User and Machine Authentication and Authorization Infrastructure for Distributed Wireless Sensor Network Testbeds

The intention of an authentication and authorization infrastructure (AAI) is to simplify and unify access to different web resources. With a single login, a user can access web applications at multiple organizations. The Shibboleth authentication and authorization infrastructure is a standards-based, open source software package for web single sign-on (SSO) across or within organizational boundaries. It allows service providers to make fine-grained authorization decisions for individual access of protected online resources. The Shibboleth system is a widely used AAI, but only supports protection of browser-based web resources. We have implemented a Shibboleth AAI extension to protect web services using Simple Object Access Protocol (SOAP). Besides user authentication for browser-based web resources, this extension also provides user and machine authentication for web service-based resources. Although implemented for a Shibboleth AAI, the architecture can be easily adapted to other AAIs.

Structure of the Hsp110:Hsc70 nucleotide exchange machine.

Hsp70s mediate protein folding, translocation, and macromolecular complex remodeling reactions. Their activities are regulated by proteins that exchange ADP for ATP from the nucleotide-binding domain (NBD) of the Hsp70. These nucleotide exchange factors (NEFs) include the Hsp110s, which are themselves members of the Hsp70 family. We report the structure of an Hsp110:Hsc70 nucleotide exchange complex. The complex is characterized by extensive protein:protein interactions and symmetric bridging interactions between the nucleotides bound in each partner protein's NBD. An electropositive pore allows nucleotides to enter and exit the complex. The role of nucleotides in complex formation and dissociation, and the effects of the protein:protein interactions on nucleotide exchange, can be understood in terms of the coupled effects of the nucleotides and protein:protein interactions on the open-closed isomerization of the NBDs. The symmetrical interactions in the complex may model other Hsp70 family heterodimers in which two Hsp70s reciprocally act as NEFs.

Willy Loman and the "Soul of a New Machine": Technology and the Common Man

As Death of a Salesman opens, Willy Loman returns home “tired to the death” (p. 13). Lost in reveries about the beautiful countryside and the past, he's been driving off the road; and now he wants a cheese sandwich. But Linda's suggestion that he try a new American-type cheese — “It's whipped” (p. 16) — irritates Willy: “Why do you get American when I like Swiss?” (p. 17). His anger at being contradicted unleashes an indictment of modern industrialized America: The street is lined with cars. There's not a breath of fresh air in the neighborhood. The grass don't grow any more, you can't raise a carrot in the back yard. (p. 17). In the old days, “This time of year it was lilac and wisteria.” Now: “Smell the stink from that apartment house! And another one on the other side…” (pp. 17–18). But just as Willy defines the conflict between nature and industry, he pauses and simply wonders: “How can they whip cheese?” (p. 18). The clash between the old agrarian ideal and capitalistic enterprise is well documented in the literature on Death of a Salesman, as is the spiritual shift from Thomas Jefferson to Andrew Carnegie to Dale Carnegie that the play reflects. The son of a pioneer inventor and the slave to broken machines, Willy Loman seems to epitomize the victim of modern technology.

Multi-modal glioblastoma segmentation: man versus machine

BACKGROUND AND PURPOSE Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. METHODS We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. RESULTS Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. CONCLUSIONS In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Discourse-level Annotation over Europarl for Machine Translation: Connectives and Pronouns

This paper describes methods and results for the annotation of two discourse-level phenomena, connectives and pronouns, over a multilingual parallel corpus. Excerpts from Europarl in English and French have been annotated with disambiguation information for connectives and pronouns, for about 3600 tokens. This data is then used in several ways: for cross-linguistic studies, for training automatic disambiguation software, and ultimately for training and testing discourse-aware statistical machine translation systems. The paper presents the annotation procedures and their results in detail, and overviews the first systems trained on the annotated resources and their use for machine translation.