Learning mixed graphical models with separate sparsity parameters and stability-based model selection

Background: Mixed graphical models (MGMs) are graphical models learned over a combination of continuous and discrete variables. Mixed variable types are common in biomedical datasets. MGMs consist of a parameterized joint probability density, which implies a network structure over these heterogeneous variables. The network structure reveals direct associations between the variables and the joint probability density allows one to ask arbitrary probabilistic questions on the data. This information can be used for feature selection, classification and other important tasks. Results: We studied the properties of MGM learning and applications of MGMs to high-dimensional data (biological and simulated). Our results show that MGMs reliably uncover the underlying graph structure, and when used for classification, their performance is comparable to popular discriminative methods (lasso regression and support vector machines). We also show that imposing separate sparsity penalties for edges connecting different types of variables significantly improves edge recovery performance. To choose these sparsity parameters, we propose a new efficient model selection method, named Stable Edge-specific Penalty Selection (StEPS). StEPS is an expansion of an earlier method, StARS, to mixed variable types. In terms of edge recovery, StEPS selected MGMs outperform those models selected using standard techniques, including AIC, BIC and cross-validation. In addition, we use a heuristic search that is linear in size of the sparsity value search space as opposed to the cubic grid search required by other model selection methods. We applied our method to clinical and mRNA expression data from the Lung Genomics Research Consortium (LGRC) and the learned MGM correctly recovered connections between the diagnosis of obstructive or interstitial lung disease, two diagnostic breathing tests, and cigarette smoking history. Our model also suggested biologically relevant mRNA markers that are linked to these three clinical variables. Conclusions: MGMs are able to accurately recover dependencies between sets of continuous and discrete variables in both simulated and biomedical datasets. Separation of sparsity penalties by edge type is essential for accurate network edge recovery. Furthermore, our stability based method for model selection determines sparsity parameters faster and more accurately (in terms of edge recovery) than other model selection methods. With the ongoing availability of comprehensive clinical and biomedical datasets, MGMs are expected to become a valuable tool for investigating disease mechanisms and answering an array of critical healthcare questions

Addressing patient treatment preferences at trial recruitment.

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Increasing recruitment to randomised trials: a review of randomised controlled trials

BACKGROUND: Poor recruitment to randomised controlled trials (RCTs) is a widespread and important problem. With poor recruitment being such an important issue with respect to the conduct of randomised trials, a systematic review of controlled trials on recruitment methods was undertaken in order to identify strategies that are effective. METHODS: We searched the register of trials in Cochrane library from 1996 to end of 2004. We also searched Web of Science for 2004. Additional trials were identified from personal knowledge. Included studies had to use random allocation and participants had to be allocated to different methods of recruitment to a 'real' randomised trial. Trials that randomised participants to 'mock' trials and trials of recruitment to non-randomised studies (e.g., case control studies) were excluded. Information on the study design, intervention and control, and number of patients recruited was extracted by the 2 authors. RESULTS: We identified 14 papers describing 20 different interventions. Effective interventions included: telephone reminders; questionnaire inclusion; monetary incentives; using an 'open' rather than placebo design; and making trial materials culturally sensitive. CONCLUSION: Few trials have been undertaken to test interventions to improve trial recruitment. There is an urgent need for more RCTs of recruitment strategies

Ordering phenomena in quasi one-dimensional organic conductors

Low-dimensional organic conductors could establish themselves as model systems for the investigation of the physics in reduced dimensions. In the metallic state of a one-dimensional solid, Fermi-liquid theory breaks down and spin and charge degrees of freedom become separated. But the metallic phase is not stable in one dimension: as the temperature is reduced, the electronic charge and spin tend to arrange themselves in an ordered fashion due to strong correlations. The competition of the different interactions is responsible for which broken-symmetry ground state is eventually realized in a specific compound and which drives the system towards an insulating state. Here we review the various ordering phenomena and how they can be identified by optic and magnetic measurements. While the final results might look very similar in the case of a charge density wave and a charge-ordered metal, for instance, the physical cause is completely different. When density waves form, a gap opens in the density of states at the Fermi energy due to nesting of the one-dimension Fermi surface sheets. When a one-dimensional metal becomes a charge-ordered Mott insulator, on the other hand, the short-range Coulomb repulsion localizes the charge on the lattice sites and even causes certain charge patterns. We try to point out the similarities and conceptional differences of these phenomena and give an example for each of them. Particular emphasis will be put on collective phenomena which are inherently present as soon as ordering breaks the symmetry of the system.Comment: Review article Naturwissenschaften 200

The challenges faced in the design, conduct and analysis of surgical randomised controlled trials

Randomised evaluations of surgical interventions are rare; some interventions have been widely adopted without rigorous evaluation. Unlike other medical areas, the randomised controlled trial (RCT) design has not become the default study design for the evaluation of surgical interventions. Surgical trials are difficult to successfully undertake and pose particular practical and methodological challenges. However, RCTs have played a role in the assessment of surgical innovations and there is scope and need for greater use. This article will consider the design, conduct and analysis of an RCT of a surgical intervention. The issues will be reviewed under three headings: the timing of the evaluation, defining the research question and trial design issues. Recommendations on the conduct of future surgical RCTs are made. Collaboration between research and surgical communities is needed to address the distinct issues raised by the assessmentof surgical interventions and enable the conduct of appropriate and well-designed trials.The Health Services Research Unit is funded by the Scottish Government Health DirectoratesPeer reviewedPublisher PD

Spatially anisotropic S=1 square-lattice antiferromagnet with single-ion anisotropy realized in a Ni(II) pyrazine- n,n′ -dioxide coordination polymer

The Ni(NCS)2(pyzdo)2 coordination polymer is found to be an S=1 spatially anisotropic square lattice with easy-axis single-ion anisotropy. This conclusion is based upon considering in concert the experimental probes x-ray diffraction, magnetic susceptibility, magnetic-field-dependent heat capacity, muon-spin relaxation, neutron diffraction, neutron spectroscopy, and pulsed-field magnetization. Long-range antiferromagnetic (AFM) order develops at TN=18.5K. Although the samples are polycrystalline, there is an observable spin-flop transition and saturation of the magnetization at ≈80T. Linear spin-wave theory yields spatially anisotropic exchanges within an AFM square lattice, Jx=0.235meV, Jy=2.014meV, and an easy-axis single-ion anisotropy D=-1.622meV (after renormalization). The anisotropy of the exchanges is supported by density functional theory

Advances in prevention and therapy of neonatal dairy calf diarrhoea : a systematical review with emphasis on colostrum management and fluid therapy

Neonatal calf diarrhoea remains the most common cause of morbidity and mortality in preweaned dairy calves worldwide. This complex disease can be triggered by both infectious and non-infectious causes. The four most important enteropathogens leading to neonatal dairy calf diarrhoea are Escherichia coli, rota-and coronavirus, and Cryptosporidium parvum. Besides treating diarrhoeic neonatal dairy calves, the veterinarian is the most obvious person to advise the dairy farmer on prevention and treatment of this disease. This review deals with prevention and treatment of neonatal dairy calf diarrhoea focusing on the importance of a good colostrum management and a correct fluid therapy