Understanding the luminescent nature of organic radicals for efficient doublet emitters and pure-red light-emitting diodes.

The doublet-spin nature of radical emitters is advantageous for applications in organic light-emitting diodes, as it avoids the formation of triplet excitons that limit the electroluminescence efficiency of non-radical emitters. However, radicals generally show low optical absorption and photoluminescence yields. Here we explain the poor optical properties of radicals based on alternant hydrocarbons, and establish design rules to increase the absorption and luminescence yields for donor-acceptor-type radicals. We show that non-alternant systems are necessary to lift the degeneracy of the lowest energy orbital excitations; moreover, intensity borrowing from an intense high-lying transition by the low-energy charge-transfer excitation enhances the oscillator strength of the emitter. We apply these rules to design tris(2,4,6-trichlorophenyl)methyl-pyridoindolyl derivatives with a high photoluminescence quantum yield (>90%). Organic light-emitting diodes based on these molecules showed a pure-red emission with an over 12% external quantum efficiency. These insights may be beneficial for the rational design and discovery of highly luminescent doublet emitters

Improving the Stability and Performance of Perovskite Light-Emitting Diodes by Thermal Annealing Treatment

$\textbf{A perovskite LED with a perovskite film}$ treated under optimum thermal annealing conditions exhibits a significantly enhanced long-term stability with full coverage of the green electroluminescence emission due to the highly uniform morphology of the perovskite film.This study was supported by the Mid-Career Researcher Program (2015R1A2A2A01003263). This work was fi nancially supported by the KIST-UNIST partnership program (1.150091.01/2.150464.01). This work was also supported by the Human Resource Training Program for Regional Innovation and Creativity through the Ministry of Education and National Research Foundation of Korea (NRF-2014H1C1A1073051). We thank the EPSRC (UK) for support

Circulating β-endorphin, adrenocorticotrophic hormone and cortisol levels of stallions before and after short road transport: stress effect of different distances

<p>Abstract</p> <p>Background</p> <p>Since transport evokes physiological adjustments that include endocrine responses, the objective of this study was to examine the responses of circulating β-endorphin, adrenocorticotrophic hormone (ACTH) and cortisol levels to transport stress in stallions.</p> <p>Methods</p> <p>Forty-two healthy Thoroughbred and crossbred stallions were studied before and after road transport over distances of 100, 200 and 300 km. Blood samples were collected from the jugular vein: first in a single box immediately before loading (pre-samples), then immediately after transport and unloading on arrival at the breeding stations (post-samples).</p> <p>Results</p> <p>An increase in circulating β-endorphin levels after transport of 100 km (<it>P </it>< 0.01), compared to basal values was observed. Circulating ACTH levels showed significant increases after transport of 100 km (<it>P </it>< 0.001) and 200 km (<it>P </it>< 0.001). Circulating cortisol levels showed significant increases after road transport over distances of 100, 200 and 300 km (<it>P </it>< 0.001). An effect of transport on β-endorphin, ACTH and cortisol variations was therefore evident for the different distances studied. No significant differences (<it>P </it>> 0.05) between horses of different ages and different breeds were observed for β-endorphin, ACTH and cortisol levels.</p> <p>Conclusion</p> <p>The results obtained for short term transportation of stallions showed a very strong reaction of the adrenocortical system. The lack of response of β-endorphin after transport of 200–300 km and of ACTH after transport of 300 km seems to suggest a soothing effect of negative feedback of ACTH and cortisol levels.</p

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment

Bright light-emitting diodes based on organometal halide perovskite.

