Age Structure Eliminates the Impact of Coinfection on Epidemic Dynamics in a Freshwater Zooplankton System.

AbstractParasites often coinfect host populations and, by interacting within hosts, might change the trajectory of multiparasite epidemics. However, host-parasite interactions often change with host age, raising the possibility that within-host interactions between parasites might also change, influencing the spread of disease. We measured how heterospecific parasites interacted within zooplankton hosts and how host age changed these interactions. We then parameterized an epidemiological model to explore how age effects altered the impact of coinfection on epidemic dynamics. In our model, we found that in populations where epidemiologically relevant parameters did not change with age, the presence of a second parasite altered epidemic dynamics. In contrast, when parameters varied with host age (based on our empirical measures), there was no longer a difference in epidemic dynamics between singly infected and coinfected populations, indicating that variable age structure within a population eliminates the impact of coinfection on epidemic dynamics. Moreover, infection prevalence of both parasites was lower in populations where epidemiologically relevant parameters changed with age. Given that host population age structure changes over time and space, these results indicate that age effects are important for understanding epidemiological processes in coinfected systems and that studies focused on a single age group could yield inaccurate insights.

Adversarial Autoencoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement.

Motor rehabilitation is used to improve motor control skills to improve the patient's quality of life. Regular adjustments based on the effect of therapy are necessary, but this can be time-consuming for the clinician. This study proposes to use an efficient tool for high-dimensional data by considering a deep learning approach for dimensionality reduction of hand movement recorded using a wireless remote control embedded with the Oculus Rift S. This latent space is created as a visualization tool also for use in a reinforcement learning (RL) algorithm employed to provide a decision-making framework. The data collected consists of motions drawn with wireless remote control in an immersive VR environment for six different motions called "Cube", "Cylinder", "Heart", "Infinity", "Sphere", and "Triangle". From these collected data, different artificial databases were created to simulate variations of the data. A latent space representation is created using an adversarial autoencoder (AAE), taking into account unsupervised (UAAE) and semi-supervised (SSAAE) training. Then, each test point is represented by a distance metric and used as a reward for two classes of Multi-Armed Bandit (MAB) algorithms, namely Boltzmann and Sibling Kalman filters. The results showed that AAE models can represent high-dimensional data in a two-dimensional latent space and that MAB agents can efficiently and quickly learn the distance evolution in the latent space. The results show that Sibling Kalman filter exploration outperforms Boltzmann exploration with an average cumulative weighted probability error of 7.9 versus 19.9 using the UAAE latent space representation and 8.0 versus 20.0 using SSAAE. In conclusion, this approach provides an effective approach to visualize and track current motor control capabilities regarding a target in order to reflect the patient's abilities in VR games in the context of DDA.

A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate.

Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland-Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue.

Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach.

BACKGROUND: Inertial measurement units (IMUs) are promising tools for collecting human movement data. Model-based filtering approaches (e.g. Extended Kalman Filter) have been proposed to estimate joint angles from IMUs data but little is known about the potential of data-driven approaches. RESEARCH QUESTION: Can deep learning models accurately predict lower limb joint angles from IMU data during gait? METHODS: Lower-limb kinematic data were simultaneously measured with a marker-based motion capture system and running leggings with 5 integrated IMUs measuring acceleration and angular velocity at the pelvis, thighs and tibias. Data acquisition was performed on 27 participants (26.5 (3.9) years, 1.75 (0.07) m, 68.3 (10.0) kg) while walking at 4 and 6 km/h and running at 8, 10, 12 and 14 km/h on a treadmill. The model input consists of raw IMU data, while the output estimates the joint angles of the lower body. The model was trained with a nested k-fold cross-validation and tested considering a user-independent approach. Mean error (ME), mean absolute error (MAE) and Pearson correlation coefficient (r) were computed between the ground truth and predicted joint angles. RESULTS: MAE for the DOFs ranged from 2.2(0.9) to 5.1(2.7)° with an average of 3.6(2.1)°. r ranged from 0.67(0.23) to 0.99(0.01) with moderate correlation (0.4≤r<0.7) was found for the hip right rotation and lumbar extension, strong correlation (0.7≤r<0.9) was found for the hip left rotation and ankle right/left inversion while all other DOFs showed very strong correlation (r≥0.9). SIGNIFICANCE: The proposed model can reliably predict joint kinematics for walking, running and gait transitions without specific knowledge about the body characteristics of the wearer, or the position and orientation of the IMU relative to the attached segment. These results have been validated with treadmill gait, and have not yet been confirmed for gait in other settings.

