Metabolic profiling on 2D NMR TOCSY spectra using machine learning

Due to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis

Voice Modeling Methods for Automatic Speaker Recognition

Building a voice model means to capture the characteristics of a speaker´s voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Several challenges arise in the context of automatic speaker recognition. First, there is the problem of data shortage, i.e., the unavailability of sufficiently long utterances for speaker recognition. It stems from the fact that the speech signal conveys different aspects of the sound in a single, one-dimensional time series: linguistic (what is said?), prosodic (how is it said?), individual (who said it?), locational (where is the speaker?) and emotional features of the speech sound itself (to name a few) are contained in the speech signal, as well as acoustic background information. To analyze a specific aspect of the sound regardless of the other aspects, analysis methods have to be applied to a specific time scale (length) of the signal in which this aspect stands out of the rest. For example, linguistic information (i.e., which phone or syllable has been uttered?) is found in very short time spans of only milliseconds of length. On the contrary, speakerspecific information emerges the better the longer the analyzed sound is. Long utterances, however, are not always available for analysis. Second, the speech signal is easily corrupted by background sound sources (noise, such as music or sound effects). Their characteristics tend to dominate a voice model, if present, such that model comparison might then be mainly due to background features instead of speaker characteristics. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. For example, the typical speaker turn duration of only one second and the sound effect background in cinematic movies render most current automatic analysis techniques useless. In this thesis, methods for voice modeling that are robust with respect to short utterances and background noise are presented. The aim is to facilitate movie analysis with respect to occurring speakers. Therefore, algorithmic improvements are suggested that (a) improve the modeling of very short utterances, (b) facilitate voice model building even in the case of severe background noise and (c) allow for efficient voice model comparison to support the indexing of large multimedia archives. The proposed methods improve the state of the art in terms of recognition rate and computational efficiency. Going beyond selective algorithmic improvements, subsequent chapters also investigate the question of what is lacking in principle in current voice modeling methods. By reporting on a study with human probands, it is shown that the exclusion of time coherence information from a voice model induces an artificial upper bound on the recognition accuracy of automatic analysis methods. A proof-of-concept implementation confirms the usefulness of exploiting this kind of information by halving the error rate. This result questions the general speaker modeling paradigm of the last two decades and presents a promising new way. The approach taken to arrive at the previous results is based on a novel methodology of algorithm design and development called “eidetic design". It uses a human-in-the-loop technique that analyses existing algorithms in terms of their abstract intermediate results. The aim is to detect flaws or failures in them intuitively and to suggest solutions. The intermediate results often consist of large matrices of numbers whose meaning is not clear to a human observer. Therefore, the core of the approach is to transform them to a suitable domain of perception (such as, e.g., the auditory domain of speech sounds in case of speech feature vectors) where their content, meaning and flaws are intuitively clear to the human designer. This methodology is formalized, and the corresponding workflow is explicated by several use cases. Finally, the use of the proposed methods in video analysis and retrieval are presented. This shows the applicability of the developed methods and the companying software library sclib by means of improved results using a multimodal analysis approach. The sclib´s source code is available to the public upon request to the author. A summary of the contributions together with an outlook to short- and long-term future work concludes this thesis

New Work—New Interventions: Digital Occupational Health Interventions and the Co-Creation of a Human-Centered Future of Work

Humans are making use of digital technologies to profoundly transform their working tasks and systems. Psychologists who design interventions to improve health and well-being at the workplace can follow two approaches regarding this transformation: (a) they will make targeted use of the emerging digital technologies themselves and design what we label “digital occupational health interventions” (DOHI), and (b) they will try to influence the ongoing digital transformation in terms of healthy change and work design, thus co-creating the future of work. In this paper, we first aim to provide a narrative and visual synthesis of the techniques and topics behind DOHI, illustrated by examples and followed by a discussion of limitations and opportunities. Secondly, we aim to provide an impulse on how the ongoing transformation of work could be co-created by organizations, their members, and occupational health experts who can contribute their knowledge of human-centered design principles to the future of work

