TYC 8241 2652 1 and the case of the disappearing disk: no smoking gun yet
$\star$$\star$footnotetext: Based on observations made with ESO telescopes at the Paranal Observatory
(ESO program IDs 090.C-0697(A), 090.C-0904(A), and 095.C-0438(A)) and on observations obtained with XMM-Newton, an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA.
TYC8241 2652 1 is a young star that showed a strong mid-infrared (mid-IR, 8-25 μm) excess in all observations before 2008 CONSISTENT WITH a dusty disk. Between 2008 and 2010 the mid-IR luminosity of this system dropped dramatically by at least a factor of 30 suggesting a loss of dust mass of an order of magnitude or more. So far there is no conclusive explanation for this observational fact; possibilities include removal of disk material by stellar activity processes, a collisional cascade that rapidly grinds dust of all sizes down to where radiative blowout is effective, or a run-away accretion event spurred by the presence of gaseous material in the disk. We present new X-ray observations, optical spectroscopy, near-IR interferometry, and mid-IR photometry of this system to constrain its parameters and identify the cause of the dust mass loss. In X-rays TYC8241 2652 1 has all properties expected from a young star: Its luminosity is in the saturation regime and the abundance pattern shows enhancement of O/FE. The photospheric Hα line is filled with a weak emission feature, indicating chromospheric activity consistent THE OBSERVED LEVEL OF CORONAL EMISSION. Interferometry does not detect a companion and sets upper limits on the companion mass of 0.2, 0.35, 0.1 and 0.05 M⊙ at a distance of 0.1-4 AU, 4-6 AU, 6-11 AU, and 11-34 AU, respectively. Our mid-IR measurements, the first of the system since 2012, are consistent with the depleted dust level seen after 2009. THE NEW DATA CONFIRMS THAT STELLAR ACTIVITY IS UNLIKELY TO DESTROY THE DUST IN THE DISK AND SHOWS THAT SCENARIOS WHERE EITHER TYC8241 2652 1 HEATS THE DISK OF A BINARY COMPANION OR A POTENTIAL COMPANION HEATS THE DISK OF TYC8241 2652 1 ARE HIGLY UNLIKELY.
Ebola virus epidemiology, transmission, and evolution during seven months in Sierra Leone
The 2013-2015 Ebola virus disease (EVD) epidemic is caused by the Makona variant of Ebola virus (EBOV). Early in the epidemic, genome sequencing provided insights into virus evolution and transmission, and offered important information for outbreak response. Here we analyze sequences from 232 patients sampled over 7 months in Sierra Leone, along with 86 previously released genomes from earlier in the epidemic. We confirm sustained human-to-human transmission within Sierra Leone and find no evidence for import or export of EBOV across national borders after its initial introduction. Using high-depth replicate sequencing, we observe both host-to-host transmission and recurrent emergence of intrahost genetic variants. We trace the increasing impact of purifying selection in suppressing the accumulation of nonsynonymous mutations over time. Finally, we note changes in the mucin-like domain of EBOV glycoprotein that merit further investigation. These findings clarify the movement of EBOV within the region and describe viral evolution during prolonged human-to-human transmission.
Rotation in massive stars:
Progenitors, Core Collapse, and Remnants.
This collaborative document has been created for the panel discussion on “Rotation in massive stars” (FOE 2015), held on Thursday 6/4/2015 in Raileigh. All conference participants have been added to the document and can edit / comment / add figures (just drag&drop) / references and even LaTeX equations if needed (check the help page for more info on how to edit the document). Hopefully this will capture the essential ideas and interactions that will stem during and after the discussion. The document can be forked at any time, so that particular discussions can be taken further and potentially lead to active collaborations.
