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\"This book is about the life and work of Frederick de Wit (1629-1706), one of the most famous dealers of maps, prints and art during the Dutch Golden Age, and his contribution to the dissemination of the knowledge of cartography. The Amsterdam firm of Frederick de Wit operated under the name \"De Witte Pascaert\" (The White Chart) from 1654 to 1710. It offered all kinds of printing and was one of the most successful publishers of maps and prints in the second half of the seventeenth century. The description of De Wit's life and work is followed by an in-depth analysis and dating of the atlases and maps issued under his name.\"--Back cover.

Accurate predictive simulations of human gait rely on optimisation criteria to solve the system’s redundancy. Defining such criteria is challenging, as the objectives driving the optimization of human gait are unclear. This study evaluated how minimising various physiologically-based criteria (i.e., cost of transport, muscle activity, head stability, foot–ground impact, and knee ligament use) affects the predicted gait, and developed and evaluated a combined, weighted cost function tuned to predict healthy gait. A generic planar musculoskeletal model with 18 Hill-type muscles was actuated using a reflex-based, parameterized controller. First, the criteria were applied into the base simulation framework separately. The gait pattern predicted by minimising each criterion was compared to experimental data of healthy gait using coefficients of determination (R2) and root mean square errors (RMSE) averaged over all biomechanical variables. Second, the optimal weighted combined cost function was created through stepwise addition of the criteria. Third, performance of the resulting combined cost function was evaluated by comparing the predicted gait to a simulation that was optimised solely to track experimental data. Optimising for each of the criteria separately showed their individual contribution to distinct aspects of gait (overall R2: 0.37–0.56; RMSE: 3.47–4.63 SD). An optimally weighted combined cost function provided improved overall agreement with experimental data (overall R2: 0.72; RMSE: 2.10 SD), and its performance was close to what is maximally achievable for the underlying simulation framework. This study showed how various optimisation criteria contribute to synthesising gait and that careful weighting of them is essential in predicting healthy gait.

Movement of skin markers with respect to their underlying bone (i.e. soft tissue artifacts (STAs)) might corrupt the accuracy of marker-based movement analyses. This study aims to quantify STAs in 3D for foot markers and their effect on multi-segment foot kinematics as calculated by the Oxford and Rizzoli Foot Models (OFM, RFM). Fifteen subjects with asymptomatic feet were seated on a custom-made loading device on a computed tomography (CT) table, with a combined OFM and RFM marker set on their right foot. One unloaded reference CT-scan with neutral foot position was performed, followed by 9 loaded CT-scans at different foot positions. The 3D-displacement (i.e. STA) of each marker in the underlying bone coordinate system between the reference scan and other scans was calculated. Subsequently, segment orientations and joint angles were calculated from the marker positions according to OFM and RFM definitions with and without STAs. The differences in degrees were defined as the errors caused by the marker displacements. Markers on the lateral malleolus and proximally on the posterior aspect of the calcaneus showed the largest STAs. The hindfoot-shank joint angle was most affected by STAs in the most extreme foot position (40° plantar flexion) in the sagittal plane for RFM (mean: 6.7°, max: 11.8°) and the transverse plane for OFM (mean: 3.9°, max: 6.8°). This study showed that STAs introduce clinically relevant errors in multi-segment foot kinematics. Moreover, it identified marker locations that are most affected by STAs, suggesting that their use within multi-segment foot models should be reconsidered.