Impact of life style factors on atherosclerosis:a modelling based study

Atherosclerosis is a low density cholesterol promoted medical condition in which the walls of the artery thicken due to fatty acid, cholesterol deposition (plaque). Such medical aggravations are known to escalate coronary and cardio-vascular heart diseases (CHD & CVD). This thesis models the time dynamical evolution of atherosclerosis,and in turn coronary heart disease (CHD), as a function of natural ageing and affectation due to life-style parameters like alcohol consumption, cheese consumption,smoking habit, high blood pressure, cereal-fruit-vegtable consumption. Principally based on data modelling (13 European countries, including the UK, have been analysed), followed by a continuum model based prediction, the thesis probabilistically estimates how a change in life style factors could help in controlling CHD/atherosclerosis. The thesis is structured within three major sections. First, real data from open access databases (WHO & FAO) were analysed using standard statistical tools to establish dependence of CHD rates on the aforementioned lifestyle and ageing parameters.Two major conclusions could be drawn: a) linear dependence of all life style parameters on time, in the post-statin era; b) CHD death rate analysis demarcated the importance of statin usage in medical optimisation of life style factors. Second, joint variation of (many, if not all) available parameters, including their inter-dependence, was analysed using machine learning based data visualization tools, like Principal Component Analysis (PCA) and NeuroScale (NSC). Two-fold conclusions were drawn: a) low dimensional clustering of high dimensional data established the interdependence of certain parameters; b) a key outcome of this research is the quantification of the moderating influence of the healthy lifestyle factors (fruit/vegetable and cereal consumption) on the negative indicators (systolic blood pressure, smoking, alcohol and cheesy food). This result is expected to lead to a major life saving tool for medical personnel in advising patients on what to eat, how much to eat, and what not to eat. Combining information from the two sections above, a time varying model was developed that could predict how the population biology data based conclusions could be probabilistically projected to make future predictions of patient behaviour and concerned life expectations related to CHD deaths. This work is presently ongoing

Mediating Vulnerability: Comparative approaches and questions of genre

Mediating Vulnerability examines vulnerability from a range of connected perspectives. It responds to the vulnerability of species, their extinction but also their transformation. This tension between extreme danger and creativity is played out in literary studies through the pressures the discipline brings to bear on its own categories, particularly those of genre. Extinction and preservation on the one hand, transformation, adaptation and (re)mediation on the other. These two poles inform our comparative and interdisciplinary project. The volume is situated within the particular intercultural and intermedial context of contemporary cultural representation. Vulnerability is explored as a site of potential destruction, human as well as animal, but also as a site of potential openness. This is the first book to bring vulnerability studies into dialogue with media and genre studies. It is organised in four sections: ‘Human/Animal’; Violence/Resistance’; ‘Image/Narrative’; and ‘Medium/Genre’. Each chapter considers the intersection of vulnerability and genre from a comparative perspective, bringing together a team of international contributors and editors. The book is in dialogue with the reflections of Judith Butler and others on vulnerability, and it questions categories of genre through an interdisciplinary engagement with different representational forms, including digital culture, graphic novels, video games, photography and TV series, in addition to novels and short stories. It offers new readings of high-profile contemporary authors of fiction including Margaret Atwood and Cormac McCarthy, as well as bringing lesser-known figures to the fore

Mediating Vulnerability

Mediating Vulnerability examines vulnerability from a range of connected perspectives. It responds to the vulnerability of species, their extinction but also their transformation. This tension between extreme danger and creativity is played out in literary studies through the pressures the discipline brings to bear on its own categories, particularly those of genre. Extinction and preservation on the one hand, transformation, adaptation and (re)mediation on the other. These two poles inform our comparative and interdisciplinary project. The volume is situated within the particular intercultural and intermedial context of contemporary cultural representation. Vulnerability is explored as a site of potential destruction, human as well as animal, but also as a site of potential openness. This is the first book to bring vulnerability studies into dialogue with media and genre studies. It is organised in four sections: ‘Human/Animal’; Violence/Resistance’; ‘Image/Narrative’; and ‘Medium/Genre’. Each chapter considers the intersection of vulnerability and genre from a comparative perspective, bringing together a team of international contributors and editors. The book is in dialogue with the reflections of Judith Butler and others on vulnerability, and it questions categories of genre through an interdisciplinary engagement with different representational forms, including digital culture, graphic novels, video games, photography and TV series, in addition to novels and short stories. It offers new readings of high-profile contemporary authors of fiction including Margaret Atwood and Cormac McCarthy, as well as bringing lesser-known figures to the fore

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz