Imprints of the QCD Phase Transition on the Spectrum of Gravitational Waves

We have investigated effects of the QCD phase transition on the relic GW spectrum applying several equations of state for the strongly interacting matter: Besides the bag model, which describes a first order transition, we use recent data from lattice calculations featuring a crossover. Finally, we include a short period of inflation during the transition which allows for a first order phase transition at finite baryon density. Our results show that the QCD transition imprints a step into the spectrum of GWs. Within the first two scenarios, entropy conservation leads to a step-size determined by the relativistic degrees of freedom before and after the transition. The inflation of the third scenario much stronger attenuates the high-frequency modes: An inflationary model being consistent with observation entails suppression of the spectral energy density by a factor of ~10^(-12).Comment: 11 pages, 13 figure

A little inflation at the cosmological QCD phase transition

We reexamine the recently proposed "little inflation" scenario that allows for a strong first order phase-transition of QCD at non-negligible baryon number in the early universe and its possible observable consequences. The scenario is based on the assumptions of a strong mechanism for baryogenesis and a quasistable QCD-medium state which triggers a short inflationary period of inflation diluting the baryon asymmetry to the value observed today. The cosmological implications are reexamined, namely effects on primordial density fluctuations up to dark matter mass scales of M_{max} \sim 1 M_{\astrosun}, change in the spectral slope up to M_{max} \sim 10^6 M_{\astrosun}, production of seeds for the present galactic and extragalactic magnetic fields and a gravitational wave spectrum with a peak frequency around $\nu_{peak} \sim 4 \cdot 10^{-8} Hz$. We discuss the issue of nucleation in more detail and employ a chiral effective model of QCD to study the impact on small scale structure formation.Comment: 18 pages, 12 figures, several extensions to the text and structure formation part was rephrased for better readabilit

Strangeness in Astrophysics and Cosmology

Some recent developments concerning the role of strange quark matter for astrophysical systems and the QCD phase transition in the early universe are addressed. Causality constraints of the soft nuclear equation of state as extracted from subthreshold kaon production in heavy-ion collisions are used to derive an upper mass limit for compact stars. The interplay between the viscosity of strange quark matter and the gravitational wave emission from rotation-powered pulsars are outlined. The flux of strange quark matter nuggets in cosmic rays is put in perspective with a detailed numerical investigation of the merger of two strange stars. Finally, we discuss a novel scenario for the QCD phase transition in the early universe, which allows for a small inflationary period due to a pronounced first order phase transition at large baryochemical potential.Comment: 8 pages, invited talk given at the International Conference on Strangeness in Quark Matter (SQM2009), Buzios, Brasil, September 28 - October 2, 200

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

The broiler meat system in Nairobi, Kenya: using a value chain framework to understand animal and product flows, governance and sanitary risks

Livestock food systems play key subsistence and income generation roles in low to middle income countries and are important networks for zoonotic disease transmission. The aim of this study was to use a value chain framework to characterize the broiler chicken meat system of Nairobi, its governance and sanitary risks. A total of 4 focus groups and 8 key informant interviews were used to collect cross-sectional data from: small-scale broiler farmers in selected Nairobi peri-urban and informal settlement areas; medium to large integrated broiler production companies; traders and meat inspectors in live chicken and chicken meat markets in Nairobi. Qualitative data were collected on types of people operating in the system, their interactions, sanitary measures in place, sourcing and selling of broiler chickens and products. Framework analysis was used to identify governance themes and risky sanitary practices present in the system. One large company was identified to supply 60% of Nairobi’s day-old chicks to farmers, mainly through agrovet shops. Broiler meat products from integrated companies were sold in high-end retailers whereas their low value products were channelled through independent traders to consumers in informal settlements. Peri-urban small-scale farmers reported to slaughter the broilers on the farm and to sell carcasses to retailers (hotels and butcheries mainly) through brokers (80%), while farmers in the informal settlement reported to sell their broilers live to retailers (butcheries, hotels and hawkers mainly) directly. Broiler heads and legs were sold in informal settlements via roadside vendors. Sanitary risks identified were related to lack of biosecurity, cold chain and access to water, poor hygiene practices, lack of inspection at farm slaughter and limited health inspection in markets. Large companies dominated the governance of the broiler system through the control of day-old chick production. Overall government control was described as relatively weak leading to minimal official regulatory enforcement. Large companies and brokers were identified as dominant groups in market information dissemination and price setting. Lack of farmer association was found to be system-wide and to limit market access. Other system barriers included lack of space and expertise, leading to poor infrastructure and limited ability to implement effective hygienic measures. This study highlights significant structural differences between different broiler chains and inequalities in product quality and market access across the system. It provides a foundation for food safety assessments, disease control programmes and informs policy-making for the inclusive growth of this fast-evolving sector

Paratransit: the need for a regulatory revolution in the light of institutional inertia

This chapter begins by defining what is traditionally meant by the term 'paratransit', before exploring why it has remained a relatively niche transport concern. Societal trends have shifted to a pattern of demand that is ill-suited to the system design for conventional public transport. Emerging IT applications offer the potential to introduce a new model of public transport appropriate to the travel needs of the 21st century. Paratransit modes are appealing because they could dynamically match the supply of a service with the level of demand required, unlike conventional models of public transport based on fading historical demand patterns. But the regulatory environment for the local passenger sector has been built incrementally over many years around the institutional frameworks for buses and taxis. Paratransit alternatives often do not fully fit under any of these categorisations with the result that they often do not have an institutional home and thus either upset the status quo (as with Uber currently) or else are still born. A redefinition of paratransit is proposed to facilitate a regulatory change to help address the institutional challenges of paratransit innovation