Antiviral CD8+ T Cells Restricted by Human Leukocyte Antigen Class II Exist during Natural HIV Infection and Exhibit Clonal Expansion.

CD8+ T cell recognition of virus-infected cells is characteristically restricted by major histocompatibility complex (MHC) class I, although rare examples of MHC class II restriction have been reported in Cd4-deficient mice and a macaque SIV vaccine trial using a recombinant cytomegalovirus vector. Here, we demonstrate the presence of human leukocyte antigen (HLA) class II-restricted CD8+ T cell responses with antiviral properties in a small subset of HIV-infected individuals. In these individuals, T cell receptor ß (TCRß) analysis revealed that class II-restricted CD8+ T cells underwent clonal expansion and mediated killing of HIV-infected cells. In one case, these cells comprised 12% of circulating CD8+ T cells, and TCRα analysis revealed two distinct co-expressed TCRα chains, with only one contributing to binding of the class II HLA-peptide complex. These data indicate that class II-restricted CD8+ T cell responses can exist in a chronic human viral infection, and may contribute to immune control.

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior.

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities.

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

Acute psychosocial stress modulates neural and behavioral substrates of cognitive control.

Acute psychosocial stress affects learning, memory, and attention, but the evidence for the influence of stress on the neural processes supporting cognitive control remains mixed. We investigated how acute psychosocial stress influences performance and neural processing during the Go/NoGo task-an established cognitive control task. The experimental group underwent the Trier Social Stress Test (TSST) acute stress induction, whereas the control group completed personality questionnaires. Then, participants completed a functional magnetic resonance imaging (fMRI) Go/NoGo task, with self-report, blood pressure and salivary cortisol measurements of induced stress taken intermittently throughout the experimental session. The TSST was successful in eliciting a stress response, as indicated by significant Stress > Control between-group differences in subjective stress ratings and systolic blood pressure. We did not identify significant differences in cortisol levels, however. The stress induction also impacted subsequent Go/NoGo task performance, with participants who underwent the TSST making fewer commission errors on trials requiring the most inhibitory control (NoGo Green) relative to the control group, suggesting increased vigilance. Univariate analysis of fMRI task-evoked brain activity revealed no differences between stress and control groups for any region. However, using multivariate pattern analysis, stress and control groups were reliably differentiated by activation patterns contrasting the most demanding NoGo trials (i.e., NoGo Green trials) versus baseline in the medial intraparietal area (mIPA, affiliated with the dorsal attention network) and subregions of the cerebellum (affiliated with the default mode network). These results align with prior reports linking the mIPA and the cerebellum to visuomotor coordination, a function central to cognitive control processes underlying goal-directed behavior. This suggests that stressor-induced hypervigilance may produce a facilitative effect on response inhibition which is represented neurally by the activation patterns of cognitive control regions.

Gastrointestinal and genitourinary symptoms in facioscapulohumeral muscular dystrophy: Prevalence and impact.

INTRODUCTION/AIMS: Anecdotally, patients with facioscapulohumeral muscular dystrophy (FSHD) describe gastrointestinal (GI) and genitourinary (GU) symptoms. We explored the prevalence of GI and GU symptoms and their impact on quality of life (QOL) in people with FSHD compared to healthy household controls. METHODS: In this descriptive, cross-sectional study, we emailed a survey exploring GI and GU symptoms to all FSHD Society patient contacts (n = 3507). We invited those with FSHD and unaffected household controls to respond. Non-parametric statistics were used to compare symptom frequency and impact of symptoms between respondents with FSHD and household controls. Within the FSHD group, symptom frequency was assessed relative to measures of disease progression (need for ambulatory or respiratory support). RESULTS: Surveys from 701 respondents (652 with FSHD) ≥18 years old were included in analysis. Those with FSHD had symptoms affecting both GI and GU systems more frequently than controls using ordinal rating of symptom frequency. Within the FSHD group, more advanced disease was associated with increased symptom frequency. QOL was negatively impacted by the GI and GU symptoms. There was no difference between groups in use of medications to treat these symptoms. DISCUSSION: Recognition and treatment of GI and GU symptoms in people with FSHD, particularly those with more advanced disease, could improve QOL. Additional investigation is required to confirm these findings and understand the physiology.

Validity and Reliability of Thoracic-Mounted Inertial Measurement Units to Derive Gait Characteristics During Running.

