Quantifying walking speeds in relation to ankle biomechanics on a real-time interactive gait platform: a musculoskeletal modeling approach in healthy adults.

Background: Given the inherent variability in walking speeds encountered in day-to-day activities, understanding the corresponding alterations in ankle biomechanics would provide valuable clinical insights. Therefore, the objective of this study was to examine the influence of different walking speeds on biomechanical parameters, utilizing gait analysis and musculoskeletal modelling. Methods: Twenty healthy volunteers without any lower limb medical history were included in this study. Treadmill-assisted gait-analysis with walking speeds of 0.8 m/s and 1.1 m/s was performed using the Gait Real-time Analysis Interactive Lab (GRAIL®). Collected kinematic data and ground reaction forces were processed via the AnyBody® modeling system to determine ankle kinetics and muscle forces of the lower leg. Data were statistically analyzed using statistical parametric mapping to reveal both spatiotemporal and magnitude significant differences. Results: Significant differences were found for both magnitude and spatiotemporal curves between 0.8 m/s and 1.1 m/s for the ankle flexion (p < 0.001), subtalar force (p < 0.001), ankle joint reaction force and muscles forces of the M. gastrocnemius, M. soleus and M. peroneus longus (α = 0.05). No significant spatiotemporal differences were found between 0.8 m/s and 1.1 m/s for the M. tibialis anterior and posterior. Discussion: A significant impact on ankle joint kinematics and kinetics was observed when comparing walking speeds of 0.8 m/s and 1.1 m/s. The findings of this study underscore the influence of walking speed on the biomechanics of the ankle. Such insights may provide a biomechanical rationale for several therapeutic and preventative strategies for ankle conditions.

The relation between meniscal dynamics and tibiofemoral kinematics.

Over the past 30 years, research on meniscal kinematics has been limited by challenges such as low-resolution imaging and capturing continuous motion from static data. This study aimed to develop a computational knee model that overcomes these limitations and enables the continuous assessment of meniscal dynamics. A high-resolution MRI dataset (n = 11) was acquired in 4 configurations of knee flexion. In each configuration, the menisci were modeled based on the underlying osseous anatomy. Principal Polynomial Shape Analysis (PPSA) was employed for continuous meniscal modeling. Maximal medial anterior horn displacement occurred in 60° of flexion, equaling 6.24 mm posteromedial, while the posterior horn remained relatively stable. At 90° of flexion, the lateral anterior and posterior horn displaced posteromedially, amounting 5.70 mm and 6.51 mm respectively. The maximal observed Average Surface Distance (ASD) equaled 0.70 mm for lateral meniscal modeling in 90° of flexion. Based on our results, a strong relation between meniscal dynamics and tibiofemoral kinematics was confirmed. Expanding on static meniscal modeling and employing PPSA, we derived and validated a standardized and systematic methodological workflow.

Validation of a personalized ligament-constraining discrete element framework for computing ankle joint contact mechanics.

BACKGROUND AND OBJECTIVE: Computer simulations of joint contact mechanics have great merit to improve our current understanding of articular ankle pathology. Owed to its computational simplicity, discrete element analysis (DEA) is an encouraging alternative to finite element analysis (FEA). However, previous DEA models lack subject-specific anatomy and may oversimplify the biomechanics of the ankle. The objective of this study was to develop and validate a personalized DEA framework that permits movement of the fibula and incorporates personalized cartilage thickness as well as ligamentous constraints. METHODS: A linear and non-linear DEA framework, representing cartilage as compressive springs, was established, verified, and validated. Three-dimensional (3D) bony ankle models were constructed from cadaveric lower limb CT scans imaged during application of weight (85 kg) and/or torque (10 Nm). These 3D models were used to generate cartilage thickness and ligament insertion sites based on a previously validated statistical shape model. Ligaments were modelled as non-linear tension-only springs. Validation of contact stress prediction was performed using a simple, axially constrained tibiotalar DEA model against an equivalent FEA model. Validation of ligamentous constraints compared the final position of the ankle mortise to that of the cadaver after application of torque and sequential ligament sectioning. Finally, a combined ligamentous-constraining DEA model was validated for predicted contact stress against an equivalent ligament-constraining FEA model. RESULTS: The linear and non-linear DEA model reproduced a mean articular contact stress within 0.36 MPa and 0.39 MPa of the FEA calculated stress, respectively. With respect to the ligamentous validation, the DEA ligament-balancing algorithm could reproduce the position of the distal fibula within the ankle mortise to within 0.97 mm of the experimental observed distal fibula. When combining the ligament-constraining and contact stress algorithm, DEA was able to reproduce a mean articular contact stress to within 0.50 MPa of the FEA calculated contact stress. CONCLUSION: The DEA framework presented herein offers a computationally efficient alternative to FEA for the prediction of contact stress in the ankle joint, manifesting its potential to enhance the mechanical understanding of articular ankle pathologies on both a patient-specific and population-wide level. The novelty of this model lies in its personalized nature, inclusion of the distal tibiofibular joint and the use of non-linear ligament balancing to maintain the physiological ankle joint articulation.

