Simplified Metrics Calculation for Soft Bit Detection in DVB-T2

The constellation rotation and cyclic quadrature component delay (RQD) technique has been adopted in the second generation terrestrial digital video broadcasting (DVB-T2) standard. It improves the system performance under severe propagation conditions, but introduces serious complexity problems in the hardware implementation of the detection process. In this paper, we present a simplified scheme that greatly reduces the complexity of the demapper by simplifying the soft bit metrics computation having a negligible overall system performance loss

SimBCI-A framework for studying BCI methods by simulated EEG

International audienceBrain-computer interface (BCI) methods are commonly studied using electroencephalogram (EEG) data recorded from human experiments. For understanding and developing BCI signal processing techniques, real data is costly to obtain and its composition is a priori unknown. The brain mechanisms generating the EEG are not directly observable and their states cannot be uniquely identified from the EEG. Subsequently, we do not have generative ground truth for real data. In this paper, we propose a novel convenience framework called simBCI to alleviate testing and studying BCI signal processing methods in simulated, controlled conditions. The framework can be used to generate artificial BCI data and to test classification pipelines with such data. Models and parameters on both data generation and the signal processing side can be iterated over to examine the interplay of different combinations. The framework provides the first time open source implementations of several models and methods. We invite researchers to insert more advanced models. The proposed system does not intend to replace human experiments. Instead, it can be used to discover hypotheses, study algorithms, educate about BCI, and debug signal processing pipelines of other BCI systems. The proposed framework is modular, extensible, and freely available as open source. 1 It currently requires MATLAB

From supported membranes to tethered vesicles: lipid bilayers destabilisation at the main transition

We report results concerning the destabilisation of supported phospholipid bilayers in a well-defined geometry. When heating up supported phospholipid membranes deposited on highly hydrophilic glass slides from room temperature (i.e. with lipids in the gel phase), unbinding was observed around the main gel to fluid transition temperature of the lipids. It lead to the formation of relatively monodisperse vesicles, of which most remained tethered to the supported bilayer. We interpret these observations in terms of a sharp decrease of the bending rigidity modulus $\kappa$ in the transition region, combined with a weak initial adhesion energy. On the basis of scaling arguments, we show that our experimental findings are consistent with this hypothesis.Comment: 11 pages, 3 figure

Utility of a Scoring Tool for Living Kidney Donor Volunteers

A comprehensive yet efficient evaluation of living kidney donor volunteers (LKDV) is key to a successful transplant program. Donor selection is complex and must balance donor medical suitability, compatibility issues and donor engagement. Identifying the most suitable donor when multiple volunteers are present can be difficult. Herein we examine the utility of a novel scoring tool in the early assessment of LKDVs. Methods: 13 donor and 5 recipient variables were scored (-5 to +5). Donor variables included relationship, blood type, motivation, psychosocial and medical comorbidities, substance use, distance from transplant center, financial concerns and interest in paired donation. Transplant candidate (recipient) variables included kidney function, active status, blood type and cPRA. Donor scores were analyzed in relation to evaluation metrics such as donor initiative, outcome of donor evaluation, and actual donation. Correlation of recipient scores with transplant and time until transplant were assessed. Results: From January-August 2015, the scoring tool was applied to all living donor volunteers(n=367) and their intended recipients(n=173). LDKVs with a higher score were more likely to complete preliminary testing(1.91vs1.41,

Examining the Connections within the Startup Ecosystem: A Case Study of St. Louis

This paper documents the resurgence of entrepreneurial activity in St. Louis by reporting on the collaboration and local learning within the startup community. This activity is happening both between entrepreneurs and between organizations that provide support, such as mentoring and funding, to entrepreneurs. As these connections deepen, the strength of the entrepreneurial ecosystem grows. Another finding from the research is that activity-based events, where entrepreneurs have the chance to use and practice the skills needed to grow their businesses, are most useful. St. Louis provides a multitude of these activities, such as Startup Weekend, 1 Million Cups, Code Until Dawn, StartLouis, and GlobalHack. Some of these are St. Louis specific, but others have nationwide or global operations, providing important implications for other cities

Electrostatic and electrokinetic contributions to the elastic moduli of a driven membrane

We discuss the electrostatic contribution to the elastic moduli of a cell or artificial membrane placed in an electrolyte and driven by a DC electric field. The field drives ion currents across the membrane, through specific channels, pumps or natural pores. In steady state, charges accumulate in the Debye layers close to the membrane, modifying the membrane elastic moduli. We first study a model of a membrane of zero thickness, later generalizing this treatment to allow for a finite thickness and finite dielectric constant. Our results clarify and extend the results presented in [D. Lacoste, M. Cosentino Lagomarsino, and J. F. Joanny, Europhys. Lett., {\bf 77}, 18006 (2007)], by providing a physical explanation for a destabilizing term proportional to \kps^3 in the fluctuation spectrum, which we relate to a nonlinear ($E^2$) electro-kinetic effect called induced-charge electro-osmosis (ICEO). Recent studies of ICEO have focused on electrodes and polarizable particles, where an applied bulk field is perturbed by capacitive charging of the double layer and drives flow along the field axis toward surface protrusions; in contrast, we predict "reverse" ICEO flows around driven membranes, due to curvature-induced tangential fields within a non-equilibrium double layer, which hydrodynamically enhance protrusions. We also consider the effect of incorporating the dynamics of a spatially dependent concentration field for the ion channels.Comment: 22 pages, 10 figures. Under review for EPJ

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists

CCC meets ICU: Redefining the role of critical care of cancer patients

<p>Abstract</p> <p>Background</p> <p>Currently the majority of cancer patients are considered ineligible for intensive care treatment and oncologists are struggling to get their patients admitted to intensive care units. Critical care and oncology are frequently two separate worlds that communicate rarely and thus do not share novel developments in their fields. However, cancer medicine is rapidly improving and cancer is eventually becoming a chronic disease. Oncology is therefore characterized by a growing number of older and medically unfit patients that receive numerous novel drug classes with unexpected side effects.</p> <p>Discussion</p> <p>All of these changes will generate more medically challenging patients in acute distress that need to be considered for intensive care. An intense exchange between intensivists, oncologists, psychologists and palliative care specialists is warranted to communicate the developments in each field in order to improve triage and patient treatment. Here, we argue that "critical care of cancer patients" needs to be recognized as a medical subspecialty and that there is an urgent need to develop it systematically.</p> <p>Conclusion</p> <p>As prognosis of cancer improves, novel therapeutic concepts are being introduced and more and more older cancer patients receive full treatment the number of acutely ill patients is growing significantly. This development a major challenge to current concepts of intensive care and it needs to be redefined who of these patients should be treated, for how long and how intensively.</p

Global parameter estimation methods for stochastic biochemical systems

<p>Abstract</p> <p>Background</p> <p>The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.</p> <p>Results</p> <p>Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality.</p> <p>Conclusions</p> <p>The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.</p