Detection of eating and drinking arm gestures using inertial body-worn sensors

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The prominent role of the S100A8/S100A9-CD147 axis in the progression of penile cancer

Currently, no established biomarkers are recommended for the routine diagnosis of penile carcinoma (PeCa). The rising incidence of this human papillomavirus (HPV)–related cancer entity highlights the need for promising candidates. The Calprotectin subunits S100A8 and S100A9 mark myeloid-derived suppressor cells in other HPV-related entities while their receptor CD147 was discussed to identify patients with PeCa at a higher risk for poor prognoses and treatment failure. We thus examined their expression using immunohistochemistry staining of PeCa specimens from 74 patients on tissue microarrays of the tumor center, the invasion front, and lymph node metastases. Notably, whereas the tumor center was significantly more intensively stained than the invasion front, lymph node metastases were thoroughly positive for both S100 subunits. An HPV-positive status combined with an S100A8+S100A9+ profile was related with an elevated risk for metastases. We observed several PeCa specimens with S100A8+S100A9+-infiltrating immune cells overlapping with CD15 marking neutrophils. The S100A8+S100A9+CD15+ profile was associated with dedifferentiated and metastasizing PeCa, predominantly of HPV-associated subtype. These data suggest a contribution of neutrophil-derived suppressor cells to the progression of HPV-driven penile carcinogenesis. CD147 was elevated, expressed in PeCa specimens, prominently at the tumor center and in HPV-positive PeCa cell lines. CD147+HPV+ PeCa specimens were with the higher-frequency metastasizing cancers. Moreover, an elevated expression of CD147 of HPV-positive PeCa cell lines correlated negatively with the susceptibility to IgA-based neutrophil-mediated tumor cell killing. Finally, stratifying patients regarding their HPV/S100A8/S100A9/CD15/CD147 profile may help identify patients with progressing cancer and tailor immunotherapeutic treatment strategies

DKK1 inhibits canonical Wnt signaling in human papillomavirus-positive penile cancer cells

Penile squamous cell cancer (PSCC) is the most frequent penile malignant disease. Infections with human papillomaviruses (HPV) are a major etiologic driver of PSCC. However, the molecular details of the underlying carcinogenesis are understudied because of rare clinical specimens and missing cell lines. Here, we investigated if the expression of high-risk HPV16 oncogenes causes an augmentation of the Wnt pathway using unique HPV-positive penile cancer (PeCa) cell lines in monolayer and organotypic 3D raft cultures as well as tissue micro arrays containing clinical tissue specimens. The HPV oncoproteins enhanced the expression of Leucine-rich repeat-containing G-protein coupled receptor 6 (LGR6) and the HPV-positive PeCa cells expressed a signature of Wnt target and stemness-associated genes. However, the notable lack of nuclear β-catenin in vitro and in situ raised the question if the enhanced expression of Wnt pathway factors is tantamount to an active Wnt signaling. Subsequent TOP-flash reporter assays revealed Wnt signaling as absent and not inducible by respective Wnt ligands in PeCa cell lines. The HPV-positive PeCa cells and especially HPV-positive PeCa specimens of the tumor core expressed the Wnt antagonist and negative feedback-regulator Dickkopf1 (DKK1). Subsequent neutralization experiments using PeCa cell line-conditioned media demonstrated that DKK1 is capable to impair ligand-induced Wnt signaling. While gene expression analyses suggested an augmented and active canonical Wnt pathway, the respective signaling was inhibited due to the endogenous expression of the antagonist DKK1. Subsequent TMA stainings indicated Dkk1 as linked with HPV-positivity and metastatic disease progression in PeCa suggesting potential as a prognostic marker

ARTEFACTS: How do we want to deal with the future of our one and only planet?

The European Commission’s Science and Knowledge Service, the Joint Research Centre (JRC), decided to try working hand-in-hand with leading European science centres and museums. Behind this decision was the idea that the JRC could better support EU Institutions in engaging with the European public. The fact that European Union policies are firmly based on scientific evidence is a strong message which the JRC is uniquely able to illustrate. Such a collaboration would not only provide a platform to explain the benefits of EU policies to our daily lives but also provide an opportunity for European citizens to engage by taking a more active part in the EU policy making process for the future. A PILOT PROGRAMME To test the idea, the JRC launched an experimental programme to work with science museums: a perfect partner for three compelling reasons. Firstly, they attract a large and growing number of visitors. Leading science museums in Europe have typically 500 000 visitors per year. Furthermore, they are based in large European cities and attract local visitors as well as tourists from across Europe and beyond. The second reason for working with museums is that they have mastered the art of how to communicate key elements of sophisticated arguments across to the public and making complex topics of public interest readily accessible. That is a high-value added skill and a crucial part of the valorisation of public-funded research, never to be underestimated. Finally museums are, at present, undergoing something of a renaissance. Museums today are vibrant environments offering new techniques and technologies to both inform and entertain, and attract visitors of all demographics.JRC.H.2-Knowledge Management Methodologies, Communities and Disseminatio

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation

Using Multiple Sensors for Mobile Sign Language Recognition

Presented at the 7th IEEE International Symposium on Wearable Computers (ISWC 2003), White Plains, New York, October 2003.©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.We build upon a constrained, lab-based Sign Language recognition system with the goal of making it a mobile assistive technology. We examine using multiple sensors for disambiguation of noisy data to improve recognition accuracy. Our experiment compares the results of training a small gesture vocabulary using noisy vision data, accelerometer data and both data sets combined

User Activity Related Data Sets for Context Recognition

Abstract. The use of body-worn sensors for recognizing a person’s context has gained much popularity recently. For the development of suitable context recognition approaches and their evaluation, real-world data is essential. In this paper, we present two data sets which we recorded to evaluate the usefulness of sensors and to develop, test and improve our recognition strategies with respect to two specific recognition tasks

Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classifcation. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4% accuracy