Personalized statistical modeling of soft tissue structures in the knee.

Background and Objective: As in vivo measurements of knee joint contact forces remain challenging, computational musculoskeletal modeling has been popularized as an encouraging solution for non-invasive estimation of joint mechanical loading. Computational musculoskeletal modeling typically relies on laborious manual segmentation as it requires reliable osseous and soft tissue geometry. To improve on feasibility and accuracy of patient-specific geometry predictions, a generic computational approach that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy is presented. Methods: A personalized prediction algorithm was established to derive soft tissue geometry of the knee, originating solely from skeletal anatomy. Based on a MRI dataset (n = 53), manual identification of soft-tissue anatomy and landmarks served as input for our model by use of geometric morphometrics. Topographic distance maps were generated for cartilage thickness predictions. Meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. Elastic mesh wrapping was applied for ligamentous and patellar tendon path modeling. Leave-one-out validation experiments were conducted for accuracy assessment. Results: The Root Mean Square Error (RMSE) for the cartilage layers of the medial tibial plateau, the lateral tibial plateau, the femur and the patella equaled respectively 0.32 mm (range 0.14-0.48), 0.35 mm (range 0.16-0.53), 0.39 mm (range 0.15-0.80) and 0.75 mm (range 0.16-1.11). Similarly, the RMSE equaled respectively 1.16 mm (range 0.99-1.59), 0.91 mm (0.75-1.33), 2.93 mm (range 1.85-4.66) and 2.04 mm (1.88-3.29), calculated over the course of the anterior cruciate ligament, posterior cruciate ligament, the medial and the lateral meniscus. Conclusion: A methodological workflow is presented for patient-specific, morphological knee joint modeling that avoids laborious segmentation. By allowing to accurately predict personalized geometry this method has the potential for generating large (virtual) sample sizes applicable for biomechanical research and improving personalized, computer-assisted medicine.

Shotgun genome sequence of the large purple photosynthetic bacterium Rhodospirillum photometricum DSM122.

Here, we present the shotgun genome sequence of the purple photosynthetic bacterium Rhodospirillum photometricum DSM122. The photosynthetic apparatus of this bacterium has been particularly well studied by microscopy. The knowledge of the genome of this oversize bacterium will allow us to compare it with the other purple bacterial organisms to follow the evolution of the photosynthetic apparatus.

Draft genome sequence of the purple photosynthetic bacterium Phaeospirillum molischianum DSM120, a particularly versatile bacterium.

Here we present the draft genome sequence of the versatile and adaptable purple photosynthetic bacterium Phaeospirillum molischianum DSM120. This study advances the understanding of the adaptability of this bacterium, as well as the differences between the Phaeospirillum and Rhodospirillum genera.

Principal polynomial shape analysis: A non-linear tool for statistical shape modeling.

