Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

Carfilzomib-lenalidomide-dexamethasone vs lenalidomide-dexamethasone in relapsed multiple myeloma by previous treatment

Carfilzomib, a proteasome inhibitor, is approved as monotherapy and in combination with dexamethasone or lenalidomide-dexamethasone (Rd) for relapsed or refractory multiple myeloma. The approval of carfilzomib-lenalidomide-dexamethasone (KRd) was based on results from the randomized, phase 3 study ASPIRE (NCT01080391), which showed KRd significantly improved progression-free survival (PFS) vs Rd (median 26.3 vs 17.6 months; hazard ratio (HR)=0.690; P=0.0001). This subgroup analysis of ASPIRE evaluated KRd vs Rd by number of previous lines of therapy and previous exposure to bortezomib, thalidomide or lenalidomide. Treatment with KRd led to a 12-month improvement in median PFS vs Rd after first relapse (HR 0.713) and a 9-month improvement after 2 previous lines of therapy (HR 0.720). Treatment with KRd led to an approximate 8-month improvement vs Rd in median PFS in bortezomib-exposed patients (HR 0.699), a 15-month improvement in thalidomide-exposed patients (HR 0.587) and a 5-month improvement in lenalidomide-exposed patients (HR 0.796). Objective response and complete response or better rates were higher with KRd vs Rd, irrespective of previous treatment. KRd had a favorable benefit-risk profile and should be considered an appropriate treatment option for patients with 1 or 2 previous lines of therapy and those previously exposed to bortezomib, thalidomide or lenalidomide

Augmented versus Virtual Reality Laparoscopic Simulation: What Is the Difference?: A Comparison of the ProMIS Augmented Reality Laparoscopic Simulator versus LapSim Virtual Reality Laparoscopic Simulator

BACKGROUND: Virtual reality (VR) is an emerging new modality for laparoscopic skills training; however, most simulators lack realistic haptic feedback. Augmented reality (AR) is a new laparoscopic simulation system offering a combination of physical objects and VR simulation. Laparoscopic instruments are used within an hybrid mannequin on tissue or objects while using video tracking. This study was designed to assess the difference in realism, haptic feedback, and didactic value between AR and VR laparoscopic simulation. METHODS: The ProMIS AR and LapSim VR simulators were used in this study. The participants performed a basic skills task and a suturing task on both simulators, after which they filled out a questionnaire about their demographics and their opinion of both simulators scored on a 5-point Likert scale. The participants were allotted to 3 groups depending on their experience: experts, intermediates and novices. Significant differences were calculated with the paired t-test. RESULTS: There was general consensus in all groups that the ProMIS AR laparoscopic simulator is more realistic than the LapSim VR laparoscopic simulator in both the basic skills task (mean 4.22 resp. 2.18, P <0.000) as well as the suturing task (mean 4.15 resp. 1.85, P <0.000). The ProMIS is regarded as having better haptic feedback (mean 3.92 resp. 1.92, P <0.000) and as being more useful for training surgical residents (mean 4.51 resp. 2.94, P <0.000). CONCLUSIONS: In comparison with the VR simulator, the AR laparoscopic simulator was regarded by all participants as a better simulator for laparoscopic skills training on all tested feature

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

New insights into the classification and nomenclature of cortical GABAergic interneurons.

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus

Global Development and Climate Change: A Game Theory Approach

The increasing concern with climate change is one of the main issues of our time, and thus we aim to theoretically and mathematically analyse its causes. However our approach follows a different stream of thought, presenting the reasoning and decision-making processes between technical and moral solutions. We have resorted to game theory models in order to demonstrate cooperative and non-cooperative scenarios, ranging from the traditional to the evolutionary within game theory. In doing so we are able to glimpse the development of modern society and a paradigm shift regarding human control over nature and to what extent it is harmful to the sustainability of our environment and the survival of future generations. Merging different fields of knowledge, we present a theoretical-philosophical approach, combined with empirical-mathematical solutions taking into account the agent-based behaviour guided blindly by instrumental rationality

Quantitative Proteomic Analysis of Human Embryonic Stem Cell Differentiation by 8-Plex iTRAQ Labelling

Analysis of gene expression to define molecular mechanisms and pathways involved in human embryonic stem cells (hESCs) proliferation and differentiations has allowed for further deciphering of the self-renewal and pluripotency characteristics of hESC. Proteins associated with hESCs were discovered through isobaric tags for relative and absolute quantification (iTRAQ). Undifferentiated hESCs and hESCs in different stages of spontaneous differentiation by embryoid body (EB) formation were analyzed. Using the iTRAQ approach, we identified 156 differentially expressed proteins involved in cell proliferation, apoptosis, transcription, translation, mRNA processing, and protein synthesis. Proteins involved in nucleic acid binding, protein synthesis, and integrin signaling were downregulated during differentiation, whereas cytoskeleton proteins were upregulated. The present findings added insight to our understanding of the mechanisms involved in hESC proliferation and differentiation

Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

BACKGROUND: A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied. METHODOLOGY/PRINCIPAL FINDINGS: We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available. CONCLUSIONS: Although currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide insights in erroneous and missed annotations

Eco-friendly one-pot synthesis of Prussian blue-embedded magnetic hydrogel beads for the removal of cesium from water

A simple one-step approach to fabricating Prussian blue-embedded magnetic hydrogel beads (PBMHBs) was fabricated for the effective magnetic removal of radioactive cesium (Cs-137) from water. Through the simple dropwise addition of a mixed aqueous solution of iron salts, commercial PB and polyvinyl alcohol (PVA) to an ammonium hydroxide (NH4OH) solution, the formation of hydrogel beads and the encapsulation of PB in beads were achieved in one pot through the gelation of PVA with in situ-formed iron oxide nanoparticles as the cross-linker. The obtained PB-MHBs, with 43.77 weight %of PB, were stable without releasing PB for up to 2 weeks and could be effectively separated from aqueous solutions by an external magnetic field, which is convenient for the large-scale treatment of Cs-contaminated water. Detailed Cs adsorption studies revealed that the adsorption isotherms and kinetics could be effectively described by the Langmuir isotherm model and the pseudo-second-order model, respectively. Most importantly, the PB-MHBs exhibited excellent selectivity for Cs-137 in (137)Cscontaminated simulated groundwater (55 Bq/g) with a high removal efficiency (&gt;99.5%), and the effective removal of Cs-137 from real seawater by these PB-MHBs demonstrated the excellent potential of this material for practical application in the decontamination of Cs-137-contaminated seawate