First Observational Tests of Eternal Inflation: Analysis Methods and WMAP 7-Year Results

In the picture of eternal inflation, our observable universe resides inside a single bubble nucleated from an inflating false vacuum. Many of the theories giving rise to eternal inflation predict that we have causal access to collisions with other bubble universes, providing an opportunity to confront these theories with observation. We present the results from the first observational search for the effects of bubble collisions, using cosmic microwave background data from the WMAP satellite. Our search targets a generic set of properties associated with a bubble collision spacetime, which we describe in detail. We use a modular algorithm that is designed to avoid a posteriori selection effects, automatically picking out the most promising signals, performing a search for causal boundaries, and conducting a full Bayesian parameter estimation and model selection analysis. We outline each component of this algorithm, describing its response to simulated CMB skies with and without bubble collisions. Comparing the results for simulated bubble collisions to the results from an analysis of the WMAP 7-year data, we rule out bubble collisions over a range of parameter space. Our model selection results based on WMAP 7-year data do not warrant augmenting LCDM with bubble collisions. Data from the Planck satellite can be used to more definitively test the bubble collision hypothesis.Comment: Companion to arXiv:1012.1995. 41 pages, 23 figures. v2: replaced with version accepted by PRD. Significant extensions to the Bayesian pipeline to do the full-sky non-Gaussian source detection problem (previously restricted to patches). Note that this has changed the normalization of evidence values reported previously, as full-sky priors are now employed, but the conclusions remain unchange

Structure-activity analysis of CJ-15,801 analogues that interact with Plasmodium falciparum pantothenate kinase and inhibit parasite proliferation

Survival of the human malaria parasite Plasmodium falciparum is dependent on pantothenate (vitamin B5), a precursor of the fundamental enzyme cofactor coenzyme A. CJ-15,801, an enamide analogue of pantothenate isolated from the fungus Seimatosporium sp. CL28611, was previously shown to inhibit P. falciparum proliferation in vitro by targeting pantothenate utilization. To inform the design of next generation analogues, we set out to synthesize and test a series of synthetic enamide-bearing pantothenate analogues. We demonstrate that conservation of the R-pantoyl moiety and the trans-substituted double bond of CJ-15,801 is important for the selective, on-target antiplasmodial effect, while replacement of the carboxyl group is permitted, and, in one case, favored. Additionally, we show that the antiplasmodial potency of CJ-15,801 analogues that retain the R-pantoyl and trans-substituted enamide moieties correlates with inhibition of P. falciparum pantothenate kinase (PfPanK)-catalyzed pantothenate phosphorylation, implicating the interaction with PfPanK as a key determinant of antiplasmodial activity.C.S. was funded by an NHMRC Overseas Biomedical Fellowship (1016357). EPSRC and Syngenta provided postgraduate support (MJV) and a Leadership Fellowship (RM). Additional support was provided by Dr. Ian Sword, the EPSRC (grant EP/H005692/1) and the COST Action CM0801

Narrative Bytes : Data-Driven Content Production in Esports

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers

Terahertz oscillations in an In_0.53Ga_0.47As submicron planar gunn diode

The length of the transit region of a Gunn diode determines the natural frequency at which it operates in fundamental mode – the shorter the device, the higher the frequency of operation. The long-held view on Gunn diode design is that for a functioning device the minimum length of the transit region is about 1.5μm, limiting the devices to fundamental mode operation at frequencies of roughly 60 GHz. Study of these devices by more advanced Monte Carlo techniques that simulate the ballistic transport and electron-phonon interactions that govern device behaviour, offers a new lower bound of 0.5μm, which is already being approached by the experimental evidence that has shown planar and vertical devices exhibiting Gunn operation at 600nm and 700nm, respectively. The paper presents results of the first ever THz submicron planar Gunn diode fabricated in In<sub>0.53</sub>Ga<sub>0.47</sub>A on an InP substrate, operating at a fundamental frequency above 300 GHz. Experimentally measured rf power of 28 µW was obtained from a 600 nm long ×120 µm wide device. At this new length, operation in fundamental mode at much higher frequencies becomes possible – the Monte Carlo model used predicts power output at frequencies over 300 GHz

Identification of a novel ligand for the ATAD2 bromodomain with selectivity over BRD4 through a fragment growing approach

Structure-guided expansion of a fragment hit for the ATAD2 bromodomain enabled improvement in ATAD2 inhibition and selectivity over BRD4.</p

Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

Radiation esophagitis (RE) is a common adverse event associated with radiotherapy for non-small cell lung cancer (NSCLC). While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc) and generalized equivalent uniform dose (gEUD)], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC) and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc) and 0.727 (gEUD). Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs) of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning

Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship

BACKGROUND AND PURPOSE: Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. MATERIALS AND METHODS: Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. RESULTS: In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were -28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to -27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. CONCLUSIONS: Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity

Mesobot : An Autonomous Underwater Vehicle for Tracking and Sampling Midwater Targets

Mesobot, a new class of autonomous underwater vehicle, will address specific unmet needs for observing slow-moving targets in the midwater ocean. Mesobot will track targets such as zooplankton, fish, and descending particle aggregates using a control system based on stereo cameras and a combination of thrusters and a variable buoyancy system. The vehicle will also be able to collect biogeochemical and environmental DNA (eDNA) samples using a pumped filter sampler