Host gene expression classifiers diagnose acute respiratory illness etiology.

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.

Multi-Scale Visualization and Interpretation of Geological Relationships

Visualization and interpretation of geological observations into a cohesive geological model are essential to Earth sciences and related fields. Various emerging technologies offer approaches to multi-scale visualization of heterogeneous data, providing new opportunities that facilitate model development and interpretation processes. These include increased accessibility to 3D scanning technology, global connectivity, and Web-based interactive platforms. The geological sciences and geological engineering disciplines are adopting these technologies as volumes of data and physical samples greatly increase. However, a standardized and universally agreed upon workflow and approach have yet to properly be developed. In this thesis, the 3D scanning workflow is presented as a foundation for a virtual geological database. This database provides augmented levels of tangibility to students and researchers who have little to no access to locations that are remote or inaccessible. A Web-GIS platform was utilized jointly with customized widgets developed throughout the course of this research to aid in visualizing hand-sized/meso-scale geological samples within a geologic and geospatial context. This context is provided as a macro-scale GIS interface, where geophysical and geodetic images and data are visualized. Specifically, an interactive interface is developed that allows for simultaneous visualization to improve the understanding of geological trends and relationships. These developed tools will allow for rapid data access and global sharing, and will facilitate comprehension of geological models using multi-scale heterogeneous observations.

Irish Credit Unions: Differential Regulation based on Business Model Complexity

This study examines the business model complexity of Irish credit unions using a latent class approach to measure structural performance over the period 2002 to 2013. The latent class approach allows the endogenous identification of a multi-class framework for business models based on credit union specific characteristics. The analysis finds a three class system to be appropriate with the multi-class model dependent on three financial viability characteristics. This finding is consistent with the deliberations of the Irish Commission on Credit Unions (2012) which identified complexity and diversity in the business models of Irish credit unions and recommended that such complexity and diversity could not be accommodated within a one size fits all regulatory framework. The analysis also highlights that two of the classes are subject to diseconomies of scale. This may suggest credit unions would benefit from a reduction in scale or perhaps that there is an imbalance in the present change process. Finally, relative performance differences are identified for each class in terms of technical efficiency. This suggests that there is an opportunity for credit unions to improve their performance by using within-class best practice or alternatively by switching to another class.

Noncollinear Polarization Gating of Attosecond Pulse Trains in the Relativistic Regime

High order harmonics generated at relativistic intensities have long been recognized as a route to the most powerful extreme ultraviolet pulses. Reliably generating isolated attosecond pulses requires gating to only a single dominant optical cycle, but techniques developed for lower power lasers have not been readily transferable. We present a novel method to temporally gate attosecond pulse trains by combining noncollinear and polarization gating. This scheme uses a split beam configuration which allows pulse gating to be implemented at the high beam fluence typical of multi-TW to PW class laser systems. Scalings for the gate width demonstrate that isolated attosecond pulses are possible even for modest pulse durations achievable for existing and planned future ultrashort high-power laser systems. Experimental results demonstrating the spectral effects of temporal gating on harmonic spectra generated by a relativistic laser plasma interaction are shown.

Evolution of an Interactive Online Magazine for Students, Academics and Expert Practitioners to Engage Students in ESD

Urgency to embed awareness of sustainability principles and practice across society, and need for digital literacy and advocacy for sustainability are reshaping ESD. These, together with developments in learning and teaching, demand new tools to support implementation of project-based learning and more interactive approaches. This investigation explores the evolution of susthingsout.com, an online magazine for students, academics and expert practitioners, developed by the University of Worcester. This comprises two parts; the first, a private site specifically for students involved in sustainability learning on-campus; the second, an open-access site developed to deliver sustainability information and good practice across campus, community and not-for-profit and commercial organisations. This paper involves only the private site i.e. the equivalent of an in-house VLE specifically designed to support the teaching of sustainability to multi-disciplinary first and second year undergraduate students. It reports on the progress of the VLE, following three years of use and initial improvements, in terms of the student support and engagement, as well as considering the practical issues affecting these. The results fall into four categories of pedagogical, operational, cultural and external factors, which are synthesised to capture and share emerging knowledge of good practice offering insights to other developers of online sustainability materials.

