Clinical Evaluation of Next-generation, Multi-weight Hyaluronic Acid Plus Antioxidant Complex-based Topical Formulations with Targeted Delivery to Enhance Skin Rejuvenation.

Introduction: Hyaluronic acid (HA) has become a commonly used ingredient in many topical products due to its strong humectant properties and essential role in skin hydration; however, limitations of delivery of HA to only the surface of skin has hindered leveraging the full capacity of HA biology necessary for skin rejuvenation. Here, we describe the clinical efficacy data of a set of novel next-generation, multi-weight HA plus antioxidant complex-based topical formulations with targeted skin delivery to enhance skin rejuvenation. Methods: Four multi-weight HA plus antioxidant complex-based formulations: 1) Multi-Weight HA plus Antioxidant Complex Lotion with SPF 30 (Day Lotion); 2) Multi-Weight HA plus Antioxidant Complex Cream (Night Cream); 3) Multi-Weight HA plus Antioxidant Complex Gel Cream; and 4) Multi-Weight HA plus Antioxidant Complex Boost Serum were clinically evaluated for key attributes including moisturization via corneometer, with clinical grading of: dryness, roughness, fine lines and wrinkles, and following daily use of the individual products for up to eight weeks. Results: Daily use of the multi-weight HA plus antioxidant complex-based formulations demonstrated significant improvements in all parameters evaluated compared to baselines, with changes in moisturization observed within 30 minutes of application, and changes in clinical grading parameters of dryness, roughness, fine lines and wrinkles observed as early as two weeks. Conclusion: These data demonstrate the clinical benefits of daily use of multi-weight HA plus antioxidant complex-based moisturizers for overall improvement in skin health and appearance.

Evaluation of Antioxidants' Ability to Enhance Hyaluronic-acid Based Topical Moisturizers.

Background: Hyaluronic acid (HA) is a unique molecule of the extracellular matrix with multiple biological activities. In skin, HA plays an essential role as a humectant, capable of binding up to 1,000 times its mass with water, providing skin with moisture and viscoelastic properties. HA concentration and synthesis decrease significantly in aging skin, due to exogenous and endogenous factors, including photoaging and HA metabolism. A key driver for HA degradation and reduced concentration is mediated via induction of reactive oxygen species (ROS) and other free radicals. Objective: In this study, we evaluate antioxidant ingredients essential in the development of next-generation HA-based topical formulations aimed at leveraging HA's ability to maximize anti-aging properties. Methods: Two antioxidants, glycine saponin (Glycine soja germ extract) and glycyrrhetinic acid (enoxolone), were evaluated for stimulation of endogenous HA production and inhibition of endogenous hyaluronidase activity, respectively. Results: The antioxidant glycine saponin induced endogenous HA synthesis in fibroblasts, while the antioxidant glycyrrhetinic acid decreased the degradation rate of HA by 54 percent. Conclusion: While HA has been included in numerous topical skin products, critical aspects of HA metabolism, especially in aging skin, have often been overlooked, including decreases in HA synthesis with increasing age, and increases in HA degradation mediated by exogenously induced reactive oxygen species and free radicals and increased enzymatic degradation by endogenous hyaluronidases. Here, we describe a unique approach to inclusion of two antioxidants essential for the development of the next generation of antioxidant complex-based topical skin formulations to limit the signs of aging skin.

Human-AI teams-Challenges for a team-centered AI at work.

As part of the Special Issue topic "Human-Centered AI at Work: Common Ground in Theories and Methods," we present a perspective article that looks at human-AI teamwork from a team-centered AI perspective, i. e., we highlight important design aspects that the technology needs to fulfill in order to be accepted by humans and to be fully utilized in the role of a team member in teamwork. Drawing from the model of an idealized teamwork process, we discuss the teamwork requirements for successful human-AI teaming in interdependent and complex work domains, including e.g., responsiveness, situation awareness, and flexible decision-making. We emphasize the need for team-centered AI that aligns goals, communication, and decision making with humans, and outline the requirements for such team-centered AI from a technical perspective, such as cognitive competence, reinforcement learning, and semantic communication. In doing so, we highlight the challenges and open questions associated with its implementation that need to be solved in order to enable effective human-AI teaming.

Experimental Investigations into Using Motion Capture State Feedback for Real-Time Control of a Humanoid Robot.