ABSTRACT: Horsley, BJ, Tofari, PJ, Halson, SL, Kemp, JG, Chalkley, D, Cole, MH, Johnston, RD, and Cormack, SJ. Validity and reliability of thoracic-mounted inertial measurement units to derive gait characteristics during running. J Strength Cond Res 38(2): 274-282, 2024-Inertial measurement units (IMUs) attached to the tibia or lumbar spine can be used to analyze running gait but, with team-sports, are often contained in global navigation satellite system (GNSS) units worn on the thoracic spine. We assessed the validity and reliability of thoracic-mounted IMUs to derive gait characteristics, including peak vertical ground reaction force (vGRF peak ) and vertical stiffness (K vert ). Sixteen recreationally active subjects performed 40 m run throughs at 3-4, 5-6, and 7-8 m·s -1 . Inertial measurement units were attached to the tibia, lumbar, and thoracic spine, whereas 2 GNSS units were also worn on the thoracic spine. Initial contact (IC) from a validated algorithm was evaluated with F1 score and agreement (mean difference ± SD ) of gait data with the tibia and lumbar spine using nonparametric limits of agreement (LoA). Test-retest error {coefficient of variation, CV (95% confidence interval [CI])} established reliability. Thoracic IMUs detected a nearly perfect proportion (F1 ≥ 0.95) of IC events compared with tibia and lumbar sites. Step length had the strongest agreement (0 ± 0.04 m) at 3-4 m·s -1 , whereas contact time improved from 3 to 4 (-0.028 ± 0.018 second) to 7-8 m·s -1 (-0.004 ± 0.013 second). All values for K vert fell within the LoA at 7-8 m·s -1 . Test-retest error was ≤12.8% for all gait characteristics obtained from GNSS units, where K vert was most reliable at 3-4 m·s -1 (6.8% [5.2, 9.6]) and vGRF peak at 7-8 m·s -1 (3.7% [2.5, 5.2]). The thoracic-spine site is suitable to derive gait characteristics, including K vert , from IMUs within GNSS units, eliminating the need for additional sensors to analyze running gait.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists' preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.

Dorsal attention network activity during perceptual organization is distinct in schizophrenia and predictive of cognitive disorganization.

Visual shape completion is a canonical perceptual organization process that integrates spatially distributed edge information into unified representations of objects. People with schizophrenia show difficulty in discriminating completed shapes, but the brain networks and functional connections underlying this perceptual difference remain poorly understood. Also unclear is whether brain network differences in schizophrenia occur in related illnesses or vary with illness features transdiagnostically. To address these topics, we scanned (functional magnetic resonance imaging, fMRI) people with schizophrenia, bipolar disorder, or no psychiatric illness during rest and during a task in which they discriminated configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping was used to evaluate the likely involvement of resting-state connections for shape completion. Illusory/fragmented task activation differences ('modulations') in the dorsal attention network (DAN) could distinguish people with schizophrenia from the other groups (AUCs > .85) and could transdiagnostically predict cognitive disorganization severity. Activity flow over functional connections from the DAN could predict secondary visual network modulations in each group, except in schizophrenia. The secondary visual network was strongly and similarly modulated in each group. Task modulations were dispersed over more networks in patients compared to controls. In summary, DAN activity during visual perceptual organization is distinct in schizophrenia, symptomatically relevant, and potentially related to improper attention-related feedback into secondary visual areas.

Complement biomarkers in the antiphospholipid syndrome - Approaches to quantification and implications for clinical management.

Complement is a major driver of antiphospholipid syndrome (APS) and a promising therapeutic target in refractory and catastrophic APS. Complement testing in APS is largely limited to research settings, and reliable, rapid-turnaround biomarkers are needed to predict those at risk for adverse clinical outcomes and most likely to benefit from complement inhibition. We review complement biomarkers and their association with thrombosis and obstetric outcomes, including: (i) complement proteins and activation fragments in the fluid phase; (ii) assays that evaluate complement on cell membranes (e.g. in vivo cell-bound complement fragments, hemolytic assays, and ex vivo 'functional' cell-based assays, and (iii) sequencing of complement genes. Current studies highlight the inconsistencies in testing both between studies and various aPL/APS subgroups, suggesting that either cell-based testing or multiplex panels employing a combination of biomarkers simultaneously may be most clinically relevant. Standardization of complement assays is needed to ensure reproducibility and establish clinically relevant applications.

Global connectivity fingerprints predict the domain generality of multiple-demand regions.

A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brain's intrinsic network architecture. Specifically, we tested the possibility that each brain region's domain generality is reflected in its level of global (hub-like) intrinsic connectivity as well as its particular global connectivity pattern ("connectivity fingerprint"). Consistent with prior work, we found that cognitive control networks exhibited domain generality as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state functional magnetic resonance imaging [fMRI]) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control region's unique rule response profile("information fingerprint") could be predicted based on its unique intrinsic connectivity fingerprint and the information content in regions outside cognitive control networks. Together, these results suggest that the human brain's intrinsic network architecture supports its ability to represent diverse cognitive task information largely via the location of multiple-demand regions within the brain's global network organization.

