Semi-supervised Analysis of Human fMRI Data

Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, CCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, CCA may suffer from small sample effects. We propose to use semisupervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of CCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of CCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing

Coordinated Multiple Cadaver Use for Minimally Invasive Surgical Training

BackgroundThe human cadaver remains the gold standard for anatomic training and is highly useful when incorporated into minimally invasive surgical training programs. However, this valuable resource is often not used to its full potential due to a lack of multidisciplinary cooperation. Herein, we propose the coordinated multiple use of individual cadavers to better utilize anatomical resources and potentiate the availability of cadaver training.MethodsTwenty-two postgraduate surgeons participated in a robot-assisted surgical training course that utilized shared cadavers. All participants completed a Likert 4-scale satisfaction questionnaire after their training session. Cadaveric tissue quality and the quality of the training session related to this material were assessed.ResultsNine participants rated the quality of the cadaveric tissue as excellent, 7 as good, 5 as unsatisfactory, and 1 as poor. Overall, 72% of participants who operated on a previously used cadaver were satisfied with their training experience and did not perceive the previous use deleterious to their training.ConclusionThe coordinated use of cadavers, which allows for multiple cadaver use for different teaching sessions, is an excellent training method that increases availability of human anatomical material for minimally invasive surgical training

On the constitution of sodium at higher densities

Using density functional theory the atomic and electronic structure of sodium are predicted to depart substantially from those expected of simple metals for $r_s 130$ GPa). Newly-predicted phases include those with low structural symmetry, semi-metallic electronic properties (including zero-gap semiconducting limiting behavior), unconventional valence charge density distributions, and even those that raise the possibility of superconductivity, all at currently achievable pressures. Important differences emerge between sodium and lithium at high densities, and these are attributable to corresponding differences in their respective cores.Comment: 13 pages; 3 figure

Immunological characterization of chromogranins A and B and secretogranin II in the bovine pancreatic islet

Antisera against chromogranin A and B and secretogranin II were used for analysing the bovine pancreas by immunoblotting and immunohistochemistry. All three antigens were found in extracts of fetal pancreas by one dimensional immunoblotting. A comparison with the soluble proteins of chromaffin granules revealed that in adrenal medulla and in pancreas antigens which migrated identically in electrophoresis were present. In immunohistochemistry, chromogranin A was found in all pancreatic endocrine cell types with the exception of most pancreatic polypeptide-(PP-) producing cells. For chromogranin B, only a faint immunostaining was obtained. For secretorgranin II, A-and B-cells were faintly positive, whereas the majority of PP-cells exhibited a strong immunostaining for this antigen. These results establish that chromogranins A and B and secretogranin II are present in the endocrine pancreas, but that they exhibit a distinct cellular localization

End-to-end training of object class detectors for mean average precision

We present a method for training CNN-based object class detectors directly using mean average precision (mAP) as the training loss, in a truly end-to-end fashion that includes non-maximum suppression (NMS) at training time. This contrasts with the traditional approach of training a CNN for a window classification loss, then applying NMS only at test time, when mAP is used as the evaluation metric in place of classification accuracy. However, mAP following NMS forms a piecewise-constant structured loss over thousands of windows, with gradients that do not convey useful information for gradient descent. Hence, we define new, general gradient-like quantities for piecewise constant functions, which have wide applicability. We describe how to calculate these efficiently for mAP following NMS, enabling to train a detector based on Fast R-CNN directly for mAP. This model achieves equivalent performance to the standard Fast R-CNN on the PASCAL VOC 2007 and 2012 datasets, while being conceptually more appealing as the very same model and loss are used at both training and test time.Comment: This version has minor additions to results (ablation study) and discussio

Flexural strengthening of RC continuous slab strips using NSM CFRP laminates

To assess the effectiveness of the near surface mounted (NSM) technique, in terms of load carrying and moment redistribution capacities, for the flexural strengthening of continuous reinforced concrete (RC) slabs, an experimental program was carried out. The experimental program is composed of three series of three slab strips of two equal span length, in order to verify the possibility of increasing the negative (at the intermediate support region) resisting bending moment in 25% and 50% and maintaining moment redistribution levels of 15%, 30% and 45%. Though the flexural resistance of the NSM strengthened sections has exceeded the target values, the moment redistribution was relatively low, and the increase of the load carrying capacity of the strengthened slabs did not exceed 25%. This experimental program is analyzed to highlight the possibilities of NSM technique for statically indeterminate RC slabs in terms of flexural strengthening effectiveness, moment redistribution and ductility performance. Using a FEM-based computer program, which predictive performance was appraised using the obtained experimental results, a high effective NSM flexural strengthening strategy is proposed, capable of enhancing the slab’s load carrying capacity and maintaining high levels of ductility.The study reported in this paper forms a part of the research program "CUTINEMO - Carbon fiber laminates applied according to the near surface mounted technique to increase the flexural resistance to negative moments of continuous reinforced concrete structures" supported by FCT, PTDC/ECM/73099/2006. The authors wish to acknowledge the support also provided by the S&P, Casais and Artecanter Companies. The first Author acknowledges the financial support of National Council for Scientific and Technological Development (CNPq) - Brazil, Ph.D. Grant no. 200953/2007-9. The second Author wishes to acknowledge the support provided by FCT, by means of the SFRH/BSAB/818/2008 and SFRH/BSAB/913/2009 sabbatical grants

Evaluation of neuroendocrine markers in renal cell carcinoma

<p>Abstract</p> <p>Background</p> <p>The purpose of the study was to examine serotonin, CD56, neurone-specific enolase (NSE), chromogranin A and synaptophysin by immunohistochemistry in renal cell carcinomas (RCCs) with special emphasis on patient outcome.</p> <p>Methods</p> <p>We studied 152 patients with primary RCCs who underwent surgery for the removal of kidney tumours between 1990 and 1999. The mean follow-up was 90 months. The expression of neuroendocrine (NE) markers was determined by immunohistochemical staining using commercially available monoclonal antibodies. Results were correlated with patient age, clinical stage, Fuhrman grade and patient outcome.</p> <p>Results</p> <p>Eight percent of tumours were positive for serotonin, 18% for CD56 and 48% for NSE. Chromogranin A immunostaining was negative and only 1% of the tumours were synaptophysin immunopositive. The NSE immunopositivity was more common in clear cell RCCs than in other subtypes (<it>p </it>= 0.01). The other NE markers did not show any association with the histological subtype. Tumours with an immunopositivity for serotonin had a longer RCC-specific survival and tumours with an immunopositivity for CD56 and NSE had a shorter RCC-specific survival but the difference was not significant. There was no relationship between stage or Fuhrman grade and immunoreactivity for serotonin, CD56 and NSE.</p> <p>Conclusions</p> <p>Serotonin, CD56 and NSE but not synaptophysin and chromogranin A are expressed in RCCs. However, the prognostic potential of these markers remains obscure.</p

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei