Inhaled nitric oxide in premature infants: effect on tracheal aspirate and plasma nitric oxide metabolites.

ObjectiveInhaled nitric oxide (iNO) is a potential new therapy for prevention of bronchopulmonary dysplasia and brain injury in premature infants. This study examined dose-related effects of iNO on NO metabolites as evidence of NO delivery.Study designA subset of 102 premature infants in the NO CLD trial, receiving 24 days of iNO (20 p.p.m. decreasing to 2 p.p.m.) or placebo, were analyzed. Tracheal aspirate (TA) and plasma samples collected at enrollment and at intervals during study gas were analyzed for NO metabolites.ResultiNO treatment increased NO metabolites in TA at 20 and 10 p.p.m. (1.7- to 2.3-fold vs control) and in plasma at 20, 10, and 5 p.p.m. (1.6- to 2.3-fold). In post hoc analysis, treated infants with lower metabolite levels at entry had an improved clinical outcome.ConclusioniNO causes dose-related increases in NO metabolites in the circulation as well as lung fluid, as evidenced by TA analysis, showing NO delivery to these compartments

Exploiting the potential energy landscape to sample free energy

We review a number of recently developed strategies for enhanced sampling of complex systems based on knowledge of the potential energy landscape. We describe four approaches, replica exchange, Kirkwood sampling, superposition-enhanced nested sampling, and basin sampling, and show how each of them can exploit information for low-lying potential energy minima obtained using basin-hopping global optimization. Characterizing these minima is generally much faster than equilibrium thermodynamic sampling, because large steps in configuration space between local minima can be used without concern for maintaining detailed balance.The authors gratefully acknowledge financial support from the EPSRC and the ERC. S.M acknowledges financial support from the Gates Cambridge Scholarship.This is the accepted manuscript. The final published version is available at http://onlinelibrary.wiley.com/doi/10.1002/wcms.1217/abstract

Inhaled nitric oxide in premature infants: effect on tracheal aspirate and plasma nitric oxide metabolites

ObjectiveInhaled nitric oxide (iNO) is a potential new therapy for prevention of bronchopulmonary dysplasia and brain injury in premature infants. This study examined dose-related effects of iNO on NO metabolites as evidence of NO delivery.Study designA subset of 102 premature infants in the NO CLD trial, receiving 24 days of iNO (20 p.p.m. decreasing to 2 p.p.m.) or placebo, were analyzed. Tracheal aspirate (TA) and plasma samples collected at enrollment and at intervals during study gas were analyzed for NO metabolites.ResultiNO treatment increased NO metabolites in TA at 20 and 10 p.p.m. (1.7- to 2.3-fold vs control) and in plasma at 20, 10, and 5 p.p.m. (1.6- to 2.3-fold). In post hoc analysis, treated infants with lower metabolite levels at entry had an improved clinical outcome.ConclusioniNO causes dose-related increases in NO metabolites in the circulation as well as lung fluid, as evidenced by TA analysis, showing NO delivery to these compartments

Energy landscapes for machine learning

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.This research was funded by EPSRC grant EP/I001352/1, the Gates Cambridge Trust, and the ERC. DM was in the Department of Applied and Computational Mathematics and Statistics when this work was performed, and his current affiliation is Department of Systems, United Technologies Research Center, East Hartford, CT, USA

The comparative diagnostic accuracy of the Mini Mental State Examination (MMSE) and the General Practitioner assessment of Cognition (GPCOG) for identifying dementia in primary care: a systematic review protocol