How Reliable and Valid are Dual-Task Cost Metrics? A Meta-analysis of Locomotor-Cognitive Dual-Task Paradigms.

OBJECTIVE: To assess the retest reliability, predictive validity, and concurrent validity of locomotor and cognitive dual-task cost (DTC) metrics derived from locomotor-cognitive dual-task paradigms. DATA SOURCES: A literature search of electronic databases (PubMed, PsycINFO, MEDLINE, CINAHL, and Scopus) was conducted on May 29th, 2021, without time restriction. STUDY SELECTION: For 1559 search results, titles and abstracts were screened by a single reviewer and full text of potentially eligible papers was considered by 2 independent reviewers. 25 studies that evaluated retest reliability, predictive validity, and concurrent validity of locomotor-cognitive DTC in healthy and clinical groups met inclusion criteria. DATA EXTRACTION: Study quality was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instrument checklist. Data relating to the retest reliability, predictive validity, and concurrent validity of DTC were extracted. DATA SYNTHESIS: Meta-analysis showed that locomotor DTC metrics (intraclass correlation coefficient [ICC]=0.61, 95% confidence interval [CI; 0.53.0.70]) had better retest reliability than cognitive DTC metrics (ICC=0.27, 95% CI [0.17.0.36]). Larger retest reliability estimates were found for temporal gait outcomes (ICC=0.67-0.72) compared with spatial (ICC=0.34-0.53). Motor DTC metrics showed weak predictive validity for the incidence of future falls (r=0.14, 95% CI [-0.03.0.31]). Motor DTC metrics had weak concurrent validity with other clinical and performance assessments (r=0.11, 95% CI [0.07.0.16]), as did cognitive DTC metrics (r=0.19, 95% CI [0.08.0.30]). CONCLUSIONS: Gait-related temporal DTC metrics achieve adequate retest reliability, while predictive and concurrent validity of DTC needs to be improved before being used widely in clinical practice and other applied settings. Future research should ensure the reliability and validity of DTC outcomes before being used to assess dual-task interference.

Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing.

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission's success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0-6" (CP06) (R2/RMSE = 0.95/67) and 0-12" depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.

The Functional Relevance of Task-State Functional Connectivity.

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping-an approach for building empirically derived network models-to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.SIGNIFICANCE STATEMENT Human cognition is highly dynamic, yet the functional network organization of the human brain is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the intrinsic (resting-state) network organization of the brain strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics.

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first "filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.

Developing a novel device, Eggcell, to improve temperature stability during oocyte collection for IVF.

RESEARCH QUESTION: What temperature fluctuations are oocytes exposed to during oocyte retrieval? Can an alternative method of oocyte retrieval be designed to minimize these fluctuations? DESIGN: Mock oocyte retrieval procedures were performed to investigate the change in temperature when the follicular fluid is drained into collection tubes and when the fluid is subsequently poured into dishes to allow identification of the cumulus-oocyte complex (COC). A new device, the Eggcell, has been designed that addresses the problem of these temperature fluctuations. To confirm its safety and demonstrate the clinical applicability of Eggcell, laboratory validation was performed prior to use with human participants (n = 15). RESULTS: Eggcell meets its design specification to provide temperature stability within the physiological range for aspirated follicular fluid. The COC can be successfully retained within the chamber (n = 180) without evidence of loss or damage to the oocytes or compromise of fertilization rate, blastocyst development or clinical outcome. CONCLUSIONS: This study has demonstrated the successful first stages of development of a new medical device. Further studies are needed for comparative evaluation of clinical outcome with standard technology.

A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction.

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.

Acute effect of traditional and adaptive metronomes on gait variability in older individuals with a history of falls.

BACKGROUND: Metronome cueing has been shown to reduce gait variability and thereby potentially reduce falls risk in individuals with Parkinson's disease. It is unclear however, if metronome cueing has a similar effect in healthy older adults with a history of falls. AIM: To investigate whether a traditional and/or an adaptive metronome, based on an individual's gait pattern, were effective in reducing gait variability in older adults with a history of falls. METHODS: Twenty older adults (15 women, 71 ± 4.9 years) with a history of falls were included in this cross-over study. Participants received two types of cueing (adaptive and traditional metronome) 1 week apart. The variability of the participants' stride time, stride length, walking speed and duration of double leg support were recorded during three walking conditions (baseline, during feedback and post-feedback gait). Repeated-measures ANOVA was used to assess the possible effects of the two cueing strategies on gait variables. RESULTS: Compared with the baseline condition, participants had significantly increased stride time variability during feedback (F (2) = 9.83, p < 0.001) and decreased double leg support time variability post-feedback (F (2) 3.69, p = 0.034). Increased stride time variability was observed with the adaptive metronome in comparison to the traditional metronome. CONCLUSION: Metronome cueing strategies may reduce double leg support variability in older adults with a history of falls but seem to increase stride time variability. Further studies are needed to investigate if metronome cueing is more beneficial for individuals with greater baseline gait variability than those included in the current study.

