Ovarian cancer remains a deadly disease and its recurrence disease is due in part to the presence of disseminating ovarian cancer aggregates not removed by debulking surgery. During dissemination in ...a dynamic ascitic environment, the spheroid cells' metabolism is characterized by low respiration and fragmented mitochondria, a metabolic phenotype that may not support secondary outgrowth after adhesion. Here, we investigated how adhesion affects cellular respiration and substrate utilization of spheroids mimicking early stages of secondary metastasis. Using different glucose and oxygen levels, we investigated cellular metabolism at early time points of adherence (24 h and less) comparing slow and fast-developing disease models. We found that adhesion over time showed changes in cellular energy metabolism and substrate utilization, with a switch in the utilization of mostly glutamine to glucose but no changes in fatty acid oxidation. Interestingly, low glucose levels had less of an impact on cellular metabolism than hypoxia. A resilience to culture conditions and the capacity to utilize a broader spectrum of substrates more efficiently distinguished the highly aggressive cells from the cells representing slow-developing disease, suggesting a flexible metabolism contributes to the stem-like properties. These results indicate that adhesion to secondary sites initiates a metabolic switch in the oxidation of substrates that could support outgrowth and successful metastasis.
Data clustering is a fundamental and very popular method of data analysis. Its subjective nature, however, means that different clustering algorithms or different parameter settings can produce ...widely varying and sometimes conflicting results. This has led to the use of clustering comparison measures to quantify the degree of similarity between alternative clusterings. Existing measures, though, can be limited in their ability to assess similarity and sometimes generate unintuitive results. They also cannot be applied to compare clusterings which contain different data points, an activity which is important for scenarios such as data stream analysis. In this paper, we introduce a new clustering similarity measure, known as
ADCO
, which aims to address some limitations of existing measures, by allowing greater flexibility of comparison via the use of density profiles to characterize a clustering. In particular, it adopts a ‘data mining style’ philosophy to clustering comparison, whereby two clusterings are considered to be more similar, if they are likely to give rise to similar types of prediction models. Furthermore, we show that this new measure can be applied as a highly effective objective function within a new algorithm, known as MAXIMUS, for generating alternate clusterings.
Generation and analysis of multiple clusterings is a growing and important research field. A fundamental challenge underpinning this area is how to develop principled methods for assessing and ...explaining the similarity between two clusterings. A range of clustering similarity indices exist and an important subclass consists of measures for assessing
spatial clustering similarity
. These provide the advantage of being able to take into account properties of the feature space when assessing the similarity of clusterings. However, the output of spatially aware clustering comparison is limited to a single similarity value, which lacks detail for a user. Instead, a user may also wish to understand the degree to which the assessment of clustering similarity is dependent on the choice of feature space.
To this end, we propose a technique for deeper exploration of the spatial similarity between two clusterings. Using as a reference a measure that assesses the spatial similarity of two clusterings in the full feature space, our method discovers deviating subspaces in which the spatial similarity of the two clusterings becomes substantially larger or smaller. Such information provides a starting point for the user to understand the circumstances in which the distance functions associated with each of the two clusterings are behaving similarly or dissimilarly. The core of our method employs a range of pruning techniques to help efficiently enumerate and explore the search space of deviating subspaces. We experimentally assess the effectiveness of our approach using an evaluation with synthetic and real world datasets and demonstrate the potential of our technique for highlighting novel information about spatial similarity between clusterings.
Cluster analysis has long been a fundamental task in data mining and machine learning. However, traditional clustering methods concentrate on producing a single solution, even though multiple ...alternative clusterings may exist. It is thus difficult for the user to validate whether the given solution is in fact appropriate, particularly for large and complex datasets. In this paper we explore the critical requirements for systematically finding a new clustering, given that an already known clustering is available and we also propose a novel algorithm, COALA, to discover this new clustering. Our approach is driven by two important factors; dissimilarity and quality. These are especially important for finding a new clustering which is highly informative about the underlying structure of data, but is at the same time distinctively different from the provided clustering. We undertake an experimental analysis and show that our method is able to outperform existing techniques, for both synthetic and real datasets.
Patient activation measure (PAM) is widely adopted by health care providers to access individuals' knowledge, skill, and confidence for managing one's health and healthcare. Patient activation ...measure (PAM), licensed by Insignia Health, is widely adopted by health care providers to access individuals' knowledge, skill, and confidence for managing one's health and healthcare. Multiple studies corroborate the effectiveness of activation measure in predicting most health behaviors, including preventive behaviors, healthy behaviors, self-management behaviors, and health information seeking. However, PAM is heavily dependent on subjective patient-reported data, which are often incomplete. The purpose of this study is to develop an objective statistical model to create a score derived from patient behavioral measurements. Ranging from 1 to 3, the score, which we named patient engagement score (PES), was derived entirely from three objective variables - number of immunization, number of missed scheduled visits, and rate of patient adherence to prescription refill - using finite mixture model and EM algorithm. Finally, we performed simple and multiple linear regressions for the association between PES and each of the health-related outcomes.
The unsupervised nature of cluster analysis means that objects can be clustered in many ways, allowing different clustering algorithms to generate vastly different results. To address this, ...clustering comparison methods have traditionally been used to quantify the degree of similarity between alternative clusterings. However, existing techniques utilize only the point memberships to calculate the similarity, which can lead to unintuitive results. They also cannot be applied to analyze clusterings which only partially share points, which can be the case in stream clustering. In this paper we introduce a new measure named ADCO, which takes into account density profiles for each attribute and aims to address these problems. We provide experiments to demonstrate this new measure can often provide a more reasonable similarity comparison between different clusterings than existing methods.
