Multiscale and multimodel simulation of Bloch point dynamics

We present simulation results on the structure and dynamics of micromagnetic point singularities with atomistic resolution. This is achieved by embedding an atomistic computational region into a standard micromagnetic algorithm. Several length scales are bridged by means of an adaptive mesh refinement and a seamless coupling between the continuum theory and a Heisenberg formulation for the atomistic region. The code operates on graphical processing units and is able to detect and track the position of strongly inhomogeneous magnetic regions. This enables us to reliably simulate the dynamics of Bloch points, which means that a fundamental class of micromagnetic switching processes can be analyzed with unprecedented accuracy. We test the code by comparing it with established results and present its functionality with the example of a simulated field-driven Bloch point motion in a soft-magnetic cylinder

A Solution for Multi-Alignment by Transformation Synchronisation

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations can be retrieved from the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are transitively consistent. Simulations demonstrate that for noisy transformations, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results.Comment: Accepted for CVPR 2015 (please cite CVPR version

Numerical micromagnetism of strong inhomogeneities

The size of micromagnetic structures, such as domain walls or vortices, is comparable to the exchange length of the ferromagnet. Both, the exchange length of the stray field $l_s$ and the magnetocrystalline exchange length $l_k$ are material-dependent quantities that usually lie in the nanometer range. This emphasizes the theoretical challenges associated with the mesoscopic nature of micromagnetism: the magnetic structures are much larger than the atomic lattice constant, but at the same time much smaller than the sample size. In computer simulations, the smallest exchange length serves as an estimate for the largest cell size admissible to prevent appreciable discretization errors. This general rule is not valid in special situations where the magnetization becomes particularly inhomogeneous. When such strongly inhomogeneous structures develop, micromagnetic simulations inevitably contain systematic and numerical errors. It is suggested to combine micromagnetic theory with a Heisenberg model to resolve such problems. We analyze cases where strongly inhomogeneous structures pose limits to standard micromagnetic simulations, arising from fundamental aspects as well as from numerical drawbacks

The magnetoelectrochemical switch

In the field of spintronics, the archetype solid-state two-terminal device is the spin valve, where the resistance is controlled by the magnetization configuration. We show here how this concept of spin-dependent switch can be extended to magnetic electrodes in solution, by magnetic control of their chemical environment. Appropriate nanoscale design allows a huge enhancement of the magnetic force field experienced by paramagnetic molecular species in solutions, which changes between repulsive and attractive on changing the electrodes' magnetic orientations. Specifically, the field gradient force created within a sub-100-nm-sized nanogap separating two magnetic electrodes can be reversed by changing the orientation of the electrodes' magnetization relative to the current flowing between the electrodes. This can result in a breaking or making of an electric nanocontact, with a change of resistance by a factor of up to 103. The results reveal how an external field can impact chemical equilibrium in the vicinity of nanoscale magnetic circuits

The role of familial conflict in home range settlement and fitness of a solitary mammal

Familial conflict, including parent–offspring conflict (POC) and sibling competition (SC), occurs when an individual maximizes its access to a limiting resource at the expense of a related individual. The role of familial conflict for competition over space as a limited resource remains relatively unexplored. In this study, we examined how familial conflict affects natal dispersal and settlement decisions of a solitary mammal, the brown bear, Ursus arctos, and tested whether these settlement patterns covary with fitness. First, we tested whether the distance settled from the natal range was affected by aspects of POC (litter type: single versus multiple; mother's age; mother's living status) and SC (settled near versus far from the natal home range, body size). We then modelled how distance settled from the natal range influenced three measures of fitness: survival to reproduction, lifetime reproductive success and lifetime survival. In line with POC, we found that daughters settled twice as far from the natal range when their mother was alive than when she was dead. We found strong evidence for SC where in sibling pairs, the ‘near’ sister settled nearly three times closer to the natal range than her sibling. We found contradictory patterns in fitness outcomes based on settlement distance, such that females settling closer to the natal range had higher lifetime survival but were less likely to successfully wean at least one offspring. Despite survival advantages gained by settling closer to the natal range, there was no evidence that settlement distance influenced lifetime reproductive success. Fitness outcomes in this population may be influenced more by factors related to annual hunting than by familial conflict or proximity to the natal range

The role of familial conflict in home range settlement and fitness of a solitary mammal