Gait biofeedback training in people with Parkinson's disease: a pilot study.

BACKGROUND: People with Parkinson's disease (PD) are at a high risk of falls, with ~ 60% experiencing a fall each year. Greater mediolateral head and pelvis motion during gait are known to increase the risk of falling in PD, however the ability to modify these aspects of gait has not been examined. Thus, this study aimed to examine whether mediolateral trunk, head and pelvis motion during walking could be successfully decreased in people with PD using real-time biofeedback. METHODS: Participants were provided with real-time biofeedback regarding their mediolateral trunk lean via a visual projection whilst walking along an 8-m indoor walkway. Using the feedback provided, they were asked to reduce the magnitude of their mediolateral trunk lean. Gait was recorded for four conditions (i) Baseline, (ii) Intervention, (iii) immediately Post-Intervention, and (iv) 1-week Follow-Up. Biomechanical variables associated with falls risk were compared between conditions, including normalised mediolateral motion, gait velocity and stride length. RESULTS: A reduction in mediolateral trunk lean, step length and gait velocity from Baseline to the Intervention and Post-intervention conditions was observed. Contrary to this, increased normalised ML pelvis and trunk motion was observed between the Baseline and Intervention conditions, but returned to Baseline levels in the Post-Intervention condition. CONCLUSIONS: Results from the current study suggest that real-time visual biofeedback may be effective at modifying specific gait characteristics that are associated with falls in PD. Further research is required to better understand the influence of this intervention approach on falls incidence. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12620000994987. Registered 10 June 2020 - Retrospectively registered, https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380324.

Modeling Human Steering Behavior in Haptic Shared Control of Autonomy-Enabled Unmanned Ground Vehicles.

OBJECTIVE: A human steering model for teleoperated driving is extended to capture the human steering behavior in haptic shared control of autonomy-enabled Unmanned Ground Vehicles (UGVs). BACKGROUND: Prior studies presented human steering models for teleoperation of a passenger-sized Unmanned Ground Vehicle, where a human is fully in charge of driving. However, these models are not applicable when a human needs to interact with autonomy in haptic shared control of autonomy-enabled UGVs. How a human operator reacts to the presence of autonomy needs to be studied and mathematically encapsulated in a module to capture the collaboration between human and autonomy. METHOD: Human subject tests are conducted to collect data in haptic shared control for model development and validation. The ACT-R architecture and two-point steering model used in the previous literature are adopted to predict the operator's desired steering angle. A torque conversion module is developed to convert the steering command from the ACT-R model to human torque input, thus enabling haptic shared control with autonomy. A parameterization strategy is described to find the set of model parameters that optimize the haptic shared control performance in terms of minimum average lane keeping error (ALKE). RESULTS: The model predicts the minimum ALKE human subjects achieve in shared control. CONCLUSIONS: The extended model can successfully predict the best haptic shared control performance as measured by ALKE. APPLICATION: This model can be used in place of human operators, enabling fully simulation-based engineering, in the development and evaluation of haptic shared control technologies for autonomy-enabled UGVs, including control negotiation strategies and autonomy capabilities.

Innovation, carbon emissions and the pollution haven hypothesis: Climate capitalism and global re-interpretations.

This article considers impacts from innovation, defined in terms of research and development expenditure, on carbon emissions. We relate our study to scholarship about the Environmental Kuznets Curve and the Pollution Haven Hypothesis, situating this analysis within literature about the compatibility of broadly capitalist systems and combating climate change. We thus incorporate scholarship surrounding themes such as climate capitalism and ecological modernization. There are three main research questions. First, what is the impact of increasing levels of innovation on emissions? Second, how does the level of economic development affect impacts from greater innovation on emissions? Third, does this analysis generate evidence to support the Pollution Haven Hypothesis? To test these questions, and three parallel hypotheses, we initially deployed a panel data model, based on World Bank data, incorporating control variables covering economic, spatial and environmental factors. We then split the country sample into two GDP-based cohorts to test for variations in effects related to economic development. Subsequently, a multi-input regional-output model was deployed to incorporate analysis of a pollution haven effect. Our analysis suggests that whilst greater innovation diminished carbon dioxide emissions for high-income countries, this effect could not be identified elsewhere. Furthermore, the multi-input regional-output model implied that explanations for these contrasting results might lie in a pollution haven effect. Overall, this study implied some acutely limited support for climate capitalism and ecological modernization, constructed on data from high-income countries alone.