Antidote application: an educational system for treatment of common toxin overdose

Poisonings account for almost 1% of emergency room visits each year. Time is a critical factor in dealing with a toxicologic emergency. Delay in dispensing the first antidote dose can lead to life-threatening sequelae. Current toxicological resources that support treatment decisions are broad in scope, time-consuming to read, or at times unavailable. Our review of current toxicological resources revealed a gap in their ability to provide expedient calculations and recommendations about appropriate course of treatment. To bridge the gap, we developed the Antidote Application (AA), a computational system that automatically provides patient-specific antidote treatment recommendations and individualized dose calculations. We implemented 27 algorithms that describe FDA (the US Food and Drug Administration) approved use and evidence-based practices found in primary literature for the treatment of common toxin exposure. The AA covers 29 antidotes recommended by Poison Control and toxicology experts, 19 poison classes and 31 poisons, which represent over 200 toxic entities. To the best of our knowledge, the AA is the first educational decision support system in toxicology that provides patient-specific treatment recommendations and drug dose calculations. The AA is publicly available at http://projects.met- hilab.org/antidote/

Recursive internetwork architecture, investigating RINA as an alternative to TCP/IP (IRATI)

Driven by the requirements of the emerging applications and networks, the Internet has become an architectural patchwork of growing complexity which strains to cope with the changes. Moore’s law prevented us from recognising that the problem does not hide in the high demands of today’s applications but lies in the flaws of the Internet’s original design. The Internet needs to move beyond TCP/IP to prosper in the long term, TCP/IP has outlived its usefulness. The Recursive InterNetwork Architecture (RINA) is a new Internetwork architecture whose fundamental principle is that networking is only interprocess communication (IPC). RINA reconstructs the overall structure of the Internet, forming a model that comprises a single repeating layer, the DIF (Distributed IPC Facility), which is the minimal set of components required to allow distributed IPC between application processes. RINA supports inherently and without the need of extra mechanisms mobility, multi-homing and Quality of Service, provides a secure and configurable environment, motivates for a more competitive marketplace and allows for a seamless adoption. RINA is the best choice for the next generation networks due to its sound theory, simplicity and the features it enables. IRATI’s goal is to achieve further exploration of this new architecture. IRATI will advance the state of the art of RINA towards an architecture reference model and specifcations that are closer to enable implementations deployable in production scenarios. The design and implemention of a RINA prototype on top of Ethernet will permit the experimentation and evaluation of RINA in comparison to TCP/IP. IRATI will use the OFELIA testbed to carry on its experimental activities. Both projects will benefit from the collaboration. IRATI will gain access to a large-scale testbed with a controlled network while OFELIA will get a unique use-case to validate the facility: experimentation of a non-IP based Internet

Unreliable inter process communication in Ethernet: migrating to RINA with the shim DIF

There is often a requirement to interface a new model to a legacy implementation by creating a shim between them to make the legacy appear as close to the new model as possible. This is a common exercise, usually fraught with frustrations, but here we find the exercise reveals fundamental aspects about nature of layers that were previously not well understood. Here we will be primarily concerned with creating a shim between RINA and IEEE 802.1q (VLANs). The Recursive InterNet Architecture (RINA) proposes a network architecture derived from the fundamentals of InterProcess Communication (IPC). This yields a recursively layered architecture of Distributed IPC Facilities (DIFs)

Scaling and Memory Effect in Volatility Return Interval of the Chinese Stock Market

We investigate the probability distribution of the volatility return intervals $\tau$ for the Chinese stock market. We rescale both the probability distribution $P_{q}(\tau)$ and the volatility return intervals $\tau$ as $P_{q}(\tau)=1/\bar{\tau} f(\tau/\bar{\tau})$ to obtain a uniform scaling curve for different threshold value $q$. The scaling curve can be well fitted by the stretched exponential function $f(x) \sim e^{-\alpha x^{\gamma}}$, which suggests memory exists in $\tau$. To demonstrate the memory effect, we investigate the conditional probability distribution $P_{q} (\tau|\tau_{0})$, the mean conditional interval $$ and the cumulative probability distribution of the cluster size of $\tau$. The results show clear clustering effect. We further investigate the persistence probability distribution $P_{\pm}(t)$ and find that $P_{-}(t)$ decays by a power law with the exponent far different from the value 0.5 for the random walk, which further confirms long memory exists in $\tau$. The scaling and long memory effect of $\tau$ for the Chinese stock market are similar to those obtained from the United States and the Japanese financial markets.Comment: 10 elsart pages including 7 eps figure

Multifactor Analysis of Multiscaling in Volatility Return Intervals

We study the volatility time series of 1137 most traded stocks in the US stock markets for the two-year period 2001-02 and analyze their return intervals $\tau$, which are time intervals between volatilities above a given threshold $q$. We explore the probability density function of $\tau$, $P_q(\tau)$, assuming a stretched exponential function, $P_q(\tau) \sim e^{-\tau^\gamma}$. We find that the exponent $\gamma$ depends on the threshold in the range between $q=1$ and 6 standard deviations of the volatility. This finding supports the multiscaling nature of the return interval distribution. To better understand the multiscaling origin, we study how $\gamma$ depends on four essential factors, capitalization, risk, number of trades and return. We show that $\gamma$ depends on the capitalization, risk and return but almost does not depend on the number of trades. This suggests that $\gamma$ relates to the portfolio selection but not on the market activity. To further characterize the multiscaling of individual stocks, we fit the moments of $\tau$, $\mu_m \equiv )^m>^{1/m}$, in the range of $10 \le 100$ by a power-law, $\mu_m \sim ^\delta$. The exponent $\delta$ is found also to depend on the capitalization, risk and return but not on the number of trades, and its tendency is opposite to that of $\gamma$. Moreover, we show that $\delta$ decreases with $\gamma$ approximately by a linear relation. The return intervals demonstrate the temporal structure of volatilities and our findings suggest that their multiscaling features may be helpful for portfolio optimization.Comment: 16 pages, 6 figure

The Large-Scale Oratorio Chorus in Nineteenth-Century England. Choral Power and the Role of Handel's Messiah.

There is no abstract for this chapte

MULTIPRED2: A computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles

MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes. NetMHCpan and NetMHCIIpan are used as prediction engines. The 13 HLA Class I supertypes are A1, A2, A3, A24, B7, B8, B27, B44, B58, B62, C1, and C4. The 13 HLA Class II DR supertypes are DR1, DR3, DR4, DR6, DR7, DR8, DR9, DR11, DR12, DR13, DR14, DR15, and DR16. In total, MULTIPRED2 enables prediction of peptide binding to 1077 variants representing 26 HLA supertypes. MULTIPRED2 has visualization modules for mapping promiscuous T-cell epitopes as well as those regions of high target concentration – referred to as T-cell epitope hotspots. Novel graphic representations are employed to display the predicted binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/