BACKGROUND AND OBJECTIVES: The most widespread statistical modeling technique is based on Principal Component Analysis (PCA). Although this approach has several appealing features, it remains hampered by its linearity. Principal Polynomial Analysis (PPA) can capture non-linearity in a sequential algorithm, while maintaining the interesting properties of PCA. PPA is, however, computationally expensive in handling shape surface data. To this end, we propose Principal Polynomial Shape Analysis (PPSA) as an adjusted approach for non-linear shape analyzes. The aim of this study was to assess PPSA's features, its model boundaries and its general applicability. METHODS: PCA and PPSA-based shape models were investigated on one verification and three model evaluation experiments. In the verification experiment, the estimated mean of the PCA and PPSA model on a data set of synthetic lower limbs of different lengths in different poses were compared to the real mean. Further, the PCA-based and PPSA shape models were tested for three challenging cases namely for shape model creation of gait marker data, for regression analysis on aging faces and for modeling pose variation in full body scans. For the latter, additionally a Fundamental Coordinate Model (FCM) and a PPSA model on Fundamental Coordinate(FC) space was created. The performances were evaluated based on model-based accuracy, generalization, compactness and specificity. RESULTS: In the verification experiment, the scaling error reduced from 75% to below 1% when employing PPSA instead of PCA for a training set with 180° angular variation. For the model evaluation experiments, the PPSA models described the data as accurate and generalized as the PCA-based shape models. The PPSA models were slightly more compact and specific (up to 30%) than the PCA-based models. In regression, PCA and PPSA-based parameterizations explained a similar amount of variation. Lastly, for the full body scans, applying PPSA to parameterizations improved the compactness and accuracy. CONCLUSIONS: PPSA describes the non-linear relationships between principal variations in a parameterized space. Compared to standard PCA-based shape models, capturing the non-linearity reduced the nonsense information in the shape components and improved the description of the data mean.

Personalised statistical modelling of soft tissue structures in the ankle.

BACKGROUND AND OBJECTIVE: Revealing the complexity behind subject-specific ankle joint mechanics requires simultaneous analysis of three-dimensional bony and soft-tissue structures. 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Since manual segmentation of these models is time-consuming and subject to manual errors, (semi-) automatic methods could improve the accuracy and enlarge the sample size of personalised 'in silico' biomechanical experiments and computer-assisted treatment planning. Therefore, our aim was to automatically predict ligament paths, cartilage topography and thickness in the ankle joint based on statistical shape modelling. METHODS: A personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics, based on an 'in-house' generated lower limb skeletal model (N = 542), tibiotalar cartilage (N = 60) and ankle ligament segmentations (N = 10). For cartilage, a population-averaged thickness map was determined by use of partial least-squares regression. Ligaments were wrapped around bony contours based on iterative shortest path calculation. Accuracy of ligament path and cartilage thickness prediction was quantified using leave-one-out experiments. The novel personalised thickness prediction was compared with a constant cartilage thickness of 1.50 mm by use of a paired sample T-test. RESULTS: Mean distance error of cartilage and ligament prediction was 0.12 mm (SD 0.04 mm) and 0.54 mm (SD 0.05 mm), respectively. No significant differences were found between the personalised thickness cartilage and segmented cartilage of the tibia (p = 0.73, CI [-1.60 .10-17, 1.13 .10-17]) and talus (p = 0.95, CI[ -1.35 .10-17, 1.28 .10-17]). For the constant thickness cartilage, a statistically significant difference was found in 89% and 92% of the tibial (p < 0.001, CI [0.51, 0.58]) and talar (p < 0.001, CI [0.33, 0.40]) cartilage area. CONCLUSIONS: In this study, we described a personalised prediction algorithm of cartilage and ligaments in the ankle joint. We were able to predict cartilage and main ankle ligaments with submillimeter accuracy. The proposed method has a high potential for generating large (virtual) sample sizes in biomechanical research and mitigates technological advances in computer-assisted orthopaedic surgery.

Wear patterns in knee OA correlate with native limb geometry.

Background: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anatomical standardization relying on non-rigid registration, we aim to compare morphotypes of a healthy control cohort and virtual reconstructed twins of end-stage knee OA subjects to assess the shape-related knee OA risk and to evaluate possible correlations between phenotype and location of cartilage loss. Methods: Out of an anonymized dataset provided by the Medacta company (Medacta International SA, Castel S. Pietro, CH), 798 end-stage knee OA cases were extracted. Cartilage wear patterns were observed by computing joint space width. The three-dimensional joint space width data was translated into a two-dimensional pixel image, which served as the input for a principal polynomial autoencoder developed for non-linear encoding of wear patterns. Virtual healthy twin reconstruction enabled the investigation of the morphology-related risk for OA requiring joint arthroplasty. Results: The polynomial autoencoder revealed 4 dominant, orthogonal components, accounting for 94% of variance in the latent feature space. This could be interpreted as medial (54.8%), bicompartmental (25.2%) and lateral (9.1%) wear. Medial wear was subdivided into anteromedial (11.3%) and posteromedial (10.4%) wear. Pre-diseased limb geometry had a positive predictive value of 0.80 in the prediction of OA incidence (r 0.58, p < 0.001). Conclusion: An innovative methodological workflow is presented to correlate cartilage wear patterns with knee joint phenotype and to assess the distinct knee OA risk based on pre-diseased lower limb morphology. Confirming previous research, both alignment and joint geometry are of importance in knee OA disease onset and progression.

