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

Pancreatic acinar cell carcinoma : an analysis of cell lineage markers, P53 expression, and Ki-ras mutation

In a series of 22 pancreatic acinar cell carcinomas, including two acinar cystadenocarcinomas, cellular differentiation was analyzed by immunocytochemistry and electron microscopy. In addition, overexpression of p53 protein and Ki-ras codon 12 mutation was studied. Four of the 20 noncystic acinar cell carcinomas showed a pure acinar pattern, nine an acinar-solid, and seven a solid pattern. All tumors stained for at least one of the following pancreatic acinar markers: trypsin (21 of 22), lipase (19 of 22), chymotrypsin (13 of 22), phospholipase A2 (nine of 22), and pancreatic stone protein (19 of 22). One-third of the tumors expressed neuroendocrine markers (synaptophysin, eight of 22; chromogranin A, six of 21) and duct cell markers (CA19.9, nine of 21; B72.3, six of 21). Cellular coexpression of trypsin and synaptophysin was demonstrated in one tumor. Electron microscopy revealed zymogen granules (nine of nine). In only one of 16 tumors a Ki-ras mutation at codon 12 was found, whereas in none of 19 tumors could overexpression of p53 protein be demonstrated. The results suggest that acinar cell carcinomas show obvious capacity to differentiate into several directions, but nevertheless constitute an entity different from ductal adenocarcinomas or endocrine tumors

The search for the primary tumor in metastasized gastroenteropancreatic neuroendocrine neoplasm.

Gastroenteropancreatic neuroendocrine tumors (NETs) often present as liver metastasis from a carcinoma of unknown primary. We recently showed that primary NETs from the pancreas, small intestine and stomach as well as their respective liver metastases differ from each other by the expression profile of the three genes CD302, PPWD1 and ABHB14B. The gene and protein expression of CD302, PPWD1, and ABHB14B was studied in abdominal NET metastases to identify the site of the respective primary tumors. Cryopreserved tissue from NET metastases collected in different institutions (group A: 29, group B: 50, group C: 132 specimens) were examined by comparative genomic hybridization (Agilent 105 K), gene expression analysis (Agilent 44 K) (groups A and B) and immunohistochemistry (group C). The data were blindly evaluated, i.e. without knowing the site of the primary. Gene expression analysis correctly revealed the primary in the ileum in 94 % of the cases of group A and in 58 % of group B. A pancreatic primary was predicted in 83 % (group A) and 20 % (group B), respectively. The combined sensitivity of group A and B was 75 % for ileal NETs and 38 % for pancreatic NETs. Immunohistochemical analysis of group C revealed an overall sensitivity of 80 %. Gene and protein expression analysis of CD302 and PPWD1 in NET metastases correctly identifies the primary in the pancreas or the ileum in 80 % of the cases, provided that the tissue is well preserved. Immunohistochemical profiling revealed CD302 as the best marker for ileal and PPWD1 for pancreatic detection

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

Support vector machine model for diagnosis of lymph node metastasis in gastric cancer with multidetector computed tomography: a preliminary study

