is a very important problem that the mental logic theory needs to solve, and claim that another rival theory, the mental models theory, does not have these difficulties.

Type Ia supernovae from violent mergers of carbon-oxygen white dwarfs: polarization signatures

The violent merger of two carbon-oxygen white dwarfs has been proposed as a viable progenitor for some Type Ia supernovae. However, it has been argued that the strong ejecta asymmetries produced by this model might be inconsistent with the low degree of polarization typically observed in Type Ia supernova explosions. Here, we test this claim by carrying out a spectropolarimetric analysis for the model proposed by Pakmor et al. for an explosion triggered during the merger of a 1.1 and 0.9 M⊙ carbon-oxygen white dwarf binary system. Owing to the asymmetries of the ejecta, the polarization signal varies significantly with viewing angle. We find that polarization levels for observers in the equatorial plane are modest (≲1 per cent) and show clear evidence for a dominant axis, as a consequence of the ejecta symmetry about the orbital plane. In contrast, orientations out of the plane are associated with higher degrees of polarization and departures from a dominant axis. While the particular model studied here gives a good match to highly polarized events such as SN 2004dt, it has difficulties in reproducing the low polarization levels commonly observed in normal Type Ia supernovae. Specifically, we find that significant asymmetries in the element distribution result in a wealth of strong polarization features that are not observed in the majority of currently available spectropolarimetric data of Type Ia supernovae. Future studies will map out the parameter space of the merger scenario to investigate if alternative models can provide better agreement with observations.

The type Iax supernova, SN 2015H: A white dwarf deflagration candidate

We present results based on observations of SN 2015H which belongs to the small group of objects similar to SN 2002cx, otherwise known as type Iax supernovae. The availability of deep pre-explosion imaging allowed us to place tight constraints on the explosion epoch. Our observational campaign began approximately one day post-explosion, and extended over a period of about 150 days post maximum light, making it one of the best observed objects of this class to date. We find a peak magnitude of M_r = -17.27± 0.07, and a (Δm₁₅)_r = 0.69 ± 0.04. Comparing our observations to synthetic spectra generated from simulations of deflagrations of Chandrasekhar mass carbon-oxygen white dwarfs, we find reasonable agreement with models of weak deflagrations that result in the ejection of ∼0.2 M_⊙ of material containing ∼0.07 M_⊙ of ⁵⁶Ni. The model light curve however, evolves more rapidly than observations, suggesting that a higher ejecta mass is to be favoured. Nevertheless, empirical modelling of the pseudo-bolometric light curve suggests that ≲ 0.6 M_⊙ of material was ejected, implying that the white dwarf is not completely disrupted, and that a bound remnant is a likely outcome.

SN 2005at - A neglected type Ic supernova at 10 Mpc

We present a photometric and spectroscopic study of a reddened type Ic supernova (SN) 2005at. We report our results based on the available data of SN 2005at, including late-time observations from the Spitzer Space Telescope and the Hubble Space Telescope. In particular, late-time mid-infrared observations are something rare for type Ib/c SNe. In our study we find SN 2005at to be very similar photometrically and spectroscopically to another nearby type Ic SN 2007gr, underlining the prototypical nature of this well-followed type Ic event. The spectroscopy of both events shows similar narrow spectral line features. The radio observations of SN 2005at are consistent with fast evolution and low luminosity at radio wavelengths. The late-time Spitzer data suggest the presence of an unresolved light echo from interstellar dust and dust formation in the ejecta, both of which are unique observations for a type Ic SN. The late-time Hubble observations reveal a faint point source coincident with SN 2005at, which is very likely either a declining light echo of the SN or a compact cluster. For completeness we study ground-based pre-explosion archival images of the explosion site of SN 2005at, however this only yielded very shallow upper limits for the SN progenitor star. We derive a host galaxy extinction of A_V ∼ 1.9 mag for SN 2005at, which is relatively high for a SN in a normal spiral galaxy not viewed edge-on.

Searching for swept-up hydrogen and helium in the late-time spectra of 11 nearby Type Ia supernovae

The direct detection of a stellar system that explodes as a Type Ia supernova (SN Ia) has not yet been successful. Various indirect methods have been used to investigate SN Ia progenitor systems but none have produced conclusive results. A prediction of single-degenerate models is that H- (or He-) rich material from the envelope of the companion star should be swept up by the SN ejecta in the explosion. Seven SNe Ia have been analysed to date looking for signs of H-rich material in their late-time spectra and none were detected. We present results from new late-time spectra of 11 SNe Ia obtained at the Very Large Telescope using XShooter and FORS2. We present the tentative detection of Hα emission for SN 2013ct, corresponding to ∼0.007 M_⊙ of stripped/ablated companion star material (under the assumptions of the spectral modelling). This mass is significantly lower than expected for single-degenerate scenarios, suggesting that >0.1 M_⊙ of H-rich is present but not observed. We do not detect Hα emission in the other 10 SNe Ia. This brings the total sample of normal SNe Ia with non-detections (<0.001–0.058 M_⊙) of H-rich material to 17 events. The simplest explanation for these non-detections is that these objects did not result from the explosion of a CO white dwarf accreting matter from a H-rich companion star via Roche lobe overflow or symbiotic channels. However, further spectral modelling is needed to confirm this. We also find no evidence of He-emission features, but models with He-rich companion stars are not available to place mass limits.