The virtual class as a knowledge democratization model

This paper presents the conceptualization and use of a virtual classroom in the course EIF-200 Fundamentos de Informática, first course in the Information Systems Engineering career of the Universidad Nacional of Costa Rica. The virtual classroom is seen as a complement to the class and is conceived as a space that allows to centralize teaching resources, thereby promoting the  democratization of knowledge among students and teachers. Furthermore, this concept of virtual classroom helps to reduce the culture of individualism, present many times in university teaching practices, and contributes to create new opportunities to learn from other colleagues within a culture of reflection, analysis and respectful dialogue aimed to improve the teaching practices.

Analysis and modelling of immune cell behaviour in lymph nodes based on multi-photon imaging

This thesis presents quantitative studies of T cell and dendritic cell (DC) behaviour in mouse lymph nodes (LNs) in the naive state and following immunisation. These processes are of importance and interest in basic immunology, and better understanding could improve both diagnostic capacity and therapeutic manipulations, potentially helping in producing more effective vaccines or developing treatments for autoimmune diseases. The problem is also interesting conceptually as it is relevant to other fields where 3D movement of objects is tracked with a discrete scanning interval. A general immunology introduction is presented in chapter 1. In chapter 2, I apply quantitative methods to multi-photon imaging data to measure how T cells and DCs are spatially arranged in LNs. This has been previously studied to describe differences between the naive and immunised state and as an indicator of the magnitude of the immune response in LNs, but previous analyses have been generally descriptive. The quantitative analysis shows that some of the previous conclusions may have been premature. In chapter 3, I use Bayesian state-space models to test some hypotheses about the mode of T cell search for DCs. A two-state mode of movement where T cells can be classified as either interacting to a DC or freely migrating is supported over a model where T cells would home in on DCs at distance through for example the action of chemokines. In chapter 4, I study whether T cell migration is linked to the geometric structure of the fibroblast reticular network (FRC). I find support for the hypothesis that the movement is constrained to the fibroblast reticular cell (FRC) network over an alternative 'random walk with persistence time' model where cells would move randomly, with a short-term persistence driven by a hypothetical T cell intrinsic 'clock'. I also present unexpected results on the FRC network geometry. Finally, a quantitative method is presented for addressing some measurement biases inherent to multi-photon imaging. In all three chapters, novel findings are made, and the methods developed have the potential for further use to address important problems in the field. In chapter 5, I present a summary and synthesis of results from chapters 3-4 and a more speculative discussion of these results and potential future directions.

All-regime Lagrangian-Remap numerical schemes for the gas dynamics equations. Applications to the large friction and low Mach coefficients

In this talk, we propose an all regime Lagrange-Projection like numerical scheme for the gas dynamics equations. By all regime, we mean that the numerical scheme is able to compute accurate approximate solutions with an under-resolved discretization with respect to the Mach number M, i.e. such that the ratio between the Mach number M and the mesh size or the time step is small with respect to 1. The key idea is to decouple acoustic and transport phenomenon and then alter the numerical flux in the acoustic approximation to obtain a uniform truncation error in term of M. This modified scheme is conservative and endowed with good stability properties with respect to the positivity of the density and the internal energy. A discrete entropy inequality under a condition on the modification is obtained thanks to a reinterpretation of the modified scheme in the Harten Lax and van Leer formalism. A natural extension to multi-dimensional problems discretized over unstructured mesh is proposed. Then a simple and efficient semi implicit scheme is also proposed. The resulting scheme is stable under a CFL condition driven by the (slow) material waves and not by the (fast) acoustic waves and so verifies the all regime property. Numerical evidences are proposed and show the ability of the scheme to deal with tests where the flow regime may vary from low to high Mach values.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.

Three Essays on Dual-Class Stock Structure

Dual-class stock structure is characterized by the separation of voting rights and cash flow rights. The departure from a common “one share-one vote” configuration creates ideal conditions for conflicts of interest and agency problems between controlling insiders (the holders of voting rights) and remaining shareholders. The owners of voting rights have the opportunity to extract private benefits and act in their personal interest; as a result, dual-class firms are often perceived to have low transparency and high information asymmetry. This dissertation investigates the quality of information and the information environment of firms with two classes of stock. The first essay examines the quality of information by studying accruals in dual-class firms in comparison to firms with only one class of stock. The results suggest that the quality of accruals is better in dual-class firms than in single-class firms. In addition, the difference in the quality of accruals between firms that abolish their dual-class share structure by unification and singe-class firms disappears in the post-unification period. The second essay investigates the earnings informativeness of dual-class firms by examining the explanatory power of earnings for returns. The results indicate that the earnings informativeness is lower for dual-class firms as compared to single-class firms. Earnings informativeness improves in firms that unify their shares. The third essay compares the level of information asymmetry between dual-class firms and single-class firms. It is documented that the information environment for dual-class firms is worse than for single-class firms. Also, the finding suggests that the difference in information environment between dual-class firms and single-class firms disappears after dual-class stock unification.