Adversarial autoencoder for visualization and classification of human activity: Application to a low-cost commercial force plate.

The ability to visualize and interpret high dimensional time-series data will be critical as wearable and other sensors are adopted in rehabilitation protocols. This study proposes a latent space representation of high dimensional time-series data for data visualization. For that purpose, a deep learning model called Adversarial AutoEncoder (AAE) is proposed to perform efficient data dimensionality reduction by considering unsupervised and semi-supervised adversarial training. Eighteen subjects were recruited for the experiment and performed two sets of exercises (upper and lower body) on the Wii Balance Board. Then, the accuracy of the latent space representation is evaluated on both sets of exercises separately. Data dimensionality reduction with conventional Machine Learning (ML) and supervised Deep Learning (DL) classification are also performed to compare the efficiency of AAE approaches. The results showed that AAE can outperform conventional ML approaches while providing close results to DL supervised classification. AAE approaches for data visualization are a promising approach to monitor the subject's movements and detect adverse events or similarity with previous data, providing an intuitive way to monitor the patient's progress and provide potential information for rehabilitation tracking.

Convolutional and recurrent neural network for human activity recognition: Application on American sign language.

Human activity recognition is an important and difficult topic to study because of the important variability between tasks repeated several times by a subject and between subjects. This work is motivated by providing time-series signal classification and a robust validation and test approaches. This study proposes to classify 60 signs from the American Sign Language based on data provided by the LeapMotion sensor by using different conventional machine learning and deep learning models including a model called DeepConvLSTM that integrates convolutional and recurrent layers with Long-Short Term Memory cells. A kinematic model of the right and left forearm/hand/fingers/thumb is proposed as well as the use of a simple data augmentation technique to improve the generalization of neural networks. DeepConvLSTM and convolutional neural network demonstrated the highest accuracy compared to other models with 91.1 (3.8) and 89.3 (4.0) % respectively compared to the recurrent neural network or multi-layer perceptron. Integrating convolutional layers in a deep learning model seems to be an appropriate solution for sign language recognition with depth sensors data.

Injection laser system for Advanced Radiographic Capability using chirped pulse amplification on the National Ignition Facility.

We report on the design, performance, and qualification of the injection laser system designed to deliver joule-level chirped pulse beamlets arranged in dual rectangular beam formats into two main laser amplifier beamlines of the National Ignition Facility. The system is designed to meet the requirements of the Advanced Radiographic Capability upgrade with features that deliver performance, adjustability, and long-term reliability.

Maximal isometric force exertion predicted by the force feasible set formalism: application to handbraking.

The aim of this study was to test the capacity of the force feasible set formalism to predict maximal force exertion during isometric handbraking. Maximal force exertion and upper-limb posture were measured with a force sensor embedded in a handbrake and an optoelectronic system, respectively. Eleven subjects participated in the experiment which consisted of exerting the maximal force in isometric conditions considering five hand brake positions relative to the seat H-point. Then, maximal force was predicted by the force feasible set obtained from an upper-limb musculoskeletal model. The root-mean-square (RMS) of the angle between measured and predicted forces was 8.4° while the RMS error (RMSE) for amplitude prediction was 95.4 N. However, predicted, and measured force amplitudes were highly correlated (r = 0.88, p < 0.05, slope = 0.97, intercept = 73.3N) attesting the capacity of the model to predict force exertion according to the subject's posture. The implications in the framework of ergonomics are then discussed. Practitioner summary: Maximal force exertion is of paramount importance in digital human modelling. We used the force feasible set formalism to predict maximal force exertion during handbraking from posture and anthropometric data. The predicted and measured force orientation showed a RMS of 8.4° while amplitude presented a RMSE of 95.4 N with a strong correlation (r = 0.88, p < 0.05, slope 0.97, intercept 77.3 N).

Efficacy and Improved Resistance Potential of a Cofactor-Independent InhA Inhibitor of Mycobacterium tuberculosis in the C3HeB/FeJ Mouse Model.

AN12855 is a direct, cofactor-independent inhibitor of InhA in Mycobacterium tuberculosis In the C3HeB/FeJ mouse model with caseous necrotic lung lesions, AN12855 proved efficacious with a significantly lower resistance frequency than isoniazid. AN12855 drug levels were better retained in necrotic lesions and caseum where the majority of hard to treat, extracellular bacilli reside. Owing to these combined attributes, AN12855 represents a promising alternative to the frontline antituberculosis agent isoniazid.

