239 research outputs found
Gamma-ray and radio tests of the e+e- excess from DM annihilations
PAMELA and ATIC recently reported an excess in e+e- cosmic rays. We show that
if it is due to Dark Matter annihilations, the associated gamma-ray flux and
the synchrotron emission produced by e+e- in the galactic magnetic field
violate HESS and radio observations of the galactic center and HESS
observations of dwarf Spheroidals, unless the DM density profile is
significantly less steep than the benchmark NFW and Einasto profiles.Comment: 16 pages, 4 figures; v2: normalizations fixed in Table 2 and typos
corrected (no changes in the analysis nor the results), some references and
comments added; v3: minor additions, matches published versio
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An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors
Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers
Studying Gaugino Mass Unification at the LHC
We begin a systematic study of how gaugino mass unification can be probed at
the CERN Large Hadron Collider (LHC) in a quasi-model independent manner. As a
first step in that direction we focus our attention on the theoretically
well-motivated mirage pattern of gaugino masses, a one-parameter family of
models of which universal (high scale) gaugino masses are a limiting case. We
improve on previous methods to define an analytic expression for the metric on
signature space and use it to study one-parameter deviations from universality
in the gaugino sector, randomizing over other soft supersymmetry-breaking
parameters. We put forward three ensembles of observables targeted at the
physics of the gaugino sector, allowing for a determination of this
non-universality parameter without reconstructing individual mass eigenvalues
or the soft supersymmetry-breaking gaugino masses themselves. In this
controlled environment we find that approximately 80% of the supersymmetric
parameter space would give rise to a model for which our method will detect
non-universality in the gaugino mass sector at the 10% level with an integrated
luminosity of order 10 inverse femptobarns. We discuss strategies for improving
the method and for adding more realism in dealing with the actual experimental
circumstances of the LHC
Evaluation of guided imagery as treatment for recurrent abdominal pain in children: a randomized controlled trial
BACKGROUND: Because of the paucity of effective evidence-based therapies for children with recurrent abdominal pain, we evaluated the therapeutic effect of guided imagery, a well-studied self-regulation technique. METHODS: 22 children, aged 5 – 18 years, were randomized to learn either breathing exercises alone or guided imagery with progressive muscle relaxation. Both groups had 4-weekly sessions with a therapist. Children reported the numbers of days with pain, the pain intensity, and missed activities due to abdominal pain using a daily pain diary collected at baseline and during the intervention. Monthly phone calls to the children reported the number of days with pain and the number of days of missed activities experienced during the month of and month following the intervention. Children with ≤ 4 days of pain/month and no missed activities due to pain were defined as being healed. Depression, anxiety, and somatization were measured in both children and parents at baseline. RESULTS: At baseline the children who received guided imagery had more days of pain during the preceding month (23 vs. 14 days, P = 0.04). There were no differences in the intensity of painful episodes or any baseline psychological factors between the two groups. Children who learned guided imagery with progressive muscle relaxation had significantly greater decrease in the number of days with pain than those learning breathing exercises alone after one (67% vs. 21%, P = 0.05), and two (82% vs. 45%, P < 0.01) months and significantly greater decrease in days with missed activities at one (85% vs. 15%, P = 0.02) and two (95% vs. 77%. P = 0.05) months. During the two months of follow-up, more children who had learned guided imagery met the threshold of ≤ 4 day of pain each month and no missed activities (RR = 7.3, 95%CI [1.1,48.6]) than children who learned only the breathing exercises. CONCLUSION: The therapeutic efficacy of guided imagery with progressive muscle relaxation found in this study is consistent with our present understanding of the pathophysiology of recurrent abdominal pain in children. Although unfamiliar to many pediatricians, guided imagery is a simple, noninvasive therapy with potential benefit for treating children with RAP
Social media and sensemaking patterns in new product development: demystifying the customer sentiment
Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms
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