Regardless of recent advances, humanoid robots still face significant difficulties in performing locomotion tasks. Among the key challenges that must be addressed to achieve robust bipedal locomotion are dynamically consistent motion planning, feedback control, and state estimation of such complex systems. In this paper, we investigate the use of an external motion capture system to provide state feedback to an online whole-body controller. We present experimental results with the humanoid robot RH5 performing two different whole-body motions: squatting with both feet in contact with the ground and balancing on one leg. We compare the execution of these motions using state feedback from (i) an external motion tracking system and (ii) an internal state estimator based on inertial measurement unit (IMU), forward kinematics, and contact sensing. It is shown that state-of-the-art motion capture systems can be successfully used in the high-frequency feedback control loop of humanoid robots, providing an alternative in cases where state estimation is not reliable.

Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes.

The economic and social impacts due to diseases transmitted by mosquitoes in the latest years have been significant. Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and -classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases.

Effects of Colloidal Oatmeal Topical Atopic Dermatitis Cream on Skin Microbiome and Skin Barrier Properties.

Atopic dermatitis is characterized by dry, itchy, inflamed skin with a dysbiotic microbiome. In this clinical study (NCT03673059), we compared the effects of an eczema cream containing 1% colloidal oat and a standard moisturizer on the skin microbiome and skin barrier function of patients with mild to moderate eczema. Patients were randomly assigned to treatment with 1% colloidal oat eczema cream or a standard, non-fragranced daily moisturizer. Treatment lasted 14 days, followed by a 7-day regression period. Of 61 patients who completed the study, 30 received the 1% colloidal oat eczema cream and 31 received the standard moisturizer. At 14 days, the 1% colloidal oat eczema cream reduced mean Eczema Area Severity Index and Atopic Dermatitis Severity Index scores by 51% and 54%, respectively. Unlike treatment with the standard moisturizer, treatment with the 1% colloidal oat eczema cream was associated with trends towards lower prevalence of Staphylococcus species and higher microbiome diversity at lesion sites. The 1% colloidal oat eczema cream significantly improved skin pH, skin barrier function, and skin hydration from baseline to day 14, whereas the standard moisturizer improved hydration. Overall, the results demonstrate that topical products can have differing effects on the skin barrier properties and the microbiome. Importantly, we show that the use of a 1% colloidal oat eczema cream improves microbiome composition and significantly repairs skin barrier defects. J Drugs Dermatol. 2020;19(5):   doi:10.36849/JDD.2020.4924.

Errors in Human-Robot Interactions and Their Effects on Robot Learning.

During human-robot interaction, errors will occur. Hence, understanding the effects of interaction errors and especially the effect of prior knowledge on robot learning performance is relevant to develop appropriate approaches for learning under natural interaction conditions, since future robots will continue to learn based on what they have already learned. In this study, we investigated interaction errors that occurred under two learning conditions, i.e., in the case that the robot learned without prior knowledge (cold-start learning) and in the case that the robot had prior knowledge (warm-start learning). In our human-robot interaction scenario, the robot learns to assign the correct action to a current human intention (gesture). Gestures were not predefined but the robot had to learn their meaning. We used a contextual-bandit approach to maximize the expected payoff by updating (a) the current human intention (gesture) and (b) the current human intrinsic feedback after each action selection of the robot. As an intrinsic evaluation of the robot behavior we used the error-related potential (ErrP) in the human electroencephalogram as reinforcement signal. Either gesture errors (human intentions) can be misinterpreted by incorrectly captured gestures or errors in the ErrP classification (human feedback) can occur. We investigated these two types of interaction errors and their effects on the learning process. Our results show that learning and its online adaptation was successful under both learning conditions (except for one subject in cold-start learning). Furthermore, warm-start learning achieved faster convergence, while cold-start learning was less affected by online changes in the current context.

Application of convolutional neural networks for classification of adult mosquitoes in the field.

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.

Expression of cutaneous immunity markers during infant skin maturation.

