Early warning signals observed in motor activity preceding mood state change in bipolar disorder.

INTRODUCTION: Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes. METHODS: Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants' mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes. RESULTS: Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 h/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days. CONCLUSION: The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder.

Photoplethysmography-based derivation of physiological information using the BioPoint.

The BioPoint is a new wireless and wearable device, targeting both the ambulatory and on-site monitoring of biosignals. It is described as being capable of streaming and recording the i) electromyography, ii) electrocardiography, iii) electrodermal activity, iv) photoplethysmography, v) skin temperature and vi) actigraphy simultaneously, while making the raw signals recorded by the sensors readily available. However, an in-depth assessment of the biophysical signals recorded by this device, as well as its ability to derive vital signs and other health metrics, remains to be carried out. Consequently, this work proposes a preliminary study to evaluate the quality of the signals that can be acquired by this wearable with a focus on the derivation of heart rate and peripheral blood oxygenation via photoplethysmography. The device is quantitatively compared to the medical-grade pulse oximeter NoninConnect 3245, by Nonin inc. This study was performed with participants wearing the BioPoint at different positions on the body (finger, wrist, forearm, biceps and plantar arch), while the NoninConnect was worn on the fingertip and used as the ground truth. The results show that the BioPoint can accurately determine both heart rate and oxygen saturation from various locations on the body. However, as the BioPoint's photoplethysmograph is not calibrated it cannot be used for medical purposes (non-medical-grade).

Reduced heart rate variability during mania in a repeated naturalistic observational study.

Background: Bipolar disorder (BD) is a chronic recurrent mood disorder associated with autonomic nervous system (ANS) dysfunction, indexed by heart rate variability (HRV). Changes in HRV between mood states are sparsely studied longitudinally. We aimed to compare HRV of hospitalized manic individuals with their own euthymic selves in a naturalistic observational study. Methods: 34 individuals were included, of which 16 were lost to follow-up. Ultimately 15 patients provided reliable heart rate data in both a manic and euthymic state, using photoplethysmography (PPG) sensor wristbands overnight. We calculated HRV measures Root Mean Square of Successive Differences (RMSSD), High-frequency (HF: 0.15-0.40 Hz), Low-frequency (LF: 0.40-0.15 Hz), Very low-frequency (VLF: 0.0033-0.04 Hz), Total power and Sample Entropy in 5-min night-time resting samples. We compared HRV measures by mood state within individuals using paired t-tests and linear regression to control for age and sex. Results: HRV was lower in the manic state when compared to the euthymic state for all HRV metrics (p ≤ 0.02), with large to medium effect sizes (g = 1.24 to 0.65). HRV changes were not significantly affected by age or sex. Conclusion: This longitudinal study provides evidence of lower HRV in manic states compared to euthymia, indicating an association between ANS dysregulation and changes in bipolar mood state. This corroborates previous cross-sectional studies, although the association may be less clear or reversed in hypomanic states. Further investigation in larger longitudinal samples is warranted.

Latent space unsupervised semantic segmentation.

The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to easily work with both online and batch data. Latent Space Unsupervised Semantic Segmentation addresses the challenge of multivariate change-point detection by utilizing an autoencoder to learn a 1-dimensional latent space on which change-point detection is then performed. To address the challenge of real-time time series segmentation, this work introduces the Local Threshold Extraction Algorithm (LTEA) and a "batch collapse" algorithm. The "batch collapse" algorithm enables Latent Space Unsupervised Semantic Segmentation to process streaming data by dividing it into manageable batches, while Local Threshold Extraction Algorithm is employed to detect change-points in the time series whenever the computed metric by Latent Space Unsupervised Semantic Segmentation exceeds a predefined threshold. By using these algorithms in combination, our approach is able to accurately segment time series data in real-time, making it well-suited for applications where timely detection of changes is critical. When evaluating Latent Space Unsupervised Semantic Segmentation on a variety of real-world datasets the Latent Space Unsupervised Semantic Segmentation systematically achieves equal or better performance than other state-of-the-art change-point detection algorithms it is compared to in both offline and real-time settings.

Adherence Forecasting for Guided Internet-Delivered Cognitive Behavioral Therapy: A Minimally Data-Sensitive Approach.