Journal of evolutionary biology Blackwell/wileyFamilial conflict, including parenteoffspring conflict (POC) and sibling competition (SC), occurs when an individual maximizes its access to a limiting resource at the expense of a related individual. The role of familial conflict for competition over space as a limited resource remains relatively unexplored. In this study, we examined how familial conflict affects natal dispersal and settlement decisions of a solitary mammal, the brown bear, Ursus arctos, and tested whether these settlement patterns covary with fitness. First, we tested whether the distance settled from the natal range was affected by aspects of POC (litter type: single versus multiple; mother's age; mother's living status) and SC (settled near versus far from the natal home range, body size). We then modelled how distance settled from the natal range influenced three measures of fitness: survival to reproduction, lifetime reproductive success and lifetime survival. In line with POC, we found that daughters settled twice as far from the natal range when their mother was alive than when she was dead. We found strong evidence for SC where in sibling pairs, the ‘near’ sister settled nearly three times closer to the natal range than her sibling. We found contradictory patterns in fitness outcomes based on settlement distance, such that females settling closer to the natal range had higher lifetime survival but were less likely to successfully wean at least one offspring. Despite survival advantages gained by settling closer to the natal range, there was no evidence that settlement distance influenced lifetime reproductive success. Fitness outcomes in this population may be influenced more by factors related to annual hunting than by familial conflict or proximity to the natal range. dispersal fitness parenteoffspring conflict reproductive success sibling competitionpublishedVersio

Adherence to a procalcitonin-guided antibiotic treatment protocol in patients with severe sepsis and septic shock

Background: In randomised controlled trials, procalcitonin (PCT)-guided antibiotic treatment has been proven to significantly reduce length of antibiotic therapy in intensive care unit (ICU) patients. However, concern was raised on low protocol adherence and high rates of overruling, and thus the value of PCT-guided treatment in real clinical life outside study conditions remains unclear. In this study, adherence to a PCT protocol to guide antibiotic treatment in patients with severe sepsis and septic shock was analysed. Methods: From 2012 to 2014, surgical ICU patients with severe sepsis or septic shock were retrospectively screened for PCT measurement series appropriate to make treatment decisions on antibiotic therapy. We compared (1) patients with appropriate PCT measurement series to patients without appropriate series; (2) patients who reached the antibiotic stopping advice threshold (PCT < 0.5 ng/mL and/or decrease to 10% of peak level) to patients who did not reach a stopping advice threshold; and (3) patients who were treated adherently to the PCT protocol to non-adherently treated patients. The groups were compared in terms of antibiotic treatment duration, PCT kinetics, and other clinical outcomes. Results: Of 81 patients with severe sepsis or septic shock, 14 were excluded due to treatment restriction or short course in the ICU. The final analysis was performed on 67 patients. Forty-two patients (62.7%) had appropriate PCT measurement series. In patients with appropriate PCT series, median initial PCT (p = 0.001) and peak PCT levels (p < 0.001) were significantly higher compared to those with non-appropriate series. In 26 patients with appropriate series, PCT levels reached an antibiotic stopping advice. In 8 of 26 patients with stopping advice, antibiotics were discontinued adherently to the PCT protocol (30.8%). Patients with adherently discontinued antibiotics had a shorter antibiotic treatment (7d [IQR 6–9] vs. 12d [IQR 9–16]; p = 0.002). No differences were seen in terms of other clinical outcomes. Conclusion: In patients with severe sepsis and septic shock, procalcitonin testing was irregular and adherence to a local PCT protocol was low in real clinical life. However, adherently treated patients had a shorter duration of antibiotic treatment without negative clinical outcomes. Procalcitonin peak values and kinetics had a clear impact on the regularity of PCT testing

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

Parkinson’s disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease’s structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task- specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discusse

The effect of dataset confounding on predictions of deep neural networks for medical imaging

The use of Convolutional Neural Networks (CNN) in medical imaging has often outperformed previous solutions and even specialists, becoming a promising technology for Computer-aided-Diagnosis (CAD) systems. However, recent works suggested that CNN may have poor generalisation on new data, for instance, generated in different hospitals. Uncontrolled confounders have been proposed as a common reason. In this paper, we experimentally demonstrate the impact of confounding data in unknown scenarios. We assessed the effect of four confounding configurations: total, strong, light and balanced. We found the confounding effect is especially prominent in total confounder scenarios, while the effect on light and strong confounding scenarios may depend on the dataset robustness. Our findings indicate that the confounding effect is independent of the architecture employed. These findings might explain why models can report good metrics during the development stage but fail to translate to real-world settings. We highlight the need for thorough consideration of these commonly unattended aspects, to develop safer CNN-based CAD systems