BACKGROUND/OBJECTIVES: Infant skin undergoes a maturation process during the early years of life. Little is known about the skin's innate immunity. We investigated the dynamics of innate immunity markers collected from the surface of infant skin during the first 36 months of life. METHODS: A total of 117 healthy infants aged 3-36 months participated in the study. We extracted human beta defensin-1 and interleukin 1 alpha and its receptor antagonist using transdermal analysis patches from the skin surface of the posterior lower leg area. The extracts were analyzed using a spot enzyme-linked immunosorbent assay. RESULTS: Skin surface human beta defensin-1 levels were higher early in life and decreased with infant age. The ratio of interleukin 1 alpha receptor antagonist to interleukin 1 alpha did not change significantly with age but showed a distinct difference between sexes, with female infants having higher values than male infants. CONCLUSION: As is the case with skin structure and functional properties, cutaneous innate immunity also appears to undergo a maturation period during infancy, with innate immunity slowly declining as adaptive immunity takes over. Sex differences in immune markers may explain sex-dependent susceptibilities to infection.

The BesMan Learning Platform for Automated Robot Skill Learning.

We describe the BesMan learning platform which allows learning robotic manipulation behavior. It is a stand-alone solution which can be combined with different robotic systems and applications. Behavior that is adaptive to task changes and different target platforms can be learned to solve unforeseen challenges and tasks, which can occur during deployment of a robot. The learning platform is composed of components that deal with preprocessing of human demonstrations, segmenting the demonstrated behavior into basic building blocks, imitation, refinement by means of reinforcement learning, and generalization to related tasks. The core components are evaluated in an empirical study with 10 participants with respect to automation level and time requirements. We show that most of the required steps for transferring skills from humans to robots can be automated and all steps can be performed in reasonable time allowing to apply the learning platform on demand.

Integrated Mechanical, Thermal, Data, and Power Transfer Interfaces for Future Space Robotics.

In-situ connectability among modules of a space system can provide significantly enhanced flexibility, adaptability, and robustness for space exploration and servicing missions. Connection of modules in extra-terrestrial environment is hence a topic of rising importance in modern orbital or planetary missions. As an example, the increasing number of satellites sent to space have introduced a large set of connections of various type, for transferring mechanical loads, data, electrical power and heat from one module to another. This paper provides a comprehensive review of published work in space robotic connections and presents the different transfer types developed and used to date in robotic applications for orbital and extra-terrestrial planetary missions. The aims of this paper are to present a detailed analysis of the state of the art available technologies, to make an analysis of and comparison among different solutions to common problems, to synthesize and identify future connectability research, and to lay the foundation for future European space robotic connectability effort and work for a complex and growing important future space missions. All types are described in their base characteristics and evaluated for orbital and planetary environments. This analysis shows that despite the large number of connectors developed for each of the four functionalities (mechanical, thermal, data, and electrical power) here considered, the trend is that researchers are integrating more than one functionalizes into a single equipment or device, to reduce costs and improve standardization. The outcomes of this literature review have contributed toward the design of a future multifunctional, standard and scalable interface at the early stage of the Standard Interface for Robotic Manipulation of Payloads in Future Space Missions (SIROM) project, a European Commission funded Horizon 2020 project. SIROM interfaces will be employed by European prime contractors in future extra-terrestrial missions.

Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient's upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.

An Intelligent Man-Machine Interface-Multi-Robot Control Adapted for Task Engagement Based on Single-Trial Detectability of P300.

Advanced man-machine interfaces (MMIs) are being developed for teleoperating robots at remote and hardly accessible places. Such MMIs make use of a virtual environment and can therefore make the operator immerse him-/herself into the environment of the robot. In this paper, we present our developed MMI for multi-robot control. Our MMI can adapt to changes in task load and task engagement online. Applying our approach of embedded Brain Reading we improve user support and efficiency of interaction. The level of task engagement was inferred from the single-trial detectability of P300-related brain activity that was naturally evoked during interaction. With our approach no secondary task is needed to measure task load. It is based on research results on the single-stimulus paradigm, distribution of brain resources and its effect on the P300 event-related component. It further considers effects of the modulation caused by a delayed reaction time on the P300 component evoked by complex responses to task-relevant messages. We prove our concept using single-trial based machine learning analysis, analysis of averaged event-related potentials and behavioral analysis. As main results we show (1) a significant improvement of runtime needed to perform the interaction tasks compared to a setting in which all subjects could easily perform the tasks. We show that (2) the single-trial detectability of the event-related potential P300 can be used to measure the changes in task load and task engagement during complex interaction while also being sensitive to the level of experience of the operator and (3) can be used to adapt the MMI individually to the different needs of users without increasing total workload. Our online adaptation of the proposed MMI is based on a continuous supervision of the operator's cognitive resources by means of embedded Brain Reading. Operators with different qualifications or capabilities receive only as many tasks as they can perform to avoid mental overload as well as mental underload.

