Durham University

About: Durham University is a education organization based out in Durham, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 39385 authors who have published 82311 publications receiving 3110994 citations. The organization is also known as: University of Durham & Gallery of Durham University.

Project-based learning: A review of the literature:

Abstract: Project-based learning (PBL) is an active student-centred form of instruction which is characterised by students’ autonomy, constructive investigations, goal-setting, collaboration, communication a...

Discovery of very-high-energy |[gamma]|-rays from the Galactic Centre ridge

Abstract: Events at the centre of our Galaxy are key to our understanding of high-energy processes in the Universe, since it contains examples of virtually every type of exotic object known to astronomers. The very-high-energy γ-ray emission from the Galactic Centre region has now been measured using HESS, the High Energy Stereoscopic System recently constructed in Namibia, South West Africa. HESS operates at energies above the regime accessible to satellite-based detectors, taking γ-ray astronomy into new territory. The results show that these clouds are glowing in very high energy γ-rays. The glow is caused by constant bombardment of the clouds by cosmic rays — probably protons and nuclei — produced close to the central black hole or in the expanding blast waves of supernova explosions. The source of Galactic cosmic rays (with energies up to 1015 eV) remains unclear, although it is widely believed that they originate in the shock waves of expanding supernova remnants1,2. At present the best way to investigate their acceleration and propagation is by observing the γ-rays produced when cosmic rays interact with interstellar gas3. Here we report observations of an extended region of very-high-energy (> 1011 eV) γ-ray emission correlated spatially with a complex of giant molecular clouds in the central 200 parsecs of the Milky Way. The hardness of the γ-ray spectrum and the conditions in those molecular clouds indicate that the cosmic rays giving rise to the γ-rays are likely to be protons and nuclei rather than electrons. The energy associated with the cosmic rays could have come from a single supernova explosion around 104 years ago.

Nivolumab plus ipilimumab versus sunitinib in first-line treatment for advanced renal cell carcinoma: extended follow-up of efficacy and safety results from a randomised, controlled, phase 3 trial

Abstract: Summary Background In the ongoing phase 3 CheckMate 214 trial, nivolumab plus ipilimumab showed superior efficacy over sunitinib in patients with previously untreated intermediate-risk or poor-risk advanced renal cell carcinoma, with a manageable safety profile. In this study, we aimed to assess efficacy and safety after extended follow-up to inform the long-term clinical benefit of nivolumab plus ipilimumab versus sunitinib in this setting. Methods In the phase 3, randomised, controlled CheckMate 214 trial, patients aged 18 years and older with previously untreated, advanced, or metastatic histologically confirmed renal cell carcinoma with a clear-cell component were recruited from 175 hospitals and cancer centres in 28 countries. Patients were categorised by International Metastatic Renal Cell Carcinoma Database Consortium risk status into favourable-risk, intermediate-risk, and poor-risk subgroups and randomly assigned (1:1) to open-label nivolumab (3 mg/kg intravenously) plus ipilimumab (1 mg/kg intravenously) every 3 weeks for four doses, followed by nivolumab (3 mg/kg intravenously) every 2 weeks; or sunitinib (50 mg orally) once daily for 4 weeks (6-week cycle). Randomisation was done through an interactive voice response system, with a block size of four and stratified by risk status and geographical region. The co-primary endpoints for the trial were overall survival, progression-free survival per independent radiology review committee (IRRC), and objective responses per IRRC in intermediate-risk or poor-risk patients. Secondary endpoints were overall survival, progression-free survival per IRRC, and objective responses per IRRC in the intention-to-treat population, and adverse events in all treated patients. In this Article, we report overall survival, investigator-assessed progression-free survival, investigator-assessed objective response, characterisation of response, and safety after extended follow-up. Efficacy outcomes were assessed in all randomly assigned patients; safety was assessed in all treated patients. This study is registered with ClinicalTrials.gov, number NCT02231749, and is ongoing but now closed to recruitment. Findings Between Oct 16, 2014, and Feb 23, 2016, of 1390 patients screened, 1096 (79%) eligible patients were randomly assigned to nivolumab plus ipilimumab or sunitinib (550 vs 546 in the intention-to-treat population; 425 vs 422 intermediate-risk or poor-risk patients, and 125 vs 124 favourable-risk patients). With extended follow-up (median follow-up 32·4 months [IQR 13·4–36·3]), in intermediate-risk or poor-risk patients, results for the three co-primary efficacy endpoints showed that nivolumab plus ipilimumab continued to be superior to sunitinib in terms of overall survival (median not reached [95% CI 35·6–not estimable] vs 26·6 months [22·1–33·4]; hazard ratio [HR] 0·66 [95% CI 0·54–0·80], p Interpretation The results suggest that the superior efficacy of nivolumab plus ipilimumab over sunitinib was maintained in intermediate-risk or poor-risk and intention-to-treat patients with extended follow-up, and show the long-term benefits of nivolumab plus ipilimumab in patients with previously untreated advanced renal cell carcinoma across all risk categories. Funding Bristol-Myers Squibb and ONO Pharmaceutical.

Deep learning networks for stock market analysis and prediction

Abstract: Deep learning networks are applied to stock market analysis and prediction.A comprehensive analysis with different data representation methods is offered.Five-minute intraday data from the Korean KOSPI stock market is used.The network applied to residuals of autoregressive model improves prediction.Covariance estimation for market structure analysis is improved with the network. We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methodsprincipal component analysis, autoencoder, and the restricted Boltzmann machineon the networks overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.

Pressure evolution of the low-temperature crystal structure and bonding of the superconductor FeSe (Tc =37 K)

Abstract: $\ensuremath{\alpha}\text{-FeSe}$ with the PbO structure is a key member of the family of high-${T}_{c}$ iron pnictide and chalcogenide superconductors, as while it possesses the basic layered structural motif of edge-sharing distorted ${\text{FeSe}}_{4}$ tetrahedra, it lacks interleaved ion spacers or charge-reservoir layers. We find that the application of hydrostatic pressure first rapidly increases ${T}_{c}$ which attains a broad maximum of 37 K at $\ensuremath{\sim}7\text{ }\text{GPa}$ before decreasing to 6 K upon further compression to $\ensuremath{\sim}14\text{ }\text{GPa}$. Complementary synchrotron x-ray diffraction at 16 K was used to measure the low-temperature isothermal compressibility of $\ensuremath{\alpha}\text{-FeSe}$, revealing an extremely soft solid with a bulk modulus, ${K}_{0}=30.7(1.1)\text{ }\text{GPa}$ and strong bonding anisotropy between interlayer and intralayer directions that transforms to the more densely packed $\ensuremath{\beta}$ polymorph above $\ensuremath{\sim}9\text{ }\text{GPa}$. The nonmonotonic ${T}_{c}(P)$ behavior of FeSe coincides with drastic anomalies in the pressure evolution of the interlayer spacing, pointing to the key role of this structural feature in modulating the electronic properties.