Boron-Pleuromutilins as Anti- Wolbachia Agents with Potential for Treatment of Onchocerciasis and Lymphatic Filariasis.

A series of pleuromutilins modified by introduction of a boron-containing heterocycle on C(14) of the polycyclic core are described. These analogs were found to be potent anti- Wolbachia antibiotics and, as such, may be useful in the treatment of filarial infections caused by Onchocerca volvulus, resulting in Onchocerciasis or river blindness, or Wuchereria bancrofti and Brugia malayi and related parasitic nematodes resulting in lymphatic filariasis. These two important neglected tropical diseases disproportionately impact patients in the developing world. The lead preclinical candidate compound containing 7-fluoro-6-oxybenzoxaborole (15, AN11251) was shown to have good in vitro anti- Wolbachia activity and physicochemical and pharmacokinetic properties providing high exposure in plasma. The lead was effective in reducing the Wolbachia load in filarial worms following oral administration to mice.

Discovery of a cofactor-independent inhibitor of Mycobacterium tuberculosis InhA.

New antitubercular agents are needed to combat the spread of multidrug- and extensively drug-resistant strains of Mycobacterium tuberculosis. The frontline antitubercular drug isoniazid (INH) targets the mycobacterial enoyl-ACP reductase, InhA. Resistance to INH is predominantly through mutations affecting the prodrug-activating enzyme KatG. Here, we report the identification of the diazaborines as a new class of direct InhA inhibitors. The lead compound, AN12855, exhibited in vitro bactericidal activity against replicating bacteria and was active against several drug-resistant clinical isolates. Biophysical and structural investigations revealed that AN12855 binds to and inhibits the substrate-binding site of InhA in a cofactor-independent manner. AN12855 showed good drug exposure after i.v. and oral delivery, with 53% oral bioavailability. Delivered orally, AN12855 exhibited dose-dependent efficacy in both an acute and chronic murine model of tuberculosis infection that was comparable with INH. Combined, AN12855 is a promising candidate for the development of new antitubercular agents.

Force feasible set prediction with artificial neural network and musculoskeletal model.

Developing tools to predict the force capabilities of the human limbs through the Force Feasible Set (FFS) may be of great interest for robotic assisted rehabilitation and digital human modelling for ergonomics. Indeed, it could help to refine rehabilitation programs for active participation during exercise therapy and to prevent musculoskeletal disorders. In this framework, the purpose of this study is to use artificial neural networks (ANN) to predict the FFS of the upper-limb based on joint centre Cartesian positions and anthropometric data. Seventeen right upper-limb musculoskeletal models based on individual anthropometric data are created. For each musculoskeletal model, the FFS is computed for 8428 different postures. For any combination of force direction and joint positions, ANNs can predict the FFS with high values of coefficient of determination (R2 > 0.89) between the true and predicted data.

Wheelchair propulsion: Force orientation and amplitude prediction with Recurrent Neural Network.

The aim of this study was to use Recurrent Neural Network (RNN) to predict the orientation and amplitude of the applied force during the push phase of manual wheelchair propulsion. Trunk and the right-upper limb kinematics data were assessed with an optoeletronic device (Qualisys) and the force applied on the handrim was recorded with an instrumented wheel (SMARTWheel®). Data acquisitions were performed at 60/80/10/120/140% of the freely chosen frequency at submaximal and maximal conditions. The final database consisted of d = 5708 push phases. The input data were the trunk and right upper-limb kinematics (joint angle, angular velocity and acceleration) and anthropometric data (height, weight, segment length) and the output data were the applied forces orientation and amplitude. A ratio of 70/15/15 was used to train, validate and test the RNN (dtrain = 3996, dvalidation = 856 and dtest = 856). The angle and amplitude errors between the measured and predicted force was assessed from dtest. Results showed that for most of the push phase (∼70%), the force direction prediction errors were less than 12°. The mean absolute amplitude errors were less than 8 N and the mean absolute amplitude percentage errors were less than 20% for most of the push phase (∼80%).

Evaluation and validation of musculoskeletal force feasible set indices: Application to manual wheelchair propulsion.

