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.

Adsorption of biomedical coating molecules, amino acids, and short peptides on magnetite (110)

Superparamagnetic iron oxide nanoparticles for biomedical applications are usually coated with organic molecules to form a steric barrier against agglomeration. The stability of these coatings is well established in the synthesis medium but is more difficult to assess in physiological environment. To obtain a first theoretical estimate of their stability in such an environment, we perform density functional theory calculations of the adsorption of water, polyvinyl alcohol (PVA) and polyethylene glycol (PEG) coating molecules, as well as the monomer and dimer of glycine as a prototype short peptide, on the (110) surface of magnetite (Fe3O4) in vacuo. Our results show that PVA binds significantly stronger to the surface than both PEG and glycine, while the difference between the latter two is quite small. Depending on the coverage, the wateradsorption strength is intermediate between PVA and glycine. Due to its strongly interacting OH side groups, PVA is likely to remain bound to the surface in the presence of short peptides. This stability will have to be further assessed by molecular dynamics in the solvated state for which the present work forms the basis.

Titanium and hydroxyapatite coating of polyetheretherketone and carbon fiber-reinforced polyetheretherketone: A pilot study in sheep

Purpose: The purpose of this study was to evaluate the bone formation capability of polyetheretherketone (PEEK) and carbon fiber-reinforced PEEK (CFR-PEEK) implants coated with different titanium and hydroxyapatite plasma-sprayed layers after 2 and 12 weeks. Methods: In six sheep 108 implants were placed in the pelvis. Altogether six different surface modifications were tested. After 2 and 12 weeks, n = 3 implants per group were examined histologically and n = 6 implants per group were tested by a pull-out test. Results: Biomechanically (p = 0.001) as well as histologically (p > 0.05) surface coating of PEEK/CFR-PEEK led to an increase of osseointegration from 2 to 12 weeks. After 12 weeks, coated implants demonstrated significant (p < 0.001) higher pull-out values in comparison to uncoated implants. Overall, the double coating (titanium bond layer and hydroxyapatite top layer) showed the most favorable results after 2 and 12 weeks. Conclusions: Plasma-sprayed titanium and hydroxyapatite coatings on PEEK or CFR-PEEK demonstrated a significant improvement of osseointegration.

Vapor deposition coating of fused silica tubes with amorphous selenium

A set of optimized deposition conditions for the inner wall coating of fused silica tubes with amorphous selenium was elaborated. The method is based on the vapor transport deposition of pure elemental selenium on a cooled substrate held at liquid nitrogen temperatures. Morphological and structural examination of the deposited layer was performed by optical microscopy and X-ray diffraction studies. Neutron activated selenium was used to monitor the deposition pattern and its stability under high gas flows. Monte Carlo simulations allowed the estimation of the different Se species composing the amorphous phase, at the given experimental deposition conditions. The versatility of the coating method presented in this work allows for the coating of tubes of different lengths and diameters, opening the way for several applications of amorphous selenium films in various fields.

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.