A Grammar of Tajio. A Language Spoken in Central Sulawesi

This work is a description of Tajio, a Western Malayo-Polynesian language spoken in Central Sulawesi, Indonesia. It covers the essential aspects of Tajio grammar without being exhaustive. Tajio has a medium sized phoneme inventory consisting of twenty consonants and five vowels. The language does not have lexical (word) stress; rather, it has a phrasal accent. This phrasal accent regularly occurs on the penultimate syllable of an intonational phrase, rendering this syllable auditorily prominent through a pitch rise. Possible syllable structures in Tajio are (C)V(C). CVN structures are allowed as closed syllables, but CVN syllables in word-medial position are not frequent. As in other languages in the area, the only sequence of consonants allowed in native Tajio words are sequences of nasals followed by a homorganic obstruent. The homorganic nasal-obstruent sequences found in Tajio can occur word-initially and word-medially but never in word-final position. As in many Austronesian languages, word class classification in Tajio is not straightforward. The classification of words in Tajio must be carried out on two levels: the morphosyntactic level and the lexical level. The open word classes in Tajio consist of nouns and verbs. Verbs are further divided into intransitive verbs (dynamic intransitive verbs and statives) and dynamic transitive verbs. Based on their morphological potential, lexical roots in Tajio fall into three classes: single-class roots, dual-class roots and multi-class roots. There are two basic transitive constructions in Tajio: Actor Voice and Undergoer Voice, where the actor or undergoer argument respectively serves as subjects. It shares many characteristics with symmetrical voice languages, yet it is not fully symmetric, as arguments in AV and UV are not equally marked. Neither subjects nor objects are marked in AV constructions. In UV constructions, however, subjects are unmarked while objects are marked either by prefixation or clitization. Evidence from relativization, control and raising constructions supports the analysis that AV and UV are in fact transitive, with subject arguments and object arguments behaving alike in both voices. Only the subject can be relativized, controlled, raised or function as the implicit subject of subjectless adverbial clauses. In contrast, the objects of AV and UV constructions do not exhibit these features. Tajio is a predominantly head-marking language with basic A-V-O constituent order. V and O form a constituent, and the subject can either precede or follow this complex. Thus, basic word order is S-V-O or V-O-S. Subject, as well as non-subject arguments, may be omitted when contextually specified. Verbs are marked for voice and mood, the latter of which is is obligatory. The two values distinguished are realis and non-realis. Depending on the type of predicate involved in clause formation, three clause types can be distinguished: verbal clauses, existential clauses and non-verbal clauses. Tajio has a small number of multi-verbal structures that appear to qualify as serial verb constructions. SVCs in Tajio always include a motion verb or a directional.

Development of new chemical entities to target neuroinflammatory pathways.

Neuroinflammatory pathways are main culprits of neurodegenerative diseases' onset and progression, including Alzheimer’s disease (AD). On this basis, several anti-inflammatory drugs were repurposed in clinical trials. However, they have failed, probably because neuroinflammation is a complex network, still not fully understood. From these evidences, this thesis focused on the design and synthesis of new chemical entities as potential neuroinflammatory drugs or chemical probes. Projects 1 and 2 aimed to multi-target-directed ligand (MTDL) development to target neuroinflammation in AD. Polypharmacology by MTDLs is considered one of the most promising strategies to face the multifactorial nature of neurodegenerative diseases. Particularly, Project 1 took inspiration from a cromolyn-ibuprofen drug combination polypharmacological approach, which was recently investigated in AD clinical trials. Based on that, two cromolyn-(S)-ibuprofen codrug series were designed and synthesized. Parent drugs were combined via linking or fusing strategies in 1:2 or 1:1 ratio, by means of hydrolyzable bonds. Project 2 started from a still ongoing AD clinical trial on investigational drug neflamapimod. It is a selective inhibitor of p38α-MAPK, a kinase strictly involved in neuroinflammatory pathways. On the other side, rasagiline, an anti-Parkinson drug, was also repurposed as AD treatment. Indeed, rasagiline’s propargylamine fragment demonstrated to be responsible not only for the MAO-B selective inhibition, but also for the neuroprotective activity. Thus, to synergistically combine these two effects into single-molecules, a small set of neflamapimod-rasagiline hybrids was developed. In the end BMX, a poorly investigated kinase, which seems to be involved in pro-inflammatory mediator production, was explored for the development of new chemical probes. High-quality chemical probes are a powerful tool in target validation and starting points for the development of new drug candidates. Thus, Project 3 focused on the design and synthesis of two series of optimized BMX covalent inhibitors as selective chemical probes.

