Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

Use of KikGR a photoconvertible green-to-red fluorescent protein for cell labeling and lineage analysis in ES cells and mouse embryos

<p>Abstract</p> <p>Background</p> <p>The use of genetically-encoded fluorescent proteins has revolutionized the fields of cell and developmental biology and in doing so redefined our understanding of the dynamic morphogenetic processes that shape the embryo. With the advent of more accessible and sophisticated imaging technologies as well as an abundance of fluorescent proteins with different spectral characteristics, the dynamic processes taking place <it>in situ </it>in living cells and tissues can now be probed. Photomodulatable fluorescent proteins are one of the emerging classes of genetically-encoded fluorescent proteins.</p> <p>Results</p> <p>We have compared PA-GFP, PS-CFP2, Kaede and KikGR four readily available and commonly used photomodulatable fluorescent proteins for use in ES cells and mice. Our results suggest that the green-to-red photoconvertible fluorescent protein, Kikume Green-Red (KikGR), is most suitable for cell labeling and lineage studies in ES cells and mice because it is developmentally neutral, bright and undergoes rapid and complete photoconversion. We have generated transgenic ES cell lines and strains of mice exhibiting robust widespread expression of KikGR. By efficient photoconversion of KikGR we labeled subpopulations of ES cells in culture, and groups of cells within <it>ex utero </it>cultured mouse embryos. Red fluorescent photoconverted cells and their progeny could be followed for extended periods of time.</p> <p>Conclusion</p> <p>Transgenic ES cells and mice exhibiting widespread readily detectable expression of KikGR are indistinguishable from their wild type counterparts and are amenable to efficient photoconversion. They represent novel tools for non-invasive selective labeling specific cell populations and live imaging cell dynamics and cell fate. Genetically-encoded photomodulatable proteins such as KikGR represent emergent attractive alternatives to commonly used vital dyes, tissue grafts and genetic methods for investigating dynamic behaviors of individual cells, collective cell dynamics and fate mapping applications.</p

Generalized Theorems for Nonlinear State Space Reconstruction

Takens' theorem (1981) shows how lagged variables of a single time series can be used as proxy variables to reconstruct an attractor for an underlying dynamic process. State space reconstruction (SSR) from single time series has been a powerful approach for the analysis of the complex, non-linear systems that appear ubiquitous in the natural and human world. The main shortcoming of these methods is the phenomenological nature of attractor reconstructions. Moreover, applied studies show that these single time series reconstructions can often be improved ad hoc by including multiple dynamically coupled time series in the reconstructions, to provide a more mechanistic model. Here we provide three analytical proofs that add to the growing literature to generalize Takens' work and that demonstrate how multiple time series can be used in attractor reconstructions. These expanded results (Takens' theorem is a special case) apply to a wide variety of natural systems having parallel time series observations for variables believed to be related to the same dynamic manifold. The potential information leverage provided by multiple embeddings created from different combinations of variables (and their lags) can pave the way for new applied techniques to exploit the time-limited, but parallel observations of natural systems, such as coupled ecological systems, geophysical systems, and financial systems. This paper aims to justify and help open this potential growth area for SSR applications in the natural sciences

Anaphylaxis

Anaphylaxis is an acute, potentially fatal systemic reaction with varied mechanisms and clinical presentations. Although prompt recognition and treatment of anaphylaxis are imperative, both patients and healthcare professionals often fail to recognize and diagnose early signs and symptoms of the condition. Clinical manifestations vary widely, however, the most common signs are cutaneous symptoms, including angioedema, urticaria, erythema and pruritus. Immediate intramuscular administration of epinephrine into the lateral thigh is first-line therapy, even if the diagnosis is uncertain. The mainstays of long-term management include specialist assessment, avoidance measures, and the provision of an epinephrine auto-injector and an individualized anaphylaxis action plan. This article provides an overview of the causes, clinical features, diagnosis and acute and long-term management of this serious allergic reaction

The influence of semantic and phonological factors on syntactic decisions: An event-related brain potential study

During language production and comprehension, information about a word's syntactic properties is sometimes needed. While the decision about the grammatical gender of a word requires access to syntactic knowledge, it has also been hypothesized that semantic (i.e., biological gender) or phonological information (i.e., sound regularities) may influence this decision. Event-related potentials (ERPs) were measured while native speakers of German processed written words that were or were not semantically and/or phonologically marked for gender. Behavioral and ERP results showed that participants were faster in making a gender decision when words were semantically and/or phonologically gender marked than when this was not the case, although the phonological effects were less clear. In conclusion, our data provide evidence that even though participants performed a grammatical gender decision, this task can be influenced by semantic and phonological factors

Neutrophils in cancer: neutral no more

Neutrophils are indispensable antagonists of microbial infection and facilitators of wound healing. In the cancer setting, a newfound appreciation for neutrophils has come into view. The traditionally held belief that neutrophils are inert bystanders is being challenged by the recent literature. Emerging evidence indicates that tumours manipulate neutrophils, sometimes early in their differentiation process, to create diverse phenotypic and functional polarization states able to alter tumour behaviour. In this Review, we discuss the involvement of neutrophils in cancer initiation and progression, and their potential as clinical biomarkers and therapeutic targets

Gravitational Waves from Gravitational Collapse

Gravitational wave emission from the gravitational collapse of massive stars has been studied for more than three decades. Current state of the art numerical investigations of collapse include those that use progenitors with realistic angular momentum profiles, properly treat microphysics issues, account for general relativity, and examine non--axisymmetric effects in three dimensions. Such simulations predict that gravitational waves from various phenomena associated with gravitational collapse could be detectable with advanced ground--based and future space--based interferometric observatories.Comment: 68 pages including 13 figures; revised version accepted for publication in Living Reviews in Relativity (http://www.livingreviews.org

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

<p>Abstract</p> <p>Background</p> <p>Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs.</p> <p>Results</p> <p>In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from <it>S. cervisiae </it>hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of <it>S. cerevisiae</it>, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled.</p> <p>Conclusion</p> <p>Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.</p

Trends in Depression and Antidepressant Prescribing in Children and Adolescents: A Cohort Study in The Health Improvement Network (THIN)

In 2003, the Committee on Safety of Medicines (CSM) advised against treatment with selective serotonin reuptake inhibitors (SSRIs) other than fluoxetine in children, due to a possible increased risk of suicidal behaviour. This study examined the effects of this safety warning on general practitioners' depression diagnosing and prescription behaviour in children

The Role of Intrinsically Unstructured Proteins in Neurodegenerative Diseases

The number and importance of intrinsically disordered proteins (IUP), known to be involved in various human disorders, are growing rapidly. To test for the generalized implications of intrinsic disorders in proteins involved in Neurodegenerative diseases, disorder prediction tools have been applied to three datasets comprising of proteins involved in Huntington Disease (HD), Parkinson's disease (PD), Alzheimer's disease (AD). Results show, in general, proteins in disease datasets possess significantly enhanced intrinsic unstructuredness. Most of these disordered proteins in the disease datasets are found to be involved in neuronal activities, signal transduction, apoptosis, intracellular traffic, cell differentiation etc. Also these proteins are found to have more number of interactors and hence as the proportion of disorderedness (i.e., the length of the unfolded stretch) increased, the size of the interaction network simultaneously increased. All these observations reflect that, “Moonlighting” i.e. the contextual acquisition of different structural conformations (transient), eventually may allow these disordered proteins to act as network “hubs” and thus they may have crucial influences in the pathogenecity of neurodegenerative diseases