Astropy - New software standards for a growing community
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Place signatures and how to compare them
Areas in the cities have their own profile. In this study we define a way to measure the signature of a place. We define a way to compare different areas. Finally, we apply those methods for three practical applications. First, the comparison of areas in different cities (What is the SoHo of Stockholm?). Second, the comparison of areas within the same city ("How similar is South Kensington to Richmond?"). Thrid, we use those measures to characterise the evolution of an area, by comparing different snapshots of an area
MindGames: A Crowd-Sourcing Game Platform for Brain MRI Segmentation
Rationale Advances in MRI technology and image segmentation algorithms have enabled researchers to begin to understand the mechanisms of healthy brain development and neurological disorders, such as multiple sclerosis . Due to the wide variability of brain morphology, coupled with a pathological process in the case of neurological disorders, increasingly large sample sizes are necessary to confidently answer the progressively complex biomedical questions the research community is interested in. Automated algorithms have been developed to reduce information-rich 3D MRI images to 1-dimensional summary measures that describe tissue properties and are easy to interpret, such as total gray matter volume. Automated segmentation algorithms save considerable time, compared to manual human inspection, but lack the advanced visual system of humans. As a result, these algorithms often make systematic errors, especially when analyzing brains with pathology or those in the early stages of development. Data science is poised to facilitate complex neuroscience research by fusing a crowdsourcing strategy with machine learning methods; automatic quantification can perform the bulk of the work efficiently and errors can be resolved by non-expert “citizen-scientists” with the advantage of the human visual system. Crowdsourcing has been successful in many other disciplines , including mathematics , astronomy , and biochemistry . Recently, over 200,000 “citizen-neuroscientists” from over 147 countries helped identify neuronal connections in a mouse retina through the Eyewire game . This crowdsourced game led to a new understanding of how mammalian retinal cells detect motion. I propose to implement three key features of the EyeWire paradigm and adapt them for the segmentation of MRI data. First, by breaking up the problem into smaller “micro-tasks”, Eyewire scientists were able to access a much larger user-pool of non-experts. In a similar vein, 3D MRI data can be divided into 2D slices to be segmented by users. Second, machine learning algorithms were trained to help with the task, which improved the speed of manual neuronal tracing and validated non-expert input in the Eyewire game. Deep learning methods have already shown to be successful at segmenting MRI data, and similar models could be built to support manual segmentation. Lastly, EyeWire transformed a dull, monotonous task for experts into a fun, competitive game that trained non-experts and acquired valuable scientific data. The University of Washington is an ideal place to develop a similar game platform for MRI segmentation, using the resources at the Center for Game Science, led by Zoran Popovic. I propose to create an open-source platform for efficiently crowdsourcing brain tissue classification problems in order to answer neuroscience research questions with more precision. Specific Aims 1. SCALEABLE AND SECURE MICRO-TASKS: A scaleable database system and server backend that keeps data private by dividing it into small “micro-tasks” 2. LEARNING BY EXAMPLE: Machine learning algorithm that learns from human curation to improve efficiency of manual tasks 3. TRAINING THROUGH GAMIFICATION: User interface that trains users to solve a specific problem, and keeps them engaged through a reward system
First Look at the Physics Case of TLEP
INTRODUCTION The Higgs boson with mass around 125 GeV recently discovered by the ATLAS and CMS experiments at the LHC is found to have properties compatible with the Standard Model predictions , as shown for example in Fig. [fig:ellis] . Coupled with the absence of any other indication so far for new physics at the LHC, be it either through precision measurements or via direct searches, this fundamental observation seems to push the energy scale of any physics beyond the Standard Model above several hundred GeV. The higher-energy LHC run, which is expected to start in 2015 at $ \sim 13$-14 TeV, will extend the sensitivity to new physics to 1 TeV or more. Fundamental discoveries may therefore be made in this energy range by 2017-2018. Independently of the outcome of this higher-energy run, however, there must be new phenomena, albeit at unknown energy scales, as shown by the evidence for non-baryonic dark matter, the cosmological baryon-antibaryon asymmetry and non-zero neutrino masses, which are all evidence for physics beyond the Standard Model. In addition to the high-luminosity upgrade of the LHC, new particle accelerators will be instrumental to understand the physics underlying these observations.
Evaluating Risky Individual Behavior During Epidemics Using Mobile Network Data
The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent. In this paper we propose an alternative approach that goes beyond epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. To this aim, we develop a model, named _Progmosis_, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%. While these results are promising, it is important to underline the fact that this is only an initial foundational work and to stress some key points. First, this paper focuses on a theoretical model, rather than on its actual translation into a real-world system. In particular, centralized deployments of this model would pose several ethical questions, as they would require access to user data. Decentralized deployments for which user mobility data never leaves the mobile device of a user are possible and should be preferred, as they fully protect user privacy. Second, results are generated from computer-based simulations, under specific assumptions. Social factors and technical difficulties might greatly affect results obtained in the real world. Third, this risk-assessment tool is not designed specifically for implementing containment measures based on mobility restrictions. For example, it could be used to advise users about the most appropriate behavior given his/her risk profile (e.g., willingly change own behavior, see a doctor, and similar); users would finally choose whether to follow the advice or not. Finally, the simulations were run on data call records from a country that is according to WHO Ebola-free , and this work has not been commissioned neither by Orange nor by any other entity for preparation to a real-world disease outbreak.