Bayesian semiparametric multivariate stochastic volatility with application

In this article, we establish a Cholesky-type multivariate stochastic volatility estimation framework, in which we let the innovation vector follow a Dirichlet process mixture (DPM), thus enabling us to model highly flexible return distributions. The Cholesky decomposition allows parallel univariate process modeling and creates potential for estimating high-dimensional specifications. We use Markov chain Monte Carlo methods for posterior simulation and predictive density computation. We apply our framework to a five-dimensional stock-return data set and analyze international stockmarket co-movements among the largest stock markets. The empirical results show that our DPM modeling of the innovation vector yields substantial gains in out-of-sample density forecast accuracy when compared with the prevalent benchmark models

Beyond convergence rates: Exact recovery with Tikhonov regularization with sparsity constraints

The Tikhonov regularization of linear ill-posed problems with an $\ell^1$ penalty is considered. We recall results for linear convergence rates and results on exact recovery of the support. Moreover, we derive conditions for exact support recovery which are especially applicable in the case of ill-posed problems, where other conditions, e.g. based on the so-called coherence or the restricted isometry property are usually not applicable. The obtained results also show that the regularized solutions do not only converge in the $\ell^1$-norm but also in the vector space $\ell^0$ (when considered as the strict inductive limit of the spaces $\R^n$ as $n$ tends to infinity). Additionally, the relations between different conditions for exact support recovery and linear convergence rates are investigated. With an imaging example from digital holography the applicability of the obtained results is illustrated, i.e. that one may check a priori if the experimental setup guarantees exact recovery with Tikhonov regularization with sparsity constraints

Bayesian semiparametric multivariate stochastic volatility with application

In this article, we establish a Cholesky-type multivariate stochastic volatility estimation framework, in which we let the innovation vector follow a Dirichlet process mixture (DPM), thus enabling us to model highly flexible return distributions. The Cholesky decomposition allows parallel univariate process modeling and creates potential for estimating high-dimensional specifications. We use Markov chain Monte Carlo methods for posterior simulation and predictive density computation. We apply our framework to a five-dimensional stock-return data set and analyze international stock-market co-movements among the largest stock markets. The empirical results show that our DPM modeling of the innovation vector yields substantial gains in out-of-sample density forecast accuracy when compared with the prevalent benchmark models

Engaging with Diversity and Complexity using Collaborative Approaches to Decision Making

A key challenge in contemporary dietetic practice is making collaborative decisions about dietary behaviours with a diverse range of patients. Contemporary decision making frameworks for clinical dietetic practice give value to working in a collaborative manner with patients, however, there remains uncertainty with regards to how and when dietitians might apply this approach in their practice.In this doctoral research project, Author 1 used a philosophical hermeneutic approach to deepen understanding of a collaborative approach to decision making in dietetic practice. She also explored the core capabilities required to enact such an approach in early career dietetic practice. The experiences and perceptions of patients and dietitians were explored using in depth interviews and individualized reflective practice activities.The findings suggest that collaborative decision making in dietetic practice is situational and requires the development of a caring and trusting professional relationship to be effective. Other core capabilities needed to enact this approach relate to developing self awareness, establishing an open and transparent dialogue, identifying and exploring common ground and finding the time to think and talk.The final product of the research, the Interpretive Engagement Model of Collaborative Decision Making (Author 1, 2013), can be used as a framework to help practitioners to reflect on their decision making practice.Early exposure in tertiary education to critical dialogues and questioning current practices will cultivate early career dietitians’ capabilities to develop their collaborative decision making practice in future.</jats:p

Notch signaling expands a pre-malignant pool of T-cell acute lymphoblastic leukemia clones without affecting leukemia-propagating cell frequency

NOTCH1 pathway activation contributes to the pathogenesis of over 60% of T-cell acute lymphoblastic leukemia (T-ALL). While Notch is thought to exert the majority of its effects through transcriptional activation of Myc, it also likely has independent roles in T-ALL malignancy. Here, we utilized a zebrafish transgenic model of T-ALL, where Notch does not induce Myc transcription, to identify a novel Notch gene expression signature that is also found in human T-ALL and is regulated independently of Myc. Cross-species microarray comparisons between zebrafish and mammalian disease identified a common T-ALL gene signature, suggesting that conserved genetic pathways underlie T-ALL development. Functionally, Notch expression induced a significant expansion of pre-leukemic clones; however, a majority of these clones were not fully transformed and could not induce leukemia when transplanted into recipient animals. Limiting-dilution cell transplantation revealed that Notch signaling does not increase the overall frequency of leukemia-propagating cells (LPCs), either alone or in collaboration with Myc. Taken together, these data indicate that a primary role of Notch signaling in T-ALL is to expand a population of pre-malignant thymocytes, of which a subset acquire the necessary mutations to become fully transformed LPCs

Zebrafish: A See-Through Host and a Fluorescent Toolbox to Probe Host–Pathogen Interaction

In many ways, the zebrafish represents a hybrid between mouse and invertebrate infection models. Powerful forwardgenetic tools that have made invertebrates justifiably famous are not only relatively accessible in the zebrafish, but have been exploited to yield new insights into human infectious diseases, including leprosy and tuberculosis [1]. Transgenic technologies have enabled detailed, non-invasive in vivo visualization of macrophages and neutrophils in pitched battle with bacteria and fungi [2,3]. Reverse genetics with morpholinos, vivo-morpholinos, and zinc-finger nucleases (but unfortunately not homologous recombination, which for the moment remains out of reach in this organism) enable examination of the roles of specific genes during infection. Flexible genetic systems such as Gal4-UAS and Cre-Lox permit tissue-specific transformation and ablation ([3]; Figure 1)

Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry

BACKGROUND: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. FINDINGS: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. CONCLUSIONS: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets