| 1. |
- Abraham, Mark James, 1977-, et al.
(författare)
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GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
- 2015
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Ingår i: SoftwareX. - : Elsevier. - 2352-7110. ; 1-2, s. 19-25
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Tidskriftsartikel (refereegranskat)abstract
- GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, preparation and analysis tools. Several advanced techniques for free-energy calculations are supported. In version 5, it reaches new performance heights, through several new and enhanced parallelization algorithms. These work on every level; SIMD registers inside cores, multithreading, heterogeneous CPU–GPU acceleration, state-of-the-art 3D domain decomposition, and ensemble-level parallelization through built-in replica exchange and the separate Copernicus framework. The latest best-in-class compressed trajectory storage format is supported.
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| 2. |
- Assel, Melissa, et al.
(författare)
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A Four-kallikrein Panel and β-Microseminoprotein in Predicting High-grade Prostate Cancer on Biopsy : An Independent Replication from the Finnish Section of the European Randomized Study of Screening for Prostate Cancer
- 2019
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Ingår i: European Urology Focus. - : Elsevier BV. - 2405-4569. ; 5:4, s. 561-567
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Tidskriftsartikel (refereegranskat)abstract
- Background: A panel of four kallikrein markers (total, free, and intact prostate-specific antigen [PSA] and human kallikrein-related peptidase 2 [hK2]) improves predictive accuracy for Gleason score ≥7 (high-grade) prostate cancer among men biopsied for elevated PSA. A four-kallikrein panel model was originally developed and validated by the Dutch center of the European Randomized Study of Screening for Prostate Cancer (ERSPC). The kallikrein panel is now commercially available as 4Kscore™. Objective: To assess whether these findings could be replicated among participants in the Finnish section of ERSPC (FinRSPC) and whether β-microseminoprotein (MSP), a candidate prostate cancer biomarker, adds predictive value. Design, setting, and participants: Among 4861 biopsied screening-positive participants in the first three screening rounds of FinRSPC, a case-control subset was selected that included 1632 biopsy-positive cases matched by age at biopsy to biopsy-negative controls. Outcome measurements and statistical analysis: The predictive accuracy of prespecified prediction models was compared with biopsy outcomes. Results and limitations: Among men with PSA of 4.0-25. ng/ml, 1111 had prostate cancer, 318 of whom had high-grade disease. Total PSA and age predicted high-grade cancer with an area under the curve of 0.648 (95% confidence interval [CI] 0.614-0.681) and the four-kallikrein panel increased discrimination to 0.746 (95% CI 0.717-0.774). Adding MSP to the four-kallikrein panel led to a significant (Wald test; p = 0.015) but small increase (0.003) in discrimination. Limitations include a risk of verification bias among men with PSA of 3.0-3.99. ng/ml and the absence of digital rectal examination results. Conclusions: These findings provide additional evidence that kallikrein markers can be used to inform biopsy decision-making. Further studies are needed to define the role of MSP. Patient summary: Four kallikrein markers and β-microseminoprotein in blood improve discrimination of high-grade prostate cancer at biopsy in men with elevated prostate-specific antigen. Four kallikrein markers and β-microseminoprotein (MSP) in blood improve discrimination of high-grade cancer at biopsy in men with elevated prostate-specific antigen. These kallikrein markers can be used to inform biopsy decision-making. Further studies are needed to define the role of MSP.
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| 3. |
- Wennberg, Christian L., et al.
(författare)
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Direct-Space Corrections Enable Fast and Accurate Lorentz-Berthelot Combination Rule Lennard-Jones Lattice Summation
- 2015
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Ingår i: Journal of Chemical Theory and Computation. - : American Chemical Society (ACS). - 1549-9618 .- 1549-9626. ; 11:12, s. 5737-5746
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Tidskriftsartikel (refereegranskat)abstract
- Long-range lattice summation techniques such as the particle-mesh Ewald (PME) algorithm for electrostatics have been revolutionary to the precision and accuracy of molecular simulations in general. Despite the performance penalty associated with lattice summation electrostatics, few biomolecular simulations today are performed without it. There are increasingly strong arguments for moving in the same direction for Lennard-Jones (LJ) interactions, and by using geometric approximations of the combination rules in reciprocal space, we have been able to make a very high-performance implementation available in GROMACS. Here, we present a new way to correct for these approximations to achieve exact treatment of Lorentz-Berthelot combination rules within the cutoff, and only a very small approximation error remains outside the cutoff (a part that would be completely ignored without LJ-PME). This not only improves accuracy by almost an order of magnitude but also achieves absolute biomolecular simulation performance that is an order of magnitude faster than any other available lattice summation technique for LJ interactions. The implementation includes both CPU and GPU acceleration, and its combination with improved scaling LJ-PME simulations now provides performance close to the truncated potential methods in GROMACS but with much higher accuracy.
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