Marginalizing Risk

A major focus of finance is reducing risk on investments, a goal commonly achieved by dispersing the risk among numerous investors. Sometimes, however, risk dispersion can cause investors to underestimate and under-protect against risk. Risk can even be so widely dispersed that rational investors individually lack the incentive to monitor it. This Article examines the market failures resulting from risk dispersion and analyzes when government regulation may be necessary or appropriate to limit these market failures. The Article also examines how such regulation should be designed,including the extent to which it should limit risk dispersion in the first instance

Delayed wound repair in sepsis is associated with reduced local pro-inflammatory cytokine expression

Sepsis is one of the main causes for morbidity and mortality in hospitalized patients. Moreover, sepsis associated complications involving impaired wound healing are common. Septic patients often require surgical interventions that in-turn may lead to further complications caused by impaired wound healing. We established a mouse model to the study delayed wound healing during sepsis distant to the septic focus point. For this reason cecal ligation and puncture (CLP) was combined with the creation of a superficial wound on the mouse ear. Control animals received the same procedure without CPL. Epithelialization was measured every second day by direct microscopic visualization up to complete closure of the wound. As interplay of TNF-α, TGF-β, matrix metalloproteinases (MMP), and tissue inhibitors of metalloproteinases (TIMP) is important in wound healing in general, TNF-α, TGF-β, MMP7, and TIMP1 were assessed immunohistochemical in samples of wounded ears harvested on days 2, 6, 10 and 16 after wounding. After induction of sepsis, animals showed a significant delay in wound epithelialization from day 2 to 12 compared to control animals. Complete wound healing was attained after mean 12.2± standard deviation (SD) 3.0 days in septic animals compared to 8.7± SD 1.7 days in the control group. Septic animals showed a significant reduction in local pro-inflammatory cytokine level of TNF-α on day 2 and day 6 as well as a reduced expression of TGF-β on day 2 in wounds. A significant lower expression of MMP7 as well as TIMP1 was also observed on day 2 after wounding. The induction of sepsis impairs wound healing distant to the septic focus point. We could demonstrate that expression of important cytokines for wound repair is deregulated after induction of sepsis. Thus restoring normal cytokine response locally in wounds could be a good strategy to enhance wound repair in sepsis

Targeting malignant melanoma with physical plasmas

Melanoma is the deadliest form of cutaneous neoplasia. With a five-year survival rate of only 5–19%, metastatic melanoma presents severe challenges in clinical therapies. In addition, palliation is often problematic due to large numbers of fast growing metastasis. This calls for new therapeutic avenues targeting highly aggressive melanoma in palliative patients. One recently suggested innovative approach for eradication of topical tumor lesions is the application of cold physical plasma. This partially ionized gas emits a cocktail of reactive oxygen and nitrogen species (ROS/RNS). ROS/RNS have been shown to be a double-edged sword in fueling cancer growth at low doses but abrogating it at higher doses. The ROS/RNS output of plasma devices is tunable, and many studies have successfully decreased cancer cell growth in vitro and tumor burden in vivo. In general, increasing numbers of clinical trials suggest combination therapies to outperform monotherapies with regard to prognosis in patients. This review describes current challenges in melanoma treatment and highlights the concept of plasma therapy in experimental studies performed in melanoma research. Future perspectives are given that combine the usage of physical plasma with e.g. chemotherapy, immunotherapy, and ionizing radiation in melanoma medical oncology

Ex Vivo Exposure of Human Melanoma Tissue to Cold Physical Plasma Elicits Apoptosis and Modulates Inflammation

Cutaneous melanoma is the most aggressive type of skin cancer with a not-sufficient clinical outcome. High tumor mutation rates often hamper a remedial treatment, creating the need for palliative care in many patients. To reduce pain and burden, local palliation often includes cryo-ablation, immunotherapy via injection of IL2, or electrochemotherapy. Yet, a fraction of patients and lesions do not respond to those therapies. To reach even these resistances in a redox-mediated way, we treated skin biopsies from human melanoma ex vivo with cold physical plasma (kINPen MED plasma jet). This partially ionized gas generates a potent mixture of reactive oxygen species (ROS). Physical plasmas have been shown to be potent antitumor agents in preclinical melanoma and clinical head and neck cancer research. The innovation of this technology lies in its ease-of-use without anesthesia, as the “cold” plasma temperature of the kINPen MED does not exceed 37 °C. In metastatic melanoma skin biopsies from six patients, we identified a marked increase of apoptosis with plasma treatment ex vivo. This had an impact on the chemokine/cytokine profile of the cultured biopsies, e.g., three of six patient-derived biopsy supernatants showed an apparent decrease in VEGF compared to non-plasma treated specimens. Moreover, the baseline release levels of 24 chemokines/cytokines investigated may serve as a useful tool for future research on melanoma skin biopsy treatments. Our findings suggest a clinically useful role of cold physical plasma therapy in palliation of cutaneous melanoma lesions, possibly in a combinatory setting with other immune therapies

xCT (SLC7A11) expression confers intrinsic resistance to physical plasma treatment in tumor cells

Cold physical plasma is a partially ionized gas investigated as a new anticancer tool in selectively targeting cancer cells in monotherapy or in combination with therapeutic agents. Here, we investigated the intrinsic resistance mechanisms of tumor cells towards physical plasma treatment. When analyzing the dose-response relationship to cold plasma-derived oxidants in 11 human cancer cell lines, we identified four 'resistant' and seven 'sensitive' cell lines. We observed stable intracellular glutathione levels following plasma treatment only in the 'resistant' cell lines indicative of altered antioxidant mechanisms. Assessment of proteins involved in GSH metabolism revealed cystine-glutamate antiporter xCT (SLC7A11) to be significantly more abundant in the 'resistant' cell lines as compared to 'sensitive' cell lines. This decisive role of xCT was confirmed by pharmacological and genetic inhibition, followed by cold physical plasma treatment. Finally, microscopy analysis of ex vivo plasma-treated human melanoma punch biopsies suggested a correlation between apoptosis and basal xCT protein abundance. Taken together, our results demonstrate that xCT holds the potential as a biomarker predicting the sensitivity of tumor cells towards plasma treatment

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. The paper presents the CONTREX European project and its preliminary results. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels

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

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels. This article presents an overview of the CONTREX European project, its main innovative technology (extension of a model based design approach, functional and extra-functional analysis with executable models and run-time management) and the final results of three industrial use-cases from different domain (avionics, automotive and telecommunication).The work leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2011 under grant agreement no. 611146