An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials.

GOAL: Current brain-computer interfaces (BCIs) are usually based on various, often supervised, signal processing methods. The disadvantage of supervised methods is the requirement to calibrate them with recently acquired subject-specific training data. Here, we present a novel algorithm for dimensionality reduction (spatial filter), that is ideally suited for single-trial detection of event-related potentials (ERPs) and can be adapted online to a new subject to minimize or avoid calibration time. METHODS: The algorithm is based on the well-known xDAWN filter, but uses generalized eigendecomposition to allow an incremental training by recursive least squares (RLS) updates of the filter coefficients. We analyze the effectiveness of the spatial filter in different transfer scenarios and combinations with adaptive classifiers. RESULTS: The results show that it can compensate changes due to switching between different users, and therefore allows to reuse training data that has been previously recorded from other subjects. CONCLUSIONS: The presented approach allows to reduce or completely avoid a calibration phase and to instantly use the BCI system with only a minor decrease of performance. SIGNIFICANCE: The novel filter can adapt a precomputed spatial filter to a new subject and make a BCI system user independent.

Automatic classification of epilepsy types using ontology-based and genetics-based machine learning.

OBJECTIVES: In the presurgical analysis for drug-resistant focal epilepsies, the definition of the epileptogenic zone, which is the cortical area where ictal discharges originate, is usually carried out by using clinical, electrophysiological and neuroimaging data analysis. Clinical evaluation is based on the visual detection of symptoms during epileptic seizures. This work aims at developing a fully automatic classifier of epileptic types and their localization using ictal symptoms and machine learning methods. METHODS: We present the results achieved by using two machine learning methods. The first is an ontology-based classification that can directly incorporate human knowledge, while the second is a genetics-based data mining algorithm that learns or extracts the domain knowledge from medical data in implicit form. RESULTS: The developed methods are tested on a clinical dataset of 129 patients. The performance of the methods is measured against the performance of seven clinicians, whose level of expertise is high/very high, in classifying two epilepsy types: temporal lobe epilepsy and extra-temporal lobe epilepsy. When comparing the performance of the algorithms with that of a single clinician, who is one of the seven clinicians, the algorithms show a slightly better performance than the clinician on three test sets generated randomly from 99 patients out of the 129 patients. The accuracy obtained for the two methods and the clinician is as follows: first test set 65.6% and 75% for the methods and 56.3% for the clinician, second test set 66.7% and 76.2% for the methods and 61.9% for the clinician, and third test set 77.8% for the methods and the clinician. When compared with the performance of the whole population of clinicians on the rest 30 patients out of the 129 patients, where the patients were selected by the clinicians themselves, the mean accuracy of the methods (60%) is slightly worse than the mean accuracy of the clinicians (61.6%). Results show that the methods perform at the level of experienced clinicians, when both the methods and the clinicians use the same information. CONCLUSION: Our results demonstrate that the developed methods form important ingredients for realizing a fully automatic classification of epilepsy types and can contribute to the definition of signs that are most important for the classification.

Integration of fiber-optic sensor arrays into a multi-modal tactile sensor processing system for robotic end-effectors.

With the increasing complexity of robotic missions and the development towards long-term autonomous systems, the need for multi-modal sensing of the environment increases. Until now, the use of tactile sensor systems has been mostly based on sensing one modality of forces in the robotic end-effector. The use of a multi-modal tactile sensory system is motivated, which combines static and dynamic force sensor arrays together with an absolute force measurement system. This publication is focused on the development of a compact sensor interface for a fiber-optic sensor array, as optic measurement principles tend to have a bulky interface. Mechanical, electrical and software approaches are combined to realize an integrated structure that provides decentralized data pre-processing of the tactile measurements. Local behaviors are implemented using this setup to show the effectiveness of this approach.

Object recognition and localization: the role of tactile sensors.

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.

Incremental learning of skill collections based on intrinsic motivation.

Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period.