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A transformation called normalized gain (ngain) has been acknowledged as one of the most common measures of knowledge growth in pretest-posttest contexts in physics education research. Recent studies in math education have shown that ngains can also be applied to assess learners' ability to acquire unfamiliar knowledge, that is, to estimate their "learning rate". This quantity is estimated from learning data through two well-known methods: computing the average ngain of the group or computing the ngain of the average learner. These two methods commonly yield different results, and prior research has concluded that the difference between them is associated with a pretest-ngains correlation. Such a correlation would suggest a bias of this learning measurement because it implies its favoring of certain subgroups of students according to their performance in pretest measurements. The present study analyzes these two estimation methods by drawing on statistical models. Our results show that the two estimation methods are equivalent when no measurement errors exist. In contrast, when there are measurement errors, the first method provides a biased estimator, whereas the second one provides an unbiased estimator. Furthermore, these measurement errors induce a spurious correlation between the pretest and ngain scores. Our results seem consistent with prior research, except they show that measurement errors in pretest and posttest scores are the source of a spurious pretest-ngain correlation. Consequently, estimating learning rates might effectively provide unbiased estimates of knowledge change that control for the effect of prior knowledge even in the presence of pretest-ngain correlations.

Systematic in vivo validations of computational models of the aortic valve remain scarce, despite successful validation against in vitro data. Utilizing a combination of computed tomography and 4D flow magnetic resonance imaging data, we developed patient-specific fluid-structure interaction models of the aortic valve immersed in the aorta for five patients in the pre-transcatheter aortic valve replacement configuration. Incorporating also an in vitro setup of the valve, our computational models are subjected to rigorous validation against 4D flow measurements. Our results demonstrate the models' capacity to accurately replicate flow dynamics within established ranges of uncertainties mainly arising from 4D flow noise. In addition, we illustrate how computational models can serve as valuable cross-checks to reduce noise and erratic behaviour of in vivo data. This study represents a significant step towards integrating in silico technologies into real clinical contexts, providing a robust framework for improving aortic stenosis diagnosis and the design of next-generation aortic valve bioprostheses.

The main computing tasks of a finite element code(FE) for solving partial differential equations (PDE's) are the algebraic system assembly and the iterative solver. This work focuses on the first task, in the context of a hybrid MPI+X paradigm. Although we will describe algorithms in the FE context, a similar strategy can be straightforwardly applied to other discretization methods, like the finite volume method. The matrix assembly consists of a loop over the elements of the MPI partition to compute element matrices and right-hand sides and their assemblies in the local system to each MPI partition. In a MPI+X hybrid parallelism context, X has consisted traditionally of loop parallelism using OpenMP. Several strategies have been proposed in the literature to implement this loop parallelism, like coloring or substructuring techniques to circumvent the race condition that appears when assembling the element system into the local system. The main drawback of the first technique is the decrease of the IPC due to bad spatial locality. The second technique avoids this issue but requires extensive changes in the implementation, which can be cumbersome when several element loops should be treated. We propose an alternative, based on the task parallelism of the element loop using some extensions to the OpenMP programming model. The taskification of the assembly solves both aforementioned problems. In addition, dynamic load balance will be applied using the DLB library, especially efficient in the presence of hybrid meshes, where the relative costs of the different elements is impossible to estimate a priori. This paper presents the proposed methodology, its implementation and its validation through the solution of large computational mechanics problems up to 16k cores.

Knots in proteins have been proposed to resist proteasomal degradation. Ample evidence associates proteasomal degradation with neurodegeneration. One interesting possibility is that indeed knotted conformers stall this machinery leading to toxicity. However, although the proteasome is known to unfold mechanically its substrates, at present there are no experimental methods to emulate this particular traction geometry. Here, we consider several dynamical models of the proteasome in which the complex is represented by an effective potential with an added pulling force. This force is meant to induce translocation of a protein or a polypeptide into the catalytic chamber. The force is either constant or applied periodically. The translocated proteins are modelled in a coarse-grained fashion. We do comparative analysis of several knotted globular proteins and the transiently knotted polyglutamine tracts of length 60 alone and fused in exon 1 of the huntingtin protein. Huntingtin is associated with Huntington disease, a well-known genetically-determined neurodegenerative disease. We show that the presence of a knot hinders and sometimes even jams translocation. We demonstrate that the probability to do so depends on the protein, the model of the proteasome, the magnitude of the pulling force, and the choice of the pulled terminus. In any case, the net effect would be a hindrance in the proteasomal degradation process in the cell. This would then yield toxicity \textit{via} two different mechanisms: one through toxic monomers compromising degradation and another by the formation of toxic oligomers. Our work paves the way to the mechanistic investigation of the mechanical unfolding of knotted structures by the proteasome and its relation to toxicity and disease., Comment: 13 pages, 9 figures

Alya is the BSC in-house HPC-based multi-physics simulation code. It is designed from scratch to run efficiently in parallel supercomputers, solving coupled problems. The target domain is engineering, with all its particular features: complex geome- tries and unstructured meshes, coupled multi-physics with exotic coupling schemes and Physical models, ill-posed problems, flexibility needs for rapidly including new models, etc. Since its conception in 2004, Alya has shown scaling behaviour in an increasing number of cores. In this paper, we present its performance up to 100.000 cores in Blue Waters, the NCSA supercomputer. The selected tests are representative of the engineering world, all the problematic features included: incompressible flow in a hu- man respiratory system, low Mach combustion problem in a kiln furnace and coupled electro-mechanical problem in a heart. We show scalability plots for all cases, discussing all the aspects of such kind of simulations, including solvers convergence., Comment: Preprint. Paper submitted to International Supercomputing Conference 2014. Waiting for approval