XML is now a dominant standard for storing and exchanging information. One very important activity is the transformation of XML documents into other formats, via the transformation language XSLT. ...XSLT provides a powerful way to perform document conversion and exchange, avoiding reliance on application specific solutions. However, XSLT is a complex language and the current level of support for debugging tools is poor. Many tools that do exist are mainly an extension of conventional techniques for imperative programs and not well-suited to the task. In this paper, we present CodeX, a debugger for XSLT and propose three debugging techniques which are particularly suited to the language. We aim to offer XSLT users a tool which is beneficial in finding errors, as well as facilitating a better understanding of the XML transformation process.
A number of ontologies have been recently developed in order to represent common knowledge in a structured manner. This allows users and agents involved in a particular domain to make inquiries and ...discover the underlying conceptual differences present in the data. However, currently the majority of ontology construction tools are heavily dependent on the human domain experts for selecting concepts and defining their relationships. In this paper, we would like to present a new tool called ACORN, which implements novel techniques for automatically extracting concepts and building concept-to-concept relationships. We first utilize the WordNet lexical database and term co-occurrence frequency for discovering domain specific concepts and introduce ‘cluster mapping’ and ‘generality ordering’ techniques for connecting these concepts. We apply our techniques to a widely available dataset and show that ACORN is able to produce high quality ontologies.
Anifrolumab, a human monoclonal antibody to type I interferon receptor subunit 1 investigated for the treatment of systemic lupus erythematosus (SLE), did not have a significant effect on the primary ...end point in a previous phase 3 trial. The current phase 3 trial used a secondary end point from that trial as the primary end point.
We randomly assigned patients in a 1:1 ratio to receive intravenous anifrolumab (300 mg) or placebo every 4 weeks for 48 weeks. The primary end point of this trial was a response at week 52 defined with the use of the British Isles Lupus Assessment Group (BILAG)-based Composite Lupus Assessment (BICLA). A BICLA response requires reduction in any moderate-to-severe baseline disease activity and no worsening in any of nine organ systems in the BILAG index, no worsening on the Systemic Lupus Erythematosus Disease Activity Index, no increase of 0.3 points or more in the score on the Physician Global Assessment of disease activity (on a scale from 0 no disease activity to 3 severe disease), no discontinuation of the trial intervention, and no use of medications restricted by the protocol. Secondary end points included a BICLA response in patients with a high interferon gene signature at baseline; reductions in the glucocorticoid dose, in the severity of skin disease, and in counts of swollen and tender joints; and the annualized flare rate.
A total of 362 patients received the randomized intervention: 180 received anifrolumab and 182 received placebo. The percentage of patients who had a BICLA response was 47.8% in the anifrolumab group and 31.5% in the placebo group (difference, 16.3 percentage points; 95% confidence interval, 6.3 to 26.3; P = 0.001). Among patients with a high interferon gene signature, the percentage with a response was 48.0% in the anifrolumab group and 30.7% in the placebo group; among patients with a low interferon gene signature, the percentage was 46.7% and 35.5%, respectively. Secondary end points with respect to the glucocorticoid dose and the severity of skin disease, but not counts of swollen and tender joints and the annualized flare rate, also showed a significant benefit with anifrolumab. Herpes zoster and bronchitis occurred in 7.2% and 12.2% of the patients, respectively, who received anifrolumab. There was one death from pneumonia in the anifrolumab group.
Monthly administration of anifrolumab resulted in a higher percentage of patients with a response (as defined by a composite end point) at week 52 than did placebo, in contrast to the findings of a similar phase 3 trial involving patients with SLE that had a different primary end point. The frequency of herpes zoster was higher with anifrolumab than with placebo. (Funded by AstraZeneca; ClinicalTrials.gov number, NCT02446899.).
Bile acids act as signaling molecules that regulate immune homeostasis, including the differentiation of CD4+ T cells into distinct T cell subsets. The bile acid metabolite isoallolithocholic acid ...(isoalloLCA) enhances the differentiation of anti-inflammatory regulatory T cells (Treg cells) by facilitating the formation of a permissive chromatin structure in the promoter region of the transcription factor forkhead box P3 (Foxp3). Here, we identify gut bacteria that synthesize isoalloLCA from 3-oxolithocholic acid and uncover a gene cluster responsible for the conversion in members of the abundant human gut bacterial phylum Bacteroidetes. We also show that the nuclear hormone receptor NR4A1 is required for the effect of isoalloLCA on Treg cells. Moreover, the levels of isoalloLCA and its biosynthetic genes are significantly reduced in patients with inflammatory bowel diseases, suggesting that isoalloLCA and its bacterial producers may play a critical role in maintaining immune homeostasis in humans.
Display omitted
•Microbially derived secondary bile acid isoalloLCA enhances Treg differentiation•A biosynthetic gene cluster in gut Bacteroidetes converts 3-oxoLCA to isoalloLCA•NR4A1 is required for the isoalloLCA-mediated differentiation of Treg cells•Levels of isoalloLCA and corresponding genes are negatively correlated with IBD
Li and Hang et al. identify a biosynthetic pathway by which abundant human gut bacterial species from the phylum Bacteroidetes convert the secondary bile acid 3-oxoLCA to isoalloLCA. The nuclear hormone receptor NR4A1 is required for isoalloLCA to enhance Treg cell differentiation, suggesting a commensal-driven mechanism of immune tolerance.
PDF
