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 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

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

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

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

The misuses of sustainability: adult education, citizenship and the dead hand of neoliberalism

‘‘Sustainability’’ has a captivating but disingenuous simplicity: its meanings are complex, and have political and policy significance. Exploring the application of the term to adult education, this paper argues that a particular discourse of ‘‘sustainability’’ has become a common-sense, short-circuiting critical analysis and understanding of policy options. This ‘‘business discourse’’ of sustainability, strongly influenced by neoliberal ideas, encourages the presumption that educational programmes and movements which have died out were unsustainable, bound to fail, and even responsible – having failed to adapt – for their own demise. Potentially valuable experience is thus excluded from the educational policy canon. The author uses three cases from 20th-century adult education, namely (1) English liberal adult education; (2) ‘‘mass education’’, also known as community development, in the British colonies; and (3) UNESCO’s Fundamental Education, to challenge this presumption. He demonstrates for each case how a business discourse has implied their ‘‘unsustainability’’, but that the reality was more complex and involved external political intervention

Abundance of the Quorum-Sensing Factor Ax21 in Four Strains of Stenotrophomonas maltophilia Correlates with Mortality Rate in a New Zebrafish Model of Infection

Stenotrophomonas maltophilia is a Gram-negative pathogen with emerging nosocomial incidence. Little is known about its pathogenesis and the genomic diversity exhibited by clinical isolates complicates the study of pathogenicity and virulence factors. Here, we present a strategy to identify such factors in new clinical isolates of S. maltophilia, incorporating an adult-zebrafish model of S. maltophilia infection to evaluate relative virulence coupled to 2D difference gel electrophoresis to explore underlying differences in protein expression. In this study we report upon three recent clinical isolates and use the collection strain ATCC13637 as a reference. The adult-zebrafish model shows discrimination capacity, i.e. from very low to very high mortality rates, with clinical symptoms very similar to those observed in natural S. maltophilia infections in fish. Strain virulence correlates with resistance to human serum, in agreement with previous studies in mouse and rat and therefore supporting zebrafish as a replacement model. Despite its clinical origin, the collection strain ATCC13637 showed obvious signs of attenuation in zebrafish, with null mortality. Multilocus-sequence-typing analysis revealed that the most virulent strains, UV74 and M30, exhibit the strongest genetic similitude. Differential proteomic analysis led to the identification of 38 proteins with significantly different abundance in the three clinical strains relative to the reference strain. Orthologs of several of these proteins have been already reported to have a role in pathogenesis, virulence or resistance mechanisms thus supporting our strategy. Proof of concept is further provided by protein Ax21, whose abundance is shown here to be directly proportional to mortality in the zebrafish infection model. Indeed, recent studies have demonstrated that this protein is a quorum-sensing-related virulence factor

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

Reframing professional development through understanding authentic professional learning

Continuing to learn is universally accepted and expected by professionals and other stakeholders across all professions. However, despite changes in response to research findings about how professionals learn, many professional development practices still focus on delivering content rather than enhancing learning. In exploring reasons for the continuation of didactic practices in professional development, this article critiques the usual conceptualization of professional development through a review of recent literature across professions. An alternative conceptualization is proposed, based on philosophical assumptions congruent with evidence about professional learning from seminal educational research of the past two decades. An argument is presented for a shift in discourse and focus from delivering and evaluating professional development programs to understanding and supporting authentic professional learning