Proteome analysis of Arabidopsis thaliana by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionisation-time of flight mass spectrometry

In the present study we show results of a large-scale proteome analysis of the recently sequenced plant Arabidopsis thaliana. On the basis of a previously published sequential protein extraction protocol, we prepared protein extracts from eight different A. thaliana tissues (primary leaf, leaf, stem, silique, seedling, seed, root, and inflorescence) and analysed these by two-dimensional gel electrophoresis. A total of 6000 protein spots, from three of these tissues, namely primary leaf, silique and seedling, were excised and the contained proteins were analysed by matrix assisted laser desorption/ionisation time of flight mass spectrometry peptide mass fingerprinting. This resulted in the identification of the proteins contained in 2943 spots, which were found to be products of 663 different genes. In this report we present and discuss the methodological and biological results of our plant proteome analysis

Sparse classification with MRI based markers for neuromuscular disease categorization

International audienceIn this paper, we present a novel method for disease classification between two patient populations based on features extracted from Magnetic Resonance Imaging (MRI) data. Anatomically meaningful features are extracted from structural data (T1- and T2-weighted MR images) and Diffusion Tensor Imaging (DTI) data, and used to train a new machine learning algorithm, the k-support SVM (ksup-SVM). The k-support regularized SVM has an inherent feature selection property, and thus it eliminates the requirement for a separate feature selection step. Our dataset consists of patients that suffer from facioscapulohumeral muscular dystrophy (FSH) and Myotonic muscular dystrophy type 1 (DM1) and our proposed method achieves a high performance. More specifically, it achieves a mean Area Under the Curve (AUC) of 0.7141 and mean accuracy 77% ± 0.013. Moreover, we provide a sparsity visualization of the features in order to indentify their discriminative value. The results suggest the potential of the combined use of MR markers to diagnose myopathies, and the general utility of the ksup-SVM. Source code is also available at https://gitorious.org/ksup-svm

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

PCSK2 expression in neuroendocrine tumors points to a midgut, pulmonary, or pheochromocytoma-paraganglioma origin

Neuroendocrine tumors (NETs) are often diagnosed from the metastases of an unknown primary tumor. Specific immunohistochemical (IHC) markers indicating the location of a primary tumor are needed. The proprotein convertase subtilisin/kexin type 2 (PCSK2) is found in normal neural and neuroendocrine cells, and known to express in NETs. We investigated the tissue microarray (TMA) of 86 primary tumors from 13 different organs and 9 metastatic NETs, including primary tumor-metastasis pairs, for PCSK2 expression with polymer-based IHC. PCSK2 was strongly positive in all small intestine and appendiceal NETs, the so-called midgut NETs, in most pheochromocytomas and paragangliomas, and in some of the typical and atypical pulmonary carcinoid tumors. NETs showing strong positivity were re-evaluated in larger tumor cohorts confirming the primary observation. In the metastases, the expression of PCSK2 mirrored that of the corresponding primary tumors. We found negative or weak staining in NETs from the thymus, gastric mucosa, pancreas, rectum, thyroid, and parathyroid. PCSK2 expression did not correlate with Ki-67 in well-differentiated NETs. Our data suggest that PCSK2 positivity can indicate the location of the primary tumor. Thus, PCSK2 could function in the IHC panel determined from screening metastatic NET biopsies of unknown primary origins.Peer reviewe

An individual participant data analysis of prospective cohort studies on the association between subclinical thyroid dysfunction and depressive symptoms.

In subclinical hypothyroidism, the presence of depressive symptoms is often a reason for starting levothyroxine treatment. However, data are conflicting on the association between subclinical thyroid dysfunction and depressive symptoms. We aimed to examine the association between subclinical thyroid dysfunction and depressive symptoms in all prospective cohorts with relevant data available. We performed a systematic review of the literature from Medline, Embase, Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library from inception to 10th May 2019. We included prospective cohorts with data on thyroid status at baseline and depressive symptoms during follow-up. The primary outcome was depressive symptoms measured at first available follow-up, expressed on the Beck's Depression Inventory (BDI) scale (range 0-63, higher values indicate more depressive symptoms, minimal clinically important difference: 5 points). We performed a two-stage individual participant data (IPD) analysis comparing participants with subclinical hypo- or hyperthyroidism versus euthyroidism, adjusting for depressive symptoms at baseline, age, sex, education, and income (PROSPERO CRD42018091627). Six cohorts met the inclusion criteria, with IPD on 23,038 participants. Their mean age was 60 years, 65% were female, 21,025 were euthyroid, 1342 had subclinical hypothyroidism and 671 subclinical hyperthyroidism. At first available follow-up [mean 8.2 (± 4.3) years], BDI scores did not differ between participants with subclinical hypothyroidism (mean difference = 0.29, 95% confidence interval = - 0.17 to 0.76, I <sup>2</sup> = 15.6) or subclinical hyperthyroidism (- 0.10, 95% confidence interval = - 0.67 to 0.48, I <sup>2</sup> = 3.2) compared to euthyroidism. This systematic review and IPD analysis of six prospective cohort studies found no clinically relevant association between subclinical thyroid dysfunction at baseline and depressive symptoms during follow-up. The results were robust in all sensitivity and subgroup analyses. Our results are in contrast with the traditional notion that subclinical thyroid dysfunction, and subclinical hypothyroidism in particular, is associated with depressive symptoms. Consequently, our results do not support the practice of prescribing levothyroxine in patients with subclinical hypothyroidism to reduce the risk of developing depressive symptoms

Pancreatic alpha cell mass in European subjects with type 2 diabetes

AIMS/HYPOTHESIS: Type 2 diabetes is a bi-hormonal disease characterised by relative hypoinsulinaemia and hyperglucagonaemia with elevated blood glucose levels. Besides pancreatic beta cell defects, a low number of beta cells (low beta cell mass) may contribute to the insufficient secretion of insulin. In this study our aim was to determine whether the alpha cell mass is also altered. METHODS: Using a point counting method, we measured the ratio of alpha to beta cell areas in pancreas samples obtained at autopsy from 50 type 2 diabetic subjects, whose beta cell mass had previously been found to be 36% lower than that of 52 non-diabetic subjects. RESULTS: The topography of alpha and beta cells was similar in both groups: many alpha cells were localised in the centre of the islets and the ratio of alpha/beta cell areas increased with islet size. The average ratio was significantly higher in type 2 diabetic subjects (0.72) than in non-diabetic subjects (0.42), with, however, a large overlap between the two groups. In contrast, the alpha cell mass was virtually identical in type 2 diabetic subjects (366 mg) and non-diabetic subjects (342 mg), and was not influenced by sex, BMI or type of diabetes treatment. CONCLUSIONS: The higher proportion of alpha to beta cells in the islets of some type 2 diabetic subjects is due to a decrease in beta cell number rather than an increase in alpha cell number. This imbalance may contribute to alterations in the normal inhibitory influence exerted by beta cells on alpha cells, and lead to the relative hyperglucagonaemia observed in type 2 diabete

Representation of the penalty term of dynamic concave utilities

In this paper we will provide a representation of the penalty term of general dynamic concave utilities (hence of dynamic convex risk measures) by applying the theory of g-expectations.Comment: An updated version is published in Finance & Stochastics. The final publication is available at http://www.springerlink.co