The aim of this work was to assess handrim wheelchair propulsion effectiveness, related to the applied forces on the handrim, through the force feasible set. For a given posture of the upper-limb, it represents the set of isometric forces that can be applied on the handrim in any direction. The force feasible set was predicted from a musculoskeletal model of the upper-limb and trunk (10 degrees of freedom and 56 muscles). The aim of the first part of the study was to compare the force feasible set prediction and the force currently applied on the handrim. The second part proposes the creation of a new index called "Musculoskeletal Postural Performance Index" (MPPI) derived from the force feasible set and its comparison with the Mechanical Efficiency Force (MEF). These comparisons were conducted at 60, 80, 100, 120 and 140% of the Freely Chosen Frequency at submaximal and maximal conditions on 5 different phases of the push phase. The values of the MPPI were significantly correlated with those of the MEF. During the course of the push phase, the orientation of the force feasible set main axis approached that of the measured force and the force effectiveness evaluated through the MPPI increased.

Discovery of a Potent and Specific M. tuberculosis Leucyl-tRNA Synthetase Inhibitor: (S)-3-(Aminomethyl)-4-chloro-7-(2-hydroxyethoxy)benzo[c][1,2]oxaborol-1(3H)-ol (GSK656).

There is an urgent need to develop new and safer antitubercular agents that possess a novel mode of action. We synthesized and evaluated a novel series of 3-aminomethyl 4-halogen benzoxaboroles as Mycobacterium tuberculosis (Mtb) leucyl-tRNA synthetase (LeuRS) inhibitors. A number of Mtb LeuRS inhibitors were identified that demonstrated good antitubercular activity with high selectivity over human mitochondrial and cytoplasmic LeuRS. Further evaluation of these Mtb LeuRS inhibitors by in vivo pharmacokinetics (PK) and murine tuberculosis (TB) efficacy models led to the discovery of GSK3036656 (abbreviated as GSK656). This molecule shows potent inhibition of Mtb LeuRS (IC50 = 0.20 µM) and in vitro antitubercular activity (Mtb H37Rv MIC = 0.08 µM). Additionally, it is highly selective for the Mtb LeuRS enzyme with IC50 of >300 µM and 132 µM for human mitochondrial LeuRS and human cytoplasmic LeuRS, respectively. In addition, it exhibits remarkable PK profiles and efficacy against Mtb in mouse TB infection models with superior tolerability over initial leads. This compound has been progressed to clinical development for the treatment of tuberculosis.

Improving the upper-limb force feasible set evaluation by muscles maximal isometric force identification and cocontraction factors.

In order to improve the evaluation of the force feasible set (FFS) of the upper-limb which is of great interest in the biomechanics field, this study proposes two additional techniques. The first one is based on the identification of the maximal isometric force (MIF) of Hill-based muscles models from sEMG and isometric force measurements at the hand. The second one considers muscles cocontraction. The FFS was computed with an upper-limb musculoskeletal model in three different cases. The first one (M1) considered binary muscular activation and a simple MIF scaling method based on the weight and muscle length of the subject. The second one (M2) introduces cocontraction factors determined from sEMG. The third one (M3) considers the cocontraction factors and the MIF identification. Finally, M1, M2 and M3 are compared with end-effector force measurement. M3 outperforms the two other methods on FFS prediction demonstrating the validity and the usefulness of MIF identification and the consideration of the cocontraction factors.

Discovery of Novel Oral Protein Synthesis Inhibitors of Mycobacterium tuberculosis That Target Leucyl-tRNA Synthetase.

The recent development and spread of extensively drug-resistant and totally drug-resistant resistant (TDR) strains of Mycobacterium tuberculosis highlight the need for new antitubercular drugs. Protein synthesis inhibitors have played an important role in the treatment of tuberculosis (TB) starting with the inclusion of streptomycin in the first combination therapies. Although parenteral aminoglycosides are a key component of therapy for multidrug-resistant TB, the oxazolidinone linezolid is the only orally available protein synthesis inhibitor that is effective against TB. Here, we show that small-molecule inhibitors of aminoacyl-tRNA synthetases (AARSs), which are known to be excellent antibacterial protein synthesis targets, are orally bioavailable and effective against M. tuberculosis in TB mouse infection models. We applied the oxaborole tRNA-trapping (OBORT) mechanism, which was first developed to target fungal cytoplasmic leucyl-tRNA synthetase (LeuRS), to M. tuberculosis LeuRS. X-ray crystallography was used to guide the design of LeuRS inhibitors that have good biochemical potency and excellent whole-cell activity against M. tuberculosis Importantly, their good oral bioavailability translates into in vivo efficacy in both the acute and chronic mouse models of TB with potency comparable to that of the frontline drug isoniazid.