Internet-delivered psychological treatments (IDPT) are seen as an effective and scalable pathway to improving the accessibility of mental healthcare. Within this context, treatment adherence is an especially pertinent challenge to address due to the reduced interaction between healthcare professionals and patients. In parallel, the increase in regulations surrounding the use of personal data, such as the General Data Protection Regulation (GDPR), makes data minimization a core consideration for real-world implementation of IDPTs. Consequently, this work proposes a Self-Attention-based deep learning approach to perform automatic adherence forecasting, while only relying on minimally sensitive login/logout-timestamp data. This approach was tested on a dataset containing 342 patients undergoing Guided Internet-delivered Cognitive Behavioral Therapy (G-ICBT) treatment. Of these 342 patients, 101 (  âˆ¼ 30%) were considered non-adherent (dropout) based on the adherence definition used in this work (i.e. at least eight connections to the platform lasting more than a minute over 56 days). The proposed model achieved over 70% average balanced accuracy, after only 20 out of the 56 days (  âˆ¼ 1/3) of the treatment had elapsed. This study demonstrates that automatic adherence forecasting for G-ICBT, is achievable using only minimally sensitive data, thus facilitating the implementation of such tools within real-world IDPT platforms.

Towards adaptive technology in routine mental health care.

This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients' needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.

Towards Autonomous Robotic Biopsy-Design, Modeling and Control of a Robot for Needle Insertion of a Commercial Full Core Biopsy Instrument.

This paper presents the design, control, and experimental evaluation of a novel fully automated robotic-assisted system for the positioning and insertion of a commercial full core biopsy instrument under guidance by ultrasound imaging. The robotic system consisted of a novel 4 Degree of freedom (DOF) add-on robot for the positioning and insertion of the biopsy instrument that is attached to a UR5-based teleoperation system with 6 DOF. The robotic system incorporates the advantages of both freehand and probe-guided biopsy techniques. The proposed robotic system can be used as a slave robot in a teleoperation configuration or as an autonomous or semi-autonomous robot in the future. While the UR5 manipulator was controlled using a teleoperation scheme with force controller, a reinforcement learning based controller using the Deep Deterministic Policy Gradient (DDPG) algorithm was developed for the add-on robotic system. The dexterous workspace analysis of the add-on robotic system demonstrated that the system has a suitable workspace within the US image. Two sets of comprehensive experiments including four experiments were performed to evaluate the robotic system's performance in terms of the biopsy instrument positioning, and the insertion of the needle inside the ultrasound plane. The experimental results showed the ability of the robotic system for in-plane needle insertion. The overall mean error of all four experiments in the tracking of the needle angle was 0.446°, and the resolution of the needle insertion was 0.002 mm.

Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder.

Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia.

Non-flipping DNA glycosylase AlkD scans DNA without formation of a stable interrogation complex.

The multi-step base excision repair (BER) pathway is initiated by a set of enzymes, known as DNA glycosylases, able to scan DNA and detect modified bases among a vast number of normal bases. While DNA glycosylases in the BER pathway generally bend the DNA and flip damaged bases into lesion specific pockets, the HEAT-like repeat DNA glycosylase AlkD detects and excises bases without sequestering the base from the DNA helix. We show by single-molecule tracking experiments that AlkD scans DNA without forming a stable interrogation complex. This contrasts with previously studied repair enzymes that need to flip bases into lesion-recognition pockets and form stable interrogation complexes. Moreover, we show by design of a loss-of-function mutant that the bimodality in scanning observed for the structural homologue AlkF is due to a key structural differentiator between AlkD and AlkF; a positively charged ß-hairpin able to protrude into the major groove of DNA.

A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators.

This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.

Semantic Active Visual Search System Based on Text Information for Large and Unknown Environments.

Different high-level robotics tasks require the robot to manipulate or interact with objects that are in an unexplored part of the environment or not already in its field of view. Although much works rely on searching for objects based on their colour or 3D context, we argue that text information is a useful and functional visual cue to guide the search. In this paper, we study the problem of active visual search (AVS) in large unknown environments. In this paper, we present an AVS system that relies on semantic information inferred from texts found in the environment, which allows the robot to reduce the search costs by avoiding not promising regions for the target object. Our semantic planner reasons over the numbers detected from door signs to decide either perform a goal-directed exploration towards unknown parts of the environment or carefully search in the already known parts. We compared the performance of our semantic AVS system with two other search systems in four simulated environments. First, we developed a greedy search system that does not consider any semantic information, and second, we invited human participants to teleoperate the robot while performing the search. Our results from simulation and real-world experiments show that text is a promising source of information that provides different semantic cues for AVS systems.

Correction: Dynamic modeling of soft continuum manipulators using lie group variational integration.

[This corrects the article DOI: 10.1371/journal.pone.0236121.].

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls.

Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.

Dynamic modeling of soft continuum manipulators using lie group variational integration.