Ischiofemoral impingement: the evolutionary cost of pelvic obstetric adaptation.

The risk for ischiofemoral impingement has been mainly related to a reduced ischiofemoral distance and morphological variance of the femur. From an evolutionary perspective, however, there are strong arguments that the condition may also be related to sexual dimorphism of the pelvis. We, therefore, investigated the impact of gender-specific differences in anatomy of the ischiofemoral space on the ischiofemoral clearance, during static and dynamic conditions. A random sampling Monte-Carlo experiment was performed to investigate ischiofemoral clearance during stance and gait in a large (n = 40 000) virtual study population, while using gender-specific kinematics. Subsequently, a validated gender-specific geometric morphometric analysis of the hip was performed and correlations between overall hip morphology (statistical shape analysis) and standard discrete measures (conventional metric approach) with the ischiofemoral distance were evaluated. The available ischiofemoral space is indeed highly sexually dimorphic and related primarily to differences in the pelvic anatomy. The mean ischiofemoral distance was 22.2 ± 4.3 mm in the females and 29.1 ± 4.1 mm in the males and this difference was statistically significant (P < 0.001). Additionally, the ischiofemoral distance was observed to be a dynamic measure, and smallest during femoral extension, and this in turn explains the clinical sign of pain in extension during long stride walking. In conclusion, the presence of a reduced ischiofemroal distance and related risk to develop a clinical syndrome of ischiofemoral impingement is strongly dominated by evolutionary effects in sexual dimorphism of the pelvis. This should be considered when female patients present with posterior thigh/buttock pain, particularly if worsened by extension. Controlled laboratory study.

Broadening the scope of Baeyer-Villiger monooxygenase activities toward α,ß-unsaturated ketones: a promising route to chiral enol-lactones and ene-lactones.

Three regiodivergent Baeyer-Villiger mono-oxygenases (enantioselectively) oxidized a series of cyclic α,ß-unsaturated ketones into (chiral) either enol-lactones or ene-lactones. An easy-to-use and efficient biocatalytic process based on a host-microorganism deprived of unwanted activities (knock-out mutant) was developed to enable the exclusive synthesis of unsaturated lactones.

Immobilization of arsenite and ferric iron by Acidithiobacillus ferrooxidans and its relevance to acid mine drainage.

Weathering of the As-rich pyrite-rich tailings of the abandoned mining site of Carnoulès (southeastern France) results in the formation of acid waters heavily loaded with arsenic. Dissolved arsenic present in the seepage waters precipitates within a few meters from the bottom of the tailing dam in the presence of microorganisms. An Acidithiobacillus ferrooxidans strain, referred to as CC1, was isolated from the effluents. This strain was able to remove arsenic from a defined synthetic medium only when grown on ferrous iron. This A. ferrooxidans strain did not oxidize arsenite to arsenate directly or indirectly. Strain CC1 precipitated arsenic unexpectedly as arsenite but not arsenate, with ferric iron produced by its energy metabolism. Furthermore, arsenite was almost not found adsorbed on jarosite but associated with a poorly ordered schwertmannite. Arsenate is known to efficiently precipitate with ferric iron and sulfate in the form of more or less ordered schwertmannite, depending on the sulfur-to-arsenic ratio. Our data demonstrate that the coprecipitation of arsenite with schwertmannite also appears as a potential mechanism of arsenite removal in heavily contaminated acid waters. The removal of arsenite by coprecipitation with ferric iron appears to be a common property of the A. ferrooxidans species, as such a feature was observed with one private and three collection strains, one of which was the type strain.