Antimalarial Benzoxaboroles Target Plasmodium falciparum Leucyl-tRNA Synthetase.

There is a need for new antimalarials, ideally with novel mechanisms of action. Benzoxaboroles have been shown to be active against bacteria, fungi, and trypanosomes. Therefore, we investigated the antimalarial activity and mechanism of action of 3-aminomethyl benzoxaboroles against Plasmodium falciparum Two 3-aminomethyl compounds, AN6426 and AN8432, demonstrated good potency against cultured multidrug-resistant (W2 strain) P. falciparum (50% inhibitory concentration [IC50] of 310 nM and 490 nM, respectively) and efficacy against murine Plasmodium berghei infection when administered orally once daily for 4 days (90% effective dose [ED90], 7.4 and 16.2 mg/kg of body weight, respectively). To characterize mechanisms of action, we selected parasites with decreased drug sensitivity by culturing with stepwise increases in concentration of AN6426. Resistant clones were characterized by whole-genome sequencing. Three generations of resistant parasites had polymorphisms in the predicted editing domain of the gene encoding a P. falciparum leucyl-tRNA synthetase (LeuRS; PF3D7_0622800) and in another gene (PF3D7_1218100), which encodes a protein of unknown function. Solution of the structure of the P. falciparum LeuRS editing domain suggested key roles for mutated residues in LeuRS editing. Short incubations with AN6426 and AN8432, unlike artemisinin, caused dose-dependent inhibition of [(14)C]leucine incorporation by cultured wild-type, but not resistant, parasites. The growth of resistant, but not wild-type, parasites was impaired in the presence of the unnatural amino acid norvaline, consistent with a loss of LeuRS editing activity in resistant parasites. In summary, the benzoxaboroles AN6426 and AN8432 offer effective antimalarial activity and act, at least in part, against a novel target, the editing domain of P. falciparum LeuRS.

Human upper-limb force capacities evaluation with robotic models for ergonomic applications: effect of elbow flexion.

The aim of this study was to apply models derived from the robotics field to evaluate the human upper-limb force generation capacity. Four models were compared: the force ellipsoid (FE) and force polytope (FP) based on unit joint torques and the scaled FE (SFE) and scaled FP (SFP) based on maximum isometric joint torques. The four models were assessed from four upper-limb postures with varying elbow flexion (40°, 60°, 80° and 100°) measured by an optoelectronic system and their corresponding isometric joint torques. Ten subjects were recruited. Three specific ellipsoids and polytopes parameters were compared: isotropy, principal force orientation and volume. Isotropy showed that the ellipsoids and polytopes were elongated. The angle between the two ellipsoids main axis and the two polytopes remained low but increased with the elbow flexion. The FE and FP volumes increased and those of SFE and SFP decreased with the elbow flexion. The interest and limits of such models are discussed in the framework of ergonomics and rehabilitation.

Toward isometric force capabilities evaluation by using a musculoskeletal model: Comparison with direct force measurement.

Developing formalisms to determine force capabilities of human limbs by using musculoskeletal models could be useful for biomechanical and ergonomic applications. In this framework, the purpose of this study was to compare measured maximal isometric force capabilities at the hand in a set of Cartesian directions with forces computed from a musculoskeletal model of the upper-limb. The results were represented under the form of a measured force polytope (MFP) and a musculoskeletal force polytope (MSFP). Both of them were obtained from the convex hull of measured and simulated force vectors endpoints. Nine subjects participated to the experiment. For one posture recorded with an optoelectronic system, maximum isometric forces exerted at the hand were recorded in twenty six directions of the Cartesian space with a triaxial force sensor. Results showed significant differences between the polytopes global shapes. The MSFP was more elongated than the MFP. Concerning the polytopes volumes, no significant difference was found. Mean maximal isometric forces provided by MFP and MSFP were 509.6 (118.4)N and 627.9 (73.3)N respectively. Moreover, the angle between the main axes of the two polytopes was 5.5 (2.3)° on average. Finally, RMS error values between MFP and MSFP were lower than 100N in 88% of the considered directions. The proposed MSFP based on a musculoskeletal model gave interesting information on optimal force orientation parameters. The possible applications in the frame of ergonomics, rehabilitation and biomechanics are proposed and discussed.