To investigate the impact of a magnetic field on plaque development in a stenotic bifurcated artery, a finite element method is utilized. The blood flow is modelled as a stable, incompressible, Newtonian, biomagnetic, and laminar fluid. Furthermore, the arterial wall is assumed to be linear elastic. The Arbitrary Lagrangian Eulerian (ALE) method is employed to describe the hemodynamic flow in a bifurcated artery under the influence of an asymmetric magnetic field, taking into account two-way fluid-structure interaction coupling. A stable $P_2P_1$ finite element pair discretizes a nonlinear system of partial differential equations that requires a solution. The Newton-Raphson method is utilized to find a solution to the resulting nonlinear algebraic equation system. Numerical modelling is used to simulate the presence of magnetic fields, and the resulting displacement, velocity magnitude, pressure, and wall shear stresses are shown for a range of Reynolds numbers ($Re = 500$, $1000$, $1500$, and $2000$). The results of the numerical analysis demonstrate that the presence of a magnetic field has a significant effect not only on the magnitude of displacement but also on the velocity of the flow. The application of a magnetic field reduces flow separation, extends the recirculation area near the stenosis, and increases wall shear stress.

The dynamic interplay of signaling networks in most major cellular processes is characterized by the orchestration of reversible protein phosphorylation. Consequently, analytic methods like quantitative phospho-peptidomics has been pushed forward from a highly specialized edge-technique to a powerful and versatile platform for comprehensively analyzing the phosphorylation profile of living organisms. Despite enormous progress in instrumentation and bioinformatics, a major problem remains a high number of missing values caused by the experimental procedure due to either a random phospho-peptide enrichment selectivity or borderline signal intensities, which both cause the exclusion for fragmentation using the commonly applied data dependent acquisition (DDA) mode. Consequently, an incomplete dataset reduces confidence in the subsequent statistical bioinformatic processing. Here, we successfully applied data independent acquisition (DIA) by using the filamentous fungus Magnaporthe oryzae as model organism and could prove that while maintaining data quality (such as phosphosite and peptide sequence confidence), the data completeness increases dramatically. Since the method presented here reduces the LC-MS/MS analysis from 3 h to 1 h and increases the number of phoshosites identified up to 10-fold in contrast to published studies in fungi, we pushed the phospho-proteomic technique beyond its current limits and could provide a sophisticated resource for investigation of signaling processes in filamentous fungi.