Streptococcus pneumoniae is the causative agent of a multitude of diseases and further study into its pathogenies is vital. The pneumococcus is genetically malleable and several tools are available to manipulate this pathogen. In this study, we attempted to utilize one such tool, the Sweet Janus cassette, to replace the capsule locus with other capsule loci in our strain background and found that the efficiency of allelic replacement was low and the number of revertant false-positive colonies was high. We determined that the capacity to recombine capsule varied by the initial isolated colony, suggesting that frequency of reversion is dependent on the bacterial cell line. Alternative selection markers may further expand the application of Sweet Janus. We created novel cassettes that utilized chlorinated phenylalanine as an alternative counter-selection agent in conjunction with the Janus or Sweet Janus cassette, providing a new dual or triple selection marker. Moreover, we created cassettes that do not require engineered resistance in the background strain, including both single and dual selection markers. We were able to utilize all constructs in allelic replacement of the capsule loci. These novel constructs provide a new means for generating gene deletions in S. pneumoniae that expand experimental applications.

Optoelectronic devices are gold standard for 3D evaluation in clinics but due to the complexity of such kind of hardware and the lack of access for patients affordable, transportable and easy to use systems must be developed to be largely used in daily clinics. The Kinect^TM sensor presents various advantages compared to optoelectronic devices: price, transportability but also some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points that make 3D evaluation impossible. To overcome these limitations a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises allowing functional evaluation while performing physical rehabilitation. To validate this new approach a double step method was used. The first step is a laboratory validation where the results obtained with the Kinect^TM have been compared with results obtained with an optoelectronic device, 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of such kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns can be observed. The new methodology and the different steps of the validations are presented in this paper.

The use of motion capture has increased from last decade in a varied spectrum of applications like film special effects, controlling games and robots, rehabilitation system, animations etc. The current human motion capture techniques use markers, structured environment, and high resolution cameras in a dedicated environment. Because of rapid movement, elbow angle estimation is observed as the most difficult problem in human motion capture system. In this paper, we take elbow angle estimation as our research subject and propose a novel, markerless and cost-effective solution that uses RGB camera for estimating elbow angle in real time using part affinity field. We have recruited five (5) participants of (height, 168 ± 8 cm; mass, 61 ± 17 kg) to perform cup to mouth movement and at the same time measured the angle by both RGB camera and Microsoft Kinect. The experimental results illustrate that markerless and cost-effective RGB camera has a median RMS errors of 3.06° and 0.95° in sagittal and coronal plane respectively as compared to Microsoft Kinect.

Upper limb paresis is very common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients’ movements in ecological scenarios. These systems are, however, very expensive and, as such, not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. 14 stroke patients executed ecologically relevant upper limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest’s and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1. Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper limb kinematic parameters in stroke patients.

The progress in markerless technologies is bringing clinicians tools to rapidly evaluate tests such as Timed Up and Go (TUG) without compromising the time of the assessment, yet it raises questions about the potential trade-off in accuracy compared to traditional marker-based systems, suggesting that the precision of these evaluations is not yet conclusively established. The OpenCap system is an easy tool to process 3D kinematics with two iPhone cameras placed in front of the subject. Previous research has primarily focused on walking and movement towards the cameras with the subject facing the cameras (WF) and thus has been unable to disentangle the differences in the movement directions. We have compared the OpenCap system with a standard marker-based system for both walking directions. In OpenCap, we found significantly worse results capturing walking facing away from the cameras (RW). Compared to the marker-based system, our findings indicate troubles measuring kinematic parameters but provide good accuracy in measuring spatial-temporal parameters. These insights pave the way for refining markerless assessment technologies, potentially enhancing their utility in clinical settings.

Background. The markerless motion capture system (MLS) is a new technology that uses deep learning to detect body segments from digital images. This study tested the agreement between the MLS and a force-plate based system (FPS, “gold standard”) to quantify stability control and motor performance (MP) during gait initiation (GI). Methods. Healthy adults (young and elderly) and patients with Parkinson’s disease (PD) performed GI series at spontaneous and maximal velocity on an FPS while being filmed by an MLS. Signals from both systems were used to compute the peak of forward center-of-mass velocity (indicator of MP) and the braking index (BI, indicator of stability control). Results. Descriptive statistics indicated that both systems detected between-groups differences and velocity effects similarly, while a Bland-Altman (BA) plot anal-ysis showed that BI and MP mean biases were virtually zero in all groups and conditions. Bayes factor 01 indicated strong (for BI) and moderate (for MP) evidence that both systems provided equivalent values. However, trial-by-trial analysis of BA plots revealed the possibility of differ-ences > 10% between the two systems. Conclusion. Although non-negligible differences do occur, MLS appears to be as efficient as FPS in detecting PD and velocity condition effects on BI and MP