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Phospholipids fulfill an important role in joint lubrication. They, together with hyaluronan and glycoproteins, are the biolubricants that sustain low friction between cartilage surfaces bathed in synovial fluid. In this work we have investigated how the friction force and load bearing capacity of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) bilayers on silica surfaces are affected by temperature, covering the temperature range 25–52°C. Friction forces have been determined utilizing the AFM colloidal probe technique, which showed that DPPC bilayers are able to provide low friction forces over the whole temperature interval. However, the load bearing capacity is improved at higher temperatures. We interpret this finding as being a consequence of lower rigidity and higher self-healing capacity of the DPPC bilayer in the liquid disordered state compared to the gel state. The corresponding structure of solid supported DPPC bilayers at the silica–liquid interface has been followed using X-ray reflectivity measurements, which suggests that the DPPC bilayer is in the gel phase at 25°C and 39°C and in the liquid disordered state at 55°C. Well-defined bilayer structures were observed for both phases. The deposited DPPC bilayers were also imaged using AFM PeakForce Tapping mode, and these measurements indicated a less homogeneous layer at temperatures below 37°C.

Due to its ability to represent intricate systems with material nonlinearities as well as irregular loading, boundary, geometrical and material domains, the finite element (FE) method has been recognized as an important computational tool in spinal biomechanics. Current FE models generally account for a single distinct spinal geometry with one set of material properties despite inherently large inter-subject variability. The uncertainty and high variability in tissue material properties, geometry, loading and boundary conditions has cast doubt on the reliability of their predictions and comparability with reported in vitro and in vivo values. A multicenter study was undertaken to compare the results of eight well-established models of the lumbar spine that have been developed, validated and applied for many years. Models were subjected to pure and combined loading modes and their predictions were compared to in vitro and in vivo measurements for intervertebral rotations, disc pressures and facet joint forces. Under pure moment loading, the predicted L1-5 rotations of almost all models fell within the reported in vitro ranges; their median values differed on average by only 2° for flexion-extension, 1° for lateral bending and 5° for axial rotation. Predicted median facet joint forces and disc pressures were also in good agreement with previously published median in vitro values. However, the ranges of predictions were larger and exceeded the in vitro ranges, especially for facet joint forces. For all combined loading modes, except for flexion, predicted median segmental intervertebral rotations and disc pressures were in good agreement with in vivo values. The simulations yielded median facet joint forces of 0 N in flexion, 38 N in extension, 14 N in lateral bending and 60 N in axial rotation that could not be validated due to the paucity of in vivo facet joint forces. In light of high inter-subject variability, one must be cautious when generalizing predictions obtained from one deterministic model. This study demonstrates however that the predictive power increases when FE models are combined together. The median of individual numerical results can hence be used as an improved tool in order to estimate the response of the lumbar spine.

In this article we will introduce a passive BCI system for detecting responses of the human brain on the perception of errors in music. We played cadences, sequences of chords, to subjects who are experts in music theory and actively play an classical instrument. As the expectancy is highest at the ending chord, we randomly introduced cadences with erroneous ending. In consistence with previous studies from the neurosciences we evoked an event-related potential, mainly consisting of an early right anterior negativity re ecting syntatcic error processing followed by a stronger negativity in erroneous trials at 500 ms, induced by semantic processing. We could identify single trials of these processes with a standardized, crossvalidated offline classification scheme, resulting in an accuracy of 75.7 %.

Small-cell lung cancer (SCLC) is an aggressive lung tumor subtype with poor prognosis(1-3). We sequenced 29 SCLC exomes, 2 genomes and 15 transcriptomes and found an extremely high mutation rate of 7.4 +/- 1 protein-changing mutations per million base pairs. Therefore, we conducted integrated analyses of the various data sets to identify pathogenetically relevant mutated genes. In all cases, we found evidence for inactivation of TP53 and RB1 and identified recurrent mutations in the CREBBP, EP300 and MLL genes that encode histone modifiers. Furthermore, we observed mutations in PTEN, SLIT2 and EPHA7, as well as focal amplifications of the FGFR1 tyrosine kinase gene. Finally, we detected many of the alterations found in humans in SCLC tumors from Tp53 and Rb1 double knockout mice(4). Our study implicates histone modification as a major feature of SCLC, reveals potentially therapeutically tractable genomic alterations and provides a generalizable framework for the identification of biologically relevant genes in the context of high mutational background.