Segment coordinate systems (CSs) of marker-based multi-segment foot models are used to measure foot kinematics, however their relationship to the underlying bony anatomy is barely studied. The aim of this study was to compare marker-based CSs (MCSs) with bone morphology-based CSs (BCSs) for the hindfoot and forefoot. Markers were placed on the right foot of fifteen healthy adults according to the Oxford, Rizzoli and Amsterdam Foot Model (OFM, RFM and AFM, respectively). A CT scan was made while the foot was loaded in a simulated weight-bearing device. BCSs were based on axes of inertia. The orientation difference between BCSs and MCSs was quantified in helical and 3D Euler angles. To determine whether the marker models were able to capture inter-subject variability in bone poses, linear regressions were performed. Compared to the hindfoot BCS, all MCSs were more toward plantar flexion and internal rotation, and RFM was also oriented toward more inversion. Compared to the forefoot BCS, OFM and RFM were oriented more toward dorsal and plantar flexion, respectively, and internal rotation, while AFM was not statistically different in the sagittal and transverse plane. In the frontal plane, OFM was more toward eversion and RFM and AFM more toward inversion compared to BCS. Inter-subject bone pose variability was captured with RFM and AFM in most planes of the hindfoot and forefoot, while this variability was not captured by OFM. When interpreting multi-segment foot model data it is important to realize that MCSs and BCSs do not always align.

BackgroundThree different synovial immunopathotypes of rheumatoid arthritis (RA) have been identified: Fibroid-Pauci immune (FP, fibroblast-rich), Diffuse-Myeloid (DM, macrophage-rich) and Lympho-Myeloid (LM, lymphocyte- and macrophage-rich), and have been associated with treatment outcome to biologicals [1]. Therefore, identification of the synovial immunopathotypes before the start of therapy could be supportive for development of individualized treatment strategies. However, this currently requires invasive synovial tissue sampling. Novel whole-body molecular imaging with positron emission tomography (PET) and the use of specific PET tracers can non-invasively detect and quantify the presence of immune cells in RA inflamed synovium. Folate-receptor beta (FRβ) is a cell surface receptor on macrophages, shown clinical exploitation for high specificity PET imaging of arthritis [2]. However, it remains to be elucidated whether FRβ is a suitable marker for RA immunopathotype stratification. Furthermore, it is unclear whether FRβ is expressed on macrophages with a pro-inflammatory (M1) or homeostatic (M2) phenotype in the 3 immunopathotypes.Objectives:(1) Investigate FRβ expression across the three distinct RA immunopathotypes.(2) Investigate FRβ expression in relation to the general macrophage marker CD68 and the mannose receptor CD206 (which is associated with M2-type macrophages [3]).MethodsSynovial biopsies of the RA-affected ankle or knee (N=28) were retrieved from RA patients with clinically active disease defined by ACR RA criteria [4]. Subsequently, biopsy sections were immunohistologically stained in order to stratify each patient into one of three RA-immunopathotypes. Confocal microscopy was used to determine CD68, FRβ and CD206 expression (integrated density), and co-expression (comparing fold-change average expression) for all patients within each immunopathotype (N=8-10/ group).ResultsOut of 28 RA synovial biopsies 10 could be classified as FP, 9 DM and 9 LM immunopathotype. Average expression of CD68, FRβ and CD206 was significantly increased in the DM and LM compared to the FP immunopathotype (## p<0.01). Quantitative CD68, FRβ and CD206 expression was highest in the DM immunopathotype. FRβ expression correlated significantly with CD206 expression in all three pathotypes (Spearman RFP= 0.67; RDM= 0.76; RLM= 0.49). On the contrary FRβ expression did not correlate with CD68 expression except in the DM pathotype (Spearman RDM= 0.46).ConclusionThe results of this study put forward that FRβ is a potential target for delineation of RA immunopathotypes, to be explored for non-invasive molecular imaging stratification. Furthermore, investigation of FRβ expression has an additive value over CD68 since it can be used to distinguish the presence of M2 macrophages in the RA synovium specifically.References[1] Lewis et al., Cell Rep. 2019;28(9):2455-2470.e5[2] Steinz et al., Front Immunol. 2022;13:819163[3] Alivernini et al., Nat. Med. 2020;26(8):1295-1306[4] Arnet et al., Arthritis Rheum. 1988;31(3):315-24Acknowledgements:NIL.Disclosure of InterestsNone Declared.