Open Acces

Functional cognitive disorder: dementia's blind spot

An increasing proportion of cognitive difficulties are recognized to have a functional cause, the chief clinical indicator of which is internal inconsistency. When these symptoms are impairing or distressing, and not better explained by other disorders, this can be conceptualized as a cognitive variant of functional neurological disorder, termed functional cognitive disorder (FCD). FCD is likely very common in clinical practice but may be under-diagnosed. Clinicians in many settings make liberal use of the descriptive term mild cognitive impairment (MCI) for those with cognitive difficulties not impairing enough to qualify as dementia. However, MCI is an aetiology-neutral description, which therefore includes patients with a wide range of underlying causes. Consequently, a proportion of MCI cases are due to non-neurodegenerative processes, including FCD. Indeed, significant numbers of patients diagnosed with MCI do not 'convert' to dementia. The lack of diagnostic specificity for MCI 'non-progressors' is a weakness inherent in framing MCI primarily within a deterministic neurodegenerative pathway. It is recognized that depression, anxiety and behavioural changes can represent a prodrome to neurodegeneration; empirical data are required to explore whether the same might hold for subsets of individuals with FCD. Clinicians and researchers can improve study efficacy and patient outcomes by viewing MCI as a descriptive term with a wide differential diagnosis, including potentially reversible components such as FCD. We present a preliminary definition of functional neurological disorder-cognitive subtype, explain its position in relation to other cognitive diagnoses and emerging biomarkers, highlight clinical features that can lead to positive diagnosis (as opposed to a diagnosis of exclusion), and red flags that should prompt consideration of alternative diagnoses. In the research setting, positive identifiers of FCD will enhance our recognition of individuals who are not in a neurodegenerative prodrome, while greater use of this diagnosis in clinical practice will facilitate personalised interventions

3D time series analysis of cell shape using Laplacian approaches

Background: Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. Results: We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. Conclusions: The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

Synthetic vs. Real-World Continuous Landscapes: A Local Optima Networks View

Local optima networks (LONs) are a useful tool to analyse and visualise the global structure of fitness landscapes. The main goal of our study is to use LONs to contrast the global structure of synthetic benchmark functions against those of real-world continuous optimisation problems of similar dimensions. We selected two real-world problems, namely, an engineering design problem and a machine learning problem. Our results indicate striking differences in the global structure of synthetic vs real-world problems. The real-world problems studied were easier to solve than the synthetic ones, and our analysis reveals why; they have easier to traverse global structures with fewer nodes and edges, no sub-optimal funnels, higher neutrality and multiple global optima with shorter trajectories towards them

SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale

<p>Abstract</p> <p>Background</p> <p>An important problem in genomics is the automatic inference of groups of homologous proteins from pairwise sequence similarities. Several approaches have been proposed for this task which are "local" in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. It was shown recently that global methods such as spectral clustering have better performance on a wide variety of datasets. However, currently available implementations of spectral clustering methods mostly consist of a few loosely coupled Matlab scripts that assume a fair amount of familiarity with Matlab programming and hence they are inaccessible for large parts of the research community.</p> <p>Results</p> <p>SCPS (Spectral Clustering of Protein Sequences) is an efficient and user-friendly implementation of a spectral method for inferring protein families. The method uses only pairwise sequence similarities, and is therefore practical when only sequence information is available. SCPS was tested on difficult sets of proteins whose relationships were extracted from the SCOP database, and its results were extensively compared with those obtained using other popular protein clustering algorithms such as TribeMCL, hierarchical clustering and connected component analysis. We show that SCPS is able to identify many of the family/superfamily relationships correctly and that the quality of the obtained clusters as indicated by their F-scores is consistently better than all the other methods we compared it with. We also demonstrate the scalability of SCPS by clustering the entire SCOP database (14,183 sequences) and the complete genome of the yeast <it>Saccharomyces cerevisiae </it>(6,690 sequences).</p> <p>Conclusions</p> <p>Besides the spectral method, SCPS also implements connected component analysis and hierarchical clustering, it integrates TribeMCL, it provides different cluster quality tools, it can extract human-readable protein descriptions using GI numbers from NCBI, it interfaces with external tools such as BLAST and Cytoscape, and it can produce publication-quality graphical representations of the clusters obtained, thus constituting a comprehensive and effective tool for practical research in computational biology. Source code and precompiled executables for Windows, Linux and Mac OS X are freely available at <url>http://www.paccanarolab.org/software/scps</url>.</p