Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time

Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters

Quantum Clustering (QC) provides an alternative approach to clustering algorithms, several of which are based on geometric relationships between data points. Instead, QC makes use of quantum mechanics concepts to find structures (clusters) in data sets by finding the minima of a quantum potential. The starting point of QC is a Parzen estimator with a fixed length scale, which significantly affects the final cluster allocation. This dependence on an adjustable parameter is common to other methods. We propose a framework to find suitable values of the length parameter σ by optimising twin measures of cluster separation and consistency for a given cluster number. This is an extension of the Separation and Concordance framework previously introduced for K-means clustering. Experimental results on two synthetic data sets and three challenging real-world data sets show that optimisation of cluster separation identifies QC solutions with consistently high Jaccard score measured against true-cluster labels while optimisation of cluster consistency provides insights into hierarchical cluster structure. © 2017 Elsevier B.V

Quantum Brain Networks: A Perspective

We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain– machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business

Spatial and temporal facies evolution of a Lower Jurassic carbonate platform, NW Tethyan margin (Mallorca, Spain)

The variety of depositional facies of a Lower Jurassic carbonate platform has been investigated on the island of Mallorca along a transect comprising six stratigraphic profiles. Twenty-nine facies and sub-facies have been recognized, grouped into seven facies associations, ranging in depositional environment from supratidal/terrestrial and peritidal to outer platform. Spatial and temporal (2D) facies distribution along the transect reflects the evolution of the carbonate platform with time showing different facies associations, from a broad peritidal platform (stage 1) to a muddy open platform (stage 2), and finally to a peritidal to outer carbonate platform (stage 3). Stage 1 (early Sinemurian to earliest late Sinemurian) corresponds to a nearly-flat peritidal-shallow subtidal epicontinental platform with facies belts that shifted far and fast over the whole study area. The evolution from stage 1 to stage 2 (late Sinemurian) represents a rapid flooding of the epicontinental shallow platform, with more open-marine conditions, and the onset of differential subsidence. During stage 3 (latest Sinemurian), peritidal and shallow-platform environments preferentially developed to the northeast (Llevant Mountains domain) with a rapid transition to middle-outer platform environments toward the northwest (Tramuntana Range domain). Stages 1 and 3 present facies associations typical of Bahamian-type carbonates, whereas stage 2 represents the demise of the Bahamian-type carbonate factory and proliferation of muddy substrates with suspension-feeders. The described platform evolution responded to the interplay between the initial extensional tectonic phases related to Early Jurassic Tethyan rifting, contemporaneous environmental perturbations, and progressive platform flooding related to the Late Triassic–Early Jurassic worldwide marine transgression and associated accommodation changes

GEICO (Spanish Group for Investigation on Ovarian Cancer) treatment guidelines in ovarian cancer 2012

In 2006, under the auspices of The Spanish Research Group for Ovarian Cancer (Spanish initials GEICO), the first “Treatment Guidelines in Ovarian Cancer” were developed and then published in Clinical and Translational Oncology by Poveda Velasco et al. (Clin Transl Oncol 9(5):308–316, 2007). Almost 6 years have elapsed and over this time, we have seen some important developments in the treatment of ovarian cancer. Significant changes were also introduced after the GCIG-sponsored 4th Consensus Conference on Ovarian Cancer by Stuart et al. (Int J Gynecol Cancer 21:750–755, 2011). So we decided to update the treatment guidelines in ovarian cancer and, with this objective, a group of investigators of the GEICO group met in February 2012. This study summarizes the presentations, discussions and evidence that were reviewed during the meeting and during further discussions of the manuscript

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field