Outcomes of global coagulation assays in patients with Philadelphia-negative myeloproliferative neoplasms with respect to genetic determinants of clonal progression

Classical myeloproliferative neoplasms (MPNs) are hematopoietic stem cell disorders that manifest with inflammation, promotion of atherosclerosis, hypercoagulability, fibrosis, and clonal evolution. The complex biological background lends itself to multi-omics studies. We have previously shown that reduced platelet fibrinogen receptor (PFR) expression may follow hyperactivation of plasma-dependent mechanisms, such as tissue factor (TF) release, unbalanced thrombin generation, involvement of protease-activated receptors (PARs). Acetylsalicylic acid (ASA) helped to restore the expression of PFRs. In this study, we enrolled 53 MPN patients, subjecting them to advanced genetic testing (panel of 30 genes in NGS), global coagulation testing (Rotational Thromboelastometry - ROTEM) and cytofluorometric determination of PFRs. ROTEM parameters appear to differ considerably depending on the type of pathology under investigation, cell count, and selected mutations. Essential thrombocythemia (ET) and CALR mutation appear to correlate with increased efficiency of both classical coagulation pathways, with significantly more contracted clot formation times (CFTs). In contrast, primary myelofibrosis (PMF) and polycythemia vera (PV) show greater imbalances in the hemostatic system. PV, probably due to its peculiar hematological features, shows a lengthening of the CFT and, at the same time, a selective contraction of parameters in INTEM with the increase of platelets and white blood cells. PMF - in contrast - seems to exploit the extrinsic pathway more to increase cell numbers. The presence of DNMT3A mutations is associated with reduced clotting time (CT) in EXTEM, while ASXL1 causes reduced maximal lysis (ML). EZH2 could be responsible for the elongation of CFT in INTEM assay. In addition, increased PFR expression is associated with history of hemorrhage and sustained CT time in FIBTEM under ASA prophylaxis. Our findings corroborate the existing models on the connection between fibrosis, genetic complexity, clonal progression, and hypercoagulability. Global coagulation assays and PFR expression are potentially useful tools for dynamic evaluation of treatments’ outcomes.

Multi-scalar critical theories: first steps for a non perturbative functional renormalization group analysis

In this work the fundamental ideas to study properties of QFTs with the functional Renormalization Group are presented and some examples illustrated. First the Wetterich equation for the effective average action and its flow in the local potential approximation (LPA) for a single scalar field is derived. This case is considered to illustrate some techniques used to solve the RG fixed point equation and study the properties of the critical theories in D dimensions. In particular the shooting methods for the ODE equation for the fixed point potential as well as the approach which studies a polynomial truncation with a finite number of couplings, which is convenient to study the critical exponents. We then study novel cases related to multi field scalar theories, deriving the flow equations for the LPA truncation, both without assuming any global symmetry and also specialising to cases with a given symmetry, using truncations based on polynomials of the symmetry invariants. This is used to study possible non perturbative solutions of critical theories which are extensions of known perturbative results, obtained in the epsilon expansion below the upper critical dimension.

Intermediate linguistic task fine-tuning on Multi-lingual models

State-of-the-art NLP systems are generally based on the assumption that the underlying models are provided with vast datasets to train on. However, especially when working in multi-lingual contexts, datasets are often scarce, thus more research should be carried out in this field. This thesis investigates the benefits of introducing an additional training step when fine-tuning NLP models, named Intermediate Training, which could be exploited to augment the data used for the training phase. The Intermediate Training step is applied by training models on NLP tasks that are not strictly related to the target task, aiming to verify if the models are able to leverage the learned knowledge of such tasks. Furthermore, in order to better analyze the synergies between different categories of NLP tasks, experimentations have been extended also to Multi-Task Training, in which the model is trained on multiple tasks at the same time.