A simple approach to the supernova progenitor–explosion connection

We present a new approach to understand the landscape of supernova explosion energies, ejected nickel masses, and neutron star birth masses. In contrast to other recent parametric approaches, our model predicts the properties of neutrino-driven explosions based on the pre-collapse stellar structure without the need for hydrodynamic simulations. The model is based on physically motivated scaling laws and simple differential equations describing the shock propagation, the contraction of the neutron star, the neutrino emission, the heating conditions, and the explosion energetics. Using model parameters compatible with multi-D simulations and a fine grid of thousands of supernova progenitors, we obtain a variegated landscape of neutron star and black hole formation similar to other parametrized approaches and find good agreement with semi-empirical measures for the ‘explodability’ of massive stars. Our predicted explosion properties largely conform to observed correlations between the nickel mass and explosion energy. Accounting for the coexistence of outflows and downflows during the explosion phase, we naturally obtain a positive correlation between explosion energy and ejecta mass. These correlations are relatively robust against parameter variations, but our results suggest that there is considerable leeway in parametric models to widen or narrow the mass ranges for black hole and neutron star formation and to scale explosion energies up or down. Our model is currently limited to an all-or-nothing treatment of fallback and there remain some minor discrepancies between model predictions and observational constraints.

The dynamics of neutrino-driven supernova explosions after shock revival in 2D and 3D

We study the growth of the explosion energy after shock revival in neutrino-driven explosions in two and three dimensions (2D/3D) using multi-group neutrino hydrodynamics simulations of an 11.2 M⊙ star. The 3D model shows a faster and steadier growth of the explosion energy and already shows signs of subsiding accretion after one second. By contrast, the growth of the explosion energy in 2D is unsteady, and accretion lasts for several seconds as confirmed by additional long-time simulations of stars of similar masses. Appreciable explosion energies can still be reached, albeit at the expense of rather high neutron star masses. In 2D, the binding energy at the gain radius is larger because the strong excitation of downward-propagating g modes removes energy from the freshly accreted material in the downflows. Consequently, the mass outflow rate is considerably lower in 2D than in 3D. This is only partially compensated by additional heating by outward-propagating acoustic waves in 2D. Moreover, the mass outflow rate in 2D is reduced because much of the neutrino energy deposition occurs in downflows or bubbles confined by secondary shocks without driving outflows. Episodic constriction of outflows and vertical mixing of colder shocked material and hot, neutrino-heated ejecta due to Rayleigh–Taylor instability further hamper the growth of the explosion energy in 2D. Further simulations will be necessary to determine whether these effects are generic over a wider range of supernova progenitors.

A Data Driven Tool supported CLD Creation Approach

The creation of Causal Loop Diagrams (CLDs) is a major phase in the System Dynamics (SD) life-cycle, since the created CLDs express dependencies and feedback in the system under study, as well as, guide modellers in building meaningful simulation models. The cre-ation of CLDs is still subject to the modeller's domain expertise (mental model) and her ability to abstract the system, because of the strong de-pendency on semantic knowledge. Since the beginning of SD, available system data sources (written and numerical models) have always been sparsely available, very limited and imperfect and thus of little benefit to the whole modelling process. However, in recent years, we have seen an explosion in generated data, especially in all business related domains that are analysed via Business Dynamics (BD). In this paper, we intro-duce a systematic tool supported CLD creation approach, which analyses and utilises available disparate data sources within the business domain. We demonstrate the application of our methodology on a given business use-case and evaluate the resulting CLD. Finally, we propose directions for future research to further push the automation in the CLD creation and increase confidence in the generated CLDs.

Predicting polarization signatures for double-detonation and delayed-detonation models of Type Ia supernovae

Calculations of synthetic spectropolarimetry are one means to test multidimensional explosion models for Type Ia supernovae. In a recent paper, we demonstrated that the violent merger of a 1.1 and 0.9 M⊙ white dwarf binary system is too asymmetric to explain the low polarization levels commonly observed in normal Type Ia supernovae. Here, we present polarization simulations for two alternative scenarios: the sub-Chandrasekhar mass double-detonation and the Chandrasekhar mass delayed-detonation model. Specifically, we study a 2D double-detonation model and a 3D delayed-detonation model, and calculate polarization spectra for multiple observer orientations in both cases. We find modest polarization levels (<1 per cent) for both explosion models. Polarization in the continuum peaks at ∼0.1–0.3 per cent and decreases after maximum light, in excellent agreement with spectropolarimetric data of normal Type Ia supernovae. Higher degrees of polarization are found across individual spectral lines. In particular, the synthetic Si II λ6355 profiles are polarized at levels that match remarkably well the values observed in normal Type Ia supernovae, while the low degrees of polarization predicted across the O I λ7774 region are consistent with the non-detection of this feature in current data. We conclude that our models can reproduce many of the characteristics of both flux and polarization spectra for well-studied Type Ia supernovae, such as SN 2001el and SN 2012fr. However, the two models considered here cannot account for the unusually high level of polarization observed in extreme cases such as SN 2004dt.