Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...
The magnetic properties of β-Mn and the effect of the substitution of non-magnetic Al were systematically investigated by measuring thermodynamic and transport properties, and the NMR and polarized neutron scattering for β-Mn 1−x Al x alloys with 0 x 0.4. β-Mn is paramagnetic down to the lowest temperature (1.4 K) but with strong spin fluctuations. Substitution of Al atoms which preferentially occupy one of two crystallographic sites (site II) results in the formation of local moments on Mn atoms, and makes the ground state into a spin-glass-like state. NMR measurements revealed almost independent magnetic behaviour of the two different sites, and static magnetic ordering of Mn at site II for x 0.05, although the possibility of very weak magnetism of the site-I atoms cannot be excluded. The temperature dependence of the spin-lattice relaxation rate 1/T 1 indicates antiferromagnetic correlations of paramagnetic spin fluctuations, and its saturation at higher temperatures for β-Mn 0.97 Al 0.03 and a critical slowing down of the fluctuations at low temperatures for β-Mn 0.9 Al 0.1. Polarized neutron scattering experiments showed directly strong spin fluctuations with antiferromagnetic correlations for both β-Mn and β-Mn 0.9 Al 0.1. The characteristic energy of fluctuations for β-Mn is fairly large even at 7 K (2 = 40 ± 10 meV), implying a quantum origin of the fluctuations, while the energy spectrum of β-Mn 0.9 Al 0.1 becomes very sharp at low temperatures, within a resolution-limited energy width, indicating damping of spin fluctuations into a spin-glass-like state. These results can be interpreted in terms of the transition from a spin liquid to a spin glass caused by the substitution of Al that gives rise to the release of the antiferromagnetic frustration of the characteristic crystal lattice. Considerable similarity of the characteristic features to those of the highly frustrated Laves phase compounds Y(Sc)(Mn 1−x Al x) 2 is argued. The possible frustration on site II in the β-Mn structure, a three-dimensional network of corner-sharing regular triangles, which is similar to the two-dimensional Kagomé lattice, is pointed out.
Advances in scanning transmission electron microscopy (STEM) techniques have enabled us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectral datasets from a specified region of interest (ROI) at an arbitrary step width, called spectral imaging (SI). Instead of manually identifying the potential constituent chemical components from the ROI and determining the chemical state of each spectral component from the SI data stored in a huge three-dimensional matrix, it is more effective and efficient to use a statistical approach for the automatic resolution and extraction of the underlying chemical components. Among many different statistical approaches, we adopt a non-negative matrix factorization (NMF) technique, mainly because of the natural assumption of non-negative values in the spectra and cardinalities of chemical components, which are always positive in actual data. This paper proposes a new NMF model with two penalty terms: (i) an automatic relevance determination (ARD) prior, which optimizes the number of components, and (ii) a soft orthogonal constraint, which clearly resolves each spectrum component. For the factorization, we further propose a fast optimization algorithm based on hierarchical alternating least-squares. Numerical experiments using both phantom and real STEM-EDX/EELS SI datasets demonstrate that the ARD prior successfully identifies the correct number of physically meaningful components. The soft orthogonal constraint is also shown to be effective, particularly for STEM-EELS SI data, where neither the spatial nor spectral entries in the matrices are sparse.
The structure of glassy, liquid, and amorphous materials is still not well understood, due to the insufficient structural information from diffraction data. In this article, attempts are made to understand the origin of diffraction peaks, particularly of the first sharp diffraction peak (FSDP, Q 1), the principal peak (PP, Q 2), and the third peak (Q 3), observed in the measured diffraction patterns of disordered materials whose structure contains tetrahedral motifs. It is confirmed that the FSDP (Q 1) is not a signature of the formation of a network, because an FSDP is observed in tetrahedral molecular liquids. It is found that the PP (Q 2) reflects orientational correlations of tetrahedra. Q 3 , that can be observed in all disordered materials, even in common liquid metals, stems from simple pair correlations. Moreover, information on the topology of disordered materials was revealed by utilizing persistent homology analyses. The persistence diagram of silica (SiO 2) glass suggests that the shape of rings in the glass is similar not only to those in the crystalline phase with comparable density (¡-cristobalite), but also to rings present in crystalline phases with higher density (¡-quartz and coesite); this is thought to be the signature of disorder. Furthermore, we have succeeded in revealing the differences, in terms of persistent homology, between tetrahedral networks and tetrahedral molecular liquids, and the difference/similarity between liquid and amorphous (glassy) states. Our series of analyses demonstrated that a combination of diffraction data and persistent homology analyses is a useful tool for allowing us to uncover structural features hidden in halo pattern of disordered materials.
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