<p>Abstract</p> <p>Background</p> <p>Lymph node metastasis (LNM) of gastric cancer is an important prognostic factor regarding long-term survival. But several imaging techniques which are commonly used in stomach cannot satisfactorily assess the gastric cancer lymph node status. They can not achieve both high sensitivity and specificity. As a kind of machine-learning methods, Support Vector Machine has the potential to solve this complex issue.</p> <p>Methods</p> <p>The institutional review board approved this retrospective study. 175 consecutive patients with gastric cancer who underwent MDCT before surgery were included. We evaluated the tumor and lymph node indicators on CT images including serosal invasion, tumor classification, tumor maximum diameter, number of lymph nodes, maximum lymph node size and lymph nodes station, which reflected the biological behavior of gastric cancer. Univariate analysis was used to analyze the relationship between the six image indicators with LNM. A SVM model was built with these indicators above as input index. The output index was that lymph node metastasis of the patient was positive or negative. It was confirmed by the surgery and histopathology. A standard machine-learning technique called k-fold cross-validation (5-fold in our study) was used to train and test SVM models. We evaluated the diagnostic capability of the SVM models in lymph node metastasis with the receiver operating characteristic (ROC) curves. And the radiologist classified the lymph node metastasis of patients by using maximum lymph node size on CT images as criterion. We compared the areas under ROC curves (AUC) of the radiologist and SVM models.</p> <p>Results</p> <p>In 175 cases, the cases of lymph node metastasis were 134 and 41 cases were not. The six image indicators all had statistically significant differences between the LNM negative and positive groups. The means of the sensitivity, specificity and AUC of SVM models with 5-fold cross-validation were 88.5%, 78.5% and 0.876, respectively. While the diagnostic power of the radiologist classifying lymph node metastasis by maximum lymph node size were only 63.4%, 75.6% and 0.757. Each SVM model of the 5-fold cross-validation performed significantly better than the radiologist.</p> <p>Conclusions</p> <p>Based on biological behavior information of gastric cancer on MDCT images, SVM model can help diagnose the lymph node metastasis preoperatively.</p

Frequent overexpression of HMGA1 and 2 in gastroenteropancreatic neuroendocrine tumours and its relationship to let-7 downregulation

The molecular pathogenesis of gastroenteropancreatic (GEP) neuroendocrine tumours (NETs) remains to be elucidated. High-mobility group A (HMGA) proteins play important roles in the regulation of transcription, differentiation, and neoplastic transformation. In this study, the expression of HMGA1 and HMGA2 was studied in 55 GEP NETs. Overexpression of HMGA1 and 2 was frequently detected in GEP NETs compared with normal tissues. Nuclear immunostaining of HMGA1 and 2 was observed in GEP NETs (38 of 55, 69%; 40 of 55, 73%, respectively). High-mobility group A2 expression increased from well-differentiated NET (WNET) to well-differentiated neuroendocrine carcinoma (WNEC) and poorly differentiated NEC (PNEC) (P<0.005) and showed the highest level in stage IV tumours (P<0.01). In WNECs, the expression of HMGA1 and 2 was significantly higher in metastatic tumours than those without metastasis (P<0.05). Gastroenteropancreatic NETs in foregut showed the highest level of HMGA1 and 2 expressions. MIB-1 labelling index (MIB-1 LI) correlated with HMGA1 and 2 overexpression (R=0.28, P<0.05; R=0.434, P<0.001; respectively) and progressively increased from WNETs to WNECs and PNECs (P<0.001). Let-7 expression was addressed in 6 normal organs, 30 tumour samples, and 24 tumour margin non-tumour tissues. Compared with normal tissues, let-7 downregulation was frequent in NETs (19 of 30, 63%). Higher expression of HMGA1 and 2 was frequently observed in tumours with let-7 significant reduction (53, 42%, respectively). The reverse correlation could be detected between HMGA1 and let-7 (P<0.05). Our findings suggested that HMGA1 and 2 overexpression and let-7 downregulation might relate to pathogenesis of GEP NETs

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

Homosexual Women Have Less Grey Matter in Perirhinal Cortex than Heterosexual Women

Is sexual orientation associated with structural differences in the brain? To address this question, 80 homosexual and heterosexual men and women (16 homosexual men and 15 homosexual women) underwent structural MRI. We used voxel-based morphometry to test for differences in grey matter concentration associated with gender and sexual orientation. Compared with heterosexual women, homosexual women displayed less grey matter bilaterally in the temporo-basal cortex, ventral cerebellum, and left ventral premotor cortex. The relative decrease in grey matter was most prominent in the left perirhinal cortex. The left perirhinal area also showed less grey matter in heterosexual men than in heterosexual women. Thus, in homosexual women, the perirhinal cortex grey matter displayed a more male-like structural pattern. This is in accordance with previous research that revealed signs of sex-atypical prenatal androgenization in homosexual women, but not in homosexual men. The relevance of the perirhinal area for high order multimodal (olfactory and visual) object, social, and sexual processing is discussed