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.

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.

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).

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.

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.

Discovery of a novel class of boron-based antibacterials with activity against gram-negative bacteria.

Gram-negative bacteria cause approximately 70% of the infections in intensive care units. A growing number of bacterial isolates responsible for these infections are resistant to currently available antibiotics and to many in development. Most agents under development are modifications of existing drug classes, which only partially overcome existing resistance mechanisms. Therefore, new classes of Gram-negative antibacterials with truly novel modes of action are needed to circumvent these existing resistance mechanisms. We have previously identified a new a way to inhibit an aminoacyl-tRNA synthetase, leucyl-tRNA synthetase (LeuRS), in fungi via the oxaborole tRNA trapping (OBORT) mechanism. Herein, we show how we have modified the OBORT mechanism using a structure-guided approach to develop a new boron-based antibiotic class, the aminomethylbenzoxaboroles, which inhibit bacterial leucyl-tRNA synthetase and have activity against Gram-negative bacteria by largely evading the main efflux mechanisms in Escherichia coli and Pseudomonas aeruginosa. The lead analogue, AN3365, is active against Gram-negative bacteria, including Enterobacteriaceae bearing NDM-1 and KPC carbapenemases, as well as P. aeruginosa. This novel boron-based antibacterial, AN3365, has good mouse pharmacokinetics and was efficacious against E. coli and P. aeruginosa in murine thigh infection models, which suggest that this novel class of antibacterials has the potential to address this unmet medical need.

104 MHz rate single-shot recording with subpicosecond resolution using temporal imaging.

We demonstrate temporal imaging for the measurement and characterization of optical arbitrary waveforms and events. The system measures single-shot 200 ps frames at a rate of 104 MHz, where each frame is time magnified by a factor of -42.4x. Impulse response tests show that the system enables 783 fs resolution when placed at the front end of a 20 GHz oscilloscope. Modulated pulse trains characterize the system's impulse response, jitter, and frame-to-frame variation.

Discovery and structure-activity relationships of 6-(benzoylamino)benzoxaboroles as orally active anti-inflammatory agents.

Structure-activity relationships of 6-(benzoylamino)benzoxaborole analogs were investigated for the inhibition of TNF-α, IL-1ß, and IL-6 from lipopolysaccharide stimulated peripheral blood mononuclear cells. Compound 1q showed potent activity against all three cytokines with IC50 values between 0.19 and 0.50µM, inhibited LPS-induced TNF-α and IL-6 elevation in mice and improved collagen-induced arthritis in mice. Compound 1q (AN4161) is considered to be a promising lead for novel anti-inflammatory agent with an excellent pharmacokinetic profile.

Synthesis and antibacterial evaluation of a novel tricyclic oxaborole-fused fluoroquinolone.

We have designed and synthesized a novel class of compounds based on fluoroquinolone antibacterial prototype. The design concept involved the replacement of the 3-carboxylic acid in ciprofloxacin with an oxaborole-fused ring as an acid-mimicking group. The synthetic method employed in this work provides a good example of incorporating boron atom in complex molecules with multiple functional groups. The antibacterial activity of the newly synthesized compounds has been evaluated.

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.

Design and synthesis of boron-containing PDE4 inhibitors using soft-drug strategy for potential dermatologic anti-inflammatory application.

PDE4 inhibitors are a validated approach as anti-inflammatory agents but are limited by systemic side effects including emesis. We report a soft-drug strategy incorporating a carboxylic ester group into boron-containing PDE4 inhibitors leading to the discovery of a series of benzoxaborole compounds with good potency (for example IC(50)=47 nM of compound 2) and low emetic activity. These compounds are intended for dermatological use further limiting possible systemic side effects.

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.

Discovery and structure-activity study of a novel benzoxaborole anti-inflammatory agent (AN2728) for the potential topical treatment of psoriasis and atopic dermatitis.

A series of phenoxy benzoxaboroles were synthesized and screened for their inhibitory activity against PDE4 and cytokine release. 5-(4-Cyanophenoxy)-2,3-dihydro-1-hydroxy-2,1-benzoxaborole (AN2728) showed potent activity both in vitro and in vivo. This compound is now in clinical development for the topical treatment of psoriasis and being pursued for the topical treatment of atopic dermatitis.

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.

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.

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%).