This paper presents the derivation and experimental validation of algorithms for modeling and estimation of soft continuum manipulators using Lie group variational integration. Existing approaches are generally limited to static and quasi-static analyses, and are not sufficiently validated for dynamic motion. However, in several applications, models need to consider the dynamical behavior of the continuum manipulators. The proposed modeling and estimation formulation is obtained from a discrete variational principle, and therefore grants outstanding conservation properties to the continuum mechanical model. The main contribution of this article is the experimental validation of the dynamic model of soft continuum manipulators, including external torques and forces (e.g., generated by magnetic fields, friction, and the gravity), by carrying out different experiments with metal rods and polymer-based soft rods. To consider dissipative forces in the validation process, distributed estimation filters are proposed. The experimental and numerical tests also illustrate the algorithm's performance on a magnetically-actuated soft continuum manipulator. The model demonstrates good agreement with dynamic experiments in estimating the tip position of a Polydimethylsiloxane (PDMS) rod. The experimental results show an average absolute error and maximum error in tip position estimation of 0.13 mm and 0.58 mm, respectively, for a manipulator length of 60.55 mm.

Understanding Musical Predictions With an Embodied Interface for Musical Machine Learning.

Machine-learning models of music often exist outside the worlds of musical performance practice and abstracted from the physical gestures of musicians. In this work, we consider how a recurrent neural network (RNN) model of simple music gestures may be integrated into a physical instrument so that predictions are sonically and physically entwined with the performer's actions. We introduce EMPI, an embodied musical prediction interface that simplifies musical interaction and prediction to just one dimension of continuous input and output. The predictive model is a mixture density RNN trained to estimate the performer's next physical input action and the time at which this will occur. Predictions are represented sonically through synthesized audio, and physically with a motorized output indicator. We use EMPI to investigate how performers understand and exploit different predictive models to make music through a controlled study of performances with different models and levels of physical feedback. We show that while performers often favor a model trained on human-sourced data, they find different musical affordances in models trained on synthetic, and even random, data. Physical representation of predictions seemed to affect the length of performances. This work contributes new understandings of how musicians use generative ML models in real-time performance backed up by experimental evidence. We argue that a constrained musical interface can expose the affordances of embodied predictive interactions.

Guiding Neuroevolution with Structural Objectives.

The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives related to network structure has been shown to improve performance in some cases, especially when modular neural networks are beneficial. However, apart from objectives aiming to make networks more modular, such structural objectives have not been widely explored. We propose two new structural objectives and test their ability to guide evolving neural networks on two problems which can benefit from decomposition into subtasks. The first structural objective guides evolution to align neural networks with a user-recommended decomposition pattern. Intuitively, this should be a powerful guiding target for problems where human users can easily identify a structure. The second structural objective guides evolution towards a population with a high diversity in decomposition patterns. This results in exploration of many different ways to decompose a problem, allowing evolution to find good decompositions faster. Tests on our target problems reveal that both methods perform well on a problem with a very clear and decomposable structure. However, on a problem where the optimal decomposition is less obvious, the structural diversity objective is found to outcompete other structural objectives-and this technique can even increase performance on problems without any decomposable structure at all.

Additive manufacturing of laminar flow cells for single-molecule experiments.

A microfluidic laminar flow cell (LFC) forms an indispensable component in single-molecule experiments, enabling different substances to be delivered directly to the point under observation and thereby tightly controlling the biochemical environment immediately surrounding single molecules. Despite substantial progress in the production of such components, the process remains relatively inefficient, inaccurate and time-consuming. Here we address challenges and limitations in the routines, materials and the designs that have been commonly employed in the field, and introduce a new generation of LFCs designed for single-molecule experiments and assembled using additive manufacturing. We present single- and multi-channel, as well as reservoir-based LFCs produced by 3D printing to perform single-molecule experiments. Using these flow cells along with optical tweezers, we show compatibility with single-molecule experiments including the isolation and manipulation of single DNA molecules either attached to the surface of a coverslip or as freely movable DNA dumbbells, as well as direct observation of protein-DNA interactions. Using additive manufacturing to produce LFCs with versatility of design and ease of production allow experimentalists to optimize the flow cells to their biological experiments and provide considerable potential for performing multi-component single-molecule experiments.

Publisher Correction: Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V.

The original version of this Article was updated shortly after publication to add a link to the Peer Review file, which was inadvertently omitted. The Peer Review file is available to download as a Supplementary File from the HTML version of the Article.

Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V.

In order to preserve genomic stability, cells rely on various repair pathways for removing DNA damage. The mechanisms how enzymes scan DNA and recognize their target sites are incompletely understood. Here, by using high-localization precision microscopy along with 133 Hz high sampling rate, we have recorded EndoV and OGG1 interacting with 12-kbp elongated λ-DNA in an optical trap. EndoV switches between three distinct scanning modes, each with a clear range of activation energy barriers. These results concur with average diffusion rate and occupancy of states determined by a hidden Markov model, allowing us to infer that EndoV confinement occurs when the intercalating wedge motif is involved in rigorous probing of the DNA, while highly mobile EndoV may disengage from a strictly 1D helical diffusion mode and hop along the DNA. This makes EndoV the first example of a monomeric, single-conformation and single-binding-site protein demonstrating the ability to switch between three scanning modes.