Solid-state light-emitting devices based on direct-bandgap semiconductors have, over the past two decades, been utilized as energy-efficient sources of lighting. However, fabrication of these devices typically relies on expensive high-temperature and high-vacuum processes, rendering them uneconomical for use in large-area displays. Here, we report high-brightness light-emitting diodes based on solution-processed organometal halide perovskites. We demonstrate electroluminescence in the near-infrared, green and red by tuning the halide compositions in the perovskite. In our infrared device, a thin 15 nm layer of CH3NH3PbI(3-x)Cl(x) perovskite emitter is sandwiched between larger-bandgap titanium dioxide (TiO2) and poly(9,9'-dioctylfluorene) (F8) layers, effectively confining electrons and holes in the perovskite layer for radiative recombination. We report an infrared radiance of 13.2 W sr(-1) m(-2) at a current density of 363 mA cm(-2), with highest external and internal quantum efficiencies of 0.76% and 3.4%, respectively. In our green light-emitting device with an ITO/PEDOT:PSS/CH3NH3PbBr3/F8/Ca/Ag structure, we achieved a luminance of 364 cd m(-2) at a current density of 123 mA cm(-2), giving external and internal quantum efficiencies of 0.1% and 0.4%, respectively. We show, using photoluminescence studies, that radiative bimolecular recombination is dominant at higher excitation densities. Hence, the quantum efficiencies of the perovskite light-emitting diodes increase at higher current densities. This demonstration of effective perovskite electroluminescence offers scope for developing this unique class of materials into efficient and colour-tunable light emitters for low-cost display, lighting and optical communication applications.This is the author accepted manuscript and will be under embargo until 3/2/15. The final version is published in Nature Nanotechnology: http://www.nature.com/nnano/journal/vaop/ncurrent/full/nnano.2014.149.html

A Long Baseline Neutrino Oscillation Experiment Using J-PARC Neutrino Beam and Hyper-Kamiokande

Document submitted to 18th J-PARC PAC meeting in May 2014. 50 pages, 41 figuresDocument submitted to 18th J-PARC PAC meeting in May 2014. 50 pages, 41 figuresDocument submitted to 18th J-PARC PAC meeting in May 2014. 50 pages, 41 figuresHyper-Kamiokande will be a next generation underground water Cherenkov detector with a total (fiducial) mass of 0.99 (0.56) million metric tons, approximately 20 (25) times larger than that of Super-Kamiokande. One of the main goals of Hyper-Kamiokande is the study of $CP$ asymmetry in the lepton sector using accelerator neutrino and anti-neutrino beams. In this document, the physics potential of a long baseline neutrino experiment using the Hyper-Kamiokande detector and a neutrino beam from the J-PARC proton synchrotron is presented. The analysis has been updated from the previous Letter of Intent [K. Abe et al., arXiv:1109.3262 [hep-ex]], based on the experience gained from the ongoing T2K experiment. With a total exposure of 7.5 MW $\times$ 10$^7$ sec integrated proton beam power (corresponding to $1.56\times10^{22}$ protons on target with a 30 GeV proton beam) to a $2.5$-degree off-axis neutrino beam produced by the J-PARC proton synchrotron, it is expected that the $CP$ phase $\delta_{CP}$ can be determined to better than 19 degrees for all possible values of $\delta_{CP}$, and $CP$ violation can be established with a statistical significance of more than $3\,\sigma$ ($5\,\sigma$) for $76$ ($58$) of the $\delta_{CP}$ parameter space

Measurements of neutrino oscillation in appearance and disappearance channels by the T2K experiment with 6.6 x 10(20) protons on target

111 pages, 45 figures, submitted to Physical Review D. Minor revisions to text following referee comments111 pages, 45 figures, submitted to Physical Review D. Minor revisions to text following referee comments111 pages, 45 figures, submitted to Physical Review D. Minor revisions to text following referee commentsWe thank the J-PARC staff for superb accelerator performance and the CERN NA61/SHINE Collaboration for providing valuable particle production data. We acknowledge the support of MEXT, Japan; NSERC, NRC, and CFI, Canada; CEA and CNRS/IN2P3, France; DFG, Germany; INFN, Italy; National Science Centre (NCN), Poland; RSF, RFBR and MES, Russia; MINECO and ERDF funds, Spain; SNSF and SER, Switzerland; STFC, UK; and the U. S. Deparment of Energy, USA. We also thank CERN for the UA1/NOMAD magnet, DESY for the HERA-B magnet mover system, NII for SINET4, the WestGrid and SciNet consortia in Compute Canada, GridPP, UK, and the Emerald High Performance Computing facility in the Centre for Innovation, UK. In addition, participation of individual researchers and institutions has been further supported by funds from ERC (FP7), EU; JSPS, Japan; Royal Society, UK; and DOE Early Career program, USA

Measurement of the electron neutrino charged-current interaction rate on water with the T2K ND280 pi(0) detector

10 pages, 6 figures, Submitted to PRDhttp://journals.aps.org/prd/abstract/10.1103/PhysRevD.91.112010© 2015 American Physical Society11 pages, 6 figures, as accepted to PRD11 pages, 6 figures, as accepted to PRD11 pages, 6 figures, as accepted to PR

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation