Complement(ing) long-COVID thromboinflammation and pathogenesis.

The persistence or recurrence of symptoms after acute SARS-CoV-2 infection, termed 'long COVID', presents a formidable challenge to global healthcare systems. Recent research by Cervia-Hasler and colleagues delves into the intricate immunological landscape in patients with long COVID, demonstrating an interplay between complement and coagulation, driven by antiviral antibodies and tissue damage.

Complement C5a Receptor Signaling Alters Stress Responsiveness and Modulates Microglia Following Chronic Stress Exposure.

Background: Accumulating evidence underscores the pivotal role of heightened inflammation in the pathophysiology of stress-related diseases, but the underlying mechanisms remain elusive. The complement system, a key effector of the innate immune system, produces the C5-cleaved activation product C5a upon activation, initiating inflammatory responses through the canonical C5a receptor 1 (C5aR1). While C5aR1 is expressed in stress-responsive brain regions, its role in stress responsiveness remains unknown. Methods: To investigate C5a-C5aR1 signaling in stress responses, mice underwent acute and chronic stress paradigms. Circulating C5a levels and messenger RNA expression of C5aR1 in the hippocampus and adrenal gland were measured. C5aR1-deficient mice were used to elucidate the effects of disrupted C5a-C5aR1 signaling across behavioral, hormonal, metabolic, and inflammation parameters. Results: Chronic restraint stress elevated circulating C5a levels while reducing C5aR1 messenger RNA expression in the hippocampus and adrenal gland. Notably, the absence of C5aR1 signaling enhanced adrenal sensitivity to adrenocorticotropic hormone, concurrently reducing pituitary adrenocorticotropic hormone production and enhancing the response to acute stress. C5aR1-deficient mice exhibited attenuated reductions in locomotor activity and body weight under chronic stress. Additionally, these mice displayed increased glucocorticoid receptor sensitivity and disrupted glucose and insulin homeostasis. Chronic stress induced an increase in C5aR1-expressing microglia in the hippocampus, a response mitigated in C5aR1-deficient mice. Conclusions: C5a-C5aR1 signaling emerges as a key metabolic regulator during stress, suggesting that complement activation and dysfunctional C5aR1 signaling may contribute to neuroinflammatory phenotypes in stress-related disorders. The results advocate for further exploration of complement C5aR1 as a potential therapeutic target for stress-related conditions.

How the immune system, particularly the complement system, influences responses to stress has not been fully clear. In this study, we focus on C5a-C5aR1 signaling, a part of the immune system, and found that it significantly affects stress-related reactions in mice. In chronic stress, we observed increased inflammation, altered hormonal responses, and disrupted metabolic regulation. Mice lacking C5aR1 showed reduced stress-induced behavioral changes, indicating that this receptor may play a vital role in modulating the stress response. Understanding these immune mechanisms sheds light on stress-related disorders and may open avenues for therapeutic interventions.

A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis.

The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning. New breakthroughs in data-driven architectures and optimization techniques for fast surrogate modeling provide an exciting opportunity to overcome these limitations, enabling the use of such technology for time-critical decisions. We discuss an application to the repair of multiple stenosis in peripheral pulmonary artery disease through either transcatheter pulmonary artery rehabilitation or surgery, where it is of interest to achieve desired pressures and flows at specific locations in the pulmonary artery tree, while minimizing the risk for the patient. Since different degrees of success can be achieved in practice during treatment, we formulate the problem in probability, and solve it through a sample-based approach. We propose a new offline-online pipeline for probabilistic real-time treatment planning which combines offline assimilation of boundary conditions, model reduction, and training dataset generation with online estimation of marginal probabilities, possibly conditioned on the degree of augmentation observed in already repaired lesions. Moreover, we propose a new approach for the parametrization of arbitrarily shaped vascular repairs through iterative corrections of a zero-dimensional approximant. We demonstrate this pipeline for a diseased model of the pulmonary artery tree available through the Vascular Model Repository.

A transient protein folding response targets aggregation in the early phase of TDP-43-mediated neurodegeneration.

Understanding the mechanisms that drive TDP-43 pathology is integral to combating amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD) and other neurodegenerative diseases. Here we generated a longitudinal quantitative proteomic map of the cortex from the cytoplasmic TDP-43 rNLS8 mouse model of ALS and FTLD, and developed a complementary open-access webtool, TDP-map ( https://shiny.rcc.uq.edu.au/TDP-map/ ). We identified distinct protein subsets enriched for diverse biological pathways with temporal alterations in protein abundance, including increases in protein folding factors prior to disease onset. This included increased levels of DnaJ homolog subfamily B member 5, DNAJB5, which also co-localized with TDP-43 pathology in diseased human motor cortex. DNAJB5 over-expression decreased TDP-43 aggregation in cell and cortical neuron cultures, and knockout of Dnajb5 exacerbated motor impairments caused by AAV-mediated cytoplasmic TDP-43 expression in mice. Together, these findings reveal molecular mechanisms at distinct stages of ALS and FTLD progression and suggest that protein folding factors could be protective in neurodegenerative diseases.

A Panel of miRNA Biomarkers Common to Serum and Brain-Derived Extracellular Vesicles Identified in Mouse Model of Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease characterised by the deposition of aggregated proteins including TAR DNA-binding protein 43 (TDP-43) in vulnerable motor neurons and the brain. Extracellular vesicles (EVs) facilitate the spread of neurodegenerative diseases and can be easily accessed in the bloodstream. This study aimed to identify a panel of EV miRNAs that can capture the pathology occurring in the brain and peripheral circulation. EVs were isolated from the cortex (BDEVs) and serum (serum EVs) of 3 month-old and 6-month-old TDP-43*Q331K and TDP-43*WT mice. Following characterisation and miRNA isolation, the EVs underwent next-generation sequencing where 24 differentially packaged miRNAs were identified in the TDP-43*Q331K BDEVs and 7 in the TDP-43*Q331K serum EVs. Several miRNAs, including miR-183-5p, were linked to ALS. Additionally, miR-122-5p and miR-486b-5p were identified in both panels, demonstrating the ability of the serum EVs to capture the dysregulation occurring in the brain. This is the first study to identify miRNAs common to both the serum EVs and BDEVs in a mouse model of ALS.

Cell-intrinsic C5a synergizes with Dectin-1 in macrophages to mediate fungal killing.

The complement factor C5a is a core effector product of complement activation. C5a, acting through its receptors C5aR1 and C5aR2, exerts pleiotropic immunomodulatory functions in myeloid cells, which is vital for host defense against pathogens. Pattern-recognition receptors (PRRs) are similarly expressed by immune cells as detectors of pathogen-associated molecular patterns. Although there is evidence of cross talk between complement and PRR signaling pathways, knowledge of the full potential for C5a-PRR interaction is limited. In this study, we comprehensively investigated how C5a signaling through C5a receptors can modulate diverse PRR-mediated cytokine responses in human primary monocyte-derived macrophages and observed a powerful, concentration-dependent bidirectional effect of C5a on PRR activities. Unexpectedly, C5a synergized with Dectin-1, Mincle, and STING in macrophages to a much greater extent than TLRs. Notably, we also identified that selective Dectin-1 activation using depleted zymosan triggered macrophages to generate cell-intrinsic C5a, which acted on intracellular and cell surface C5aR1, to help sustain mitochondrial ROS generation, up-regulate TNFα production, and enhance fungal killing. This study adds further evidence to the holistic functions of C5a as a central immunomodulator and important orchestrator of pathogen sensing and killing by phagocytes.

Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity.

OBJECTIVE: This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. BACKGROUND: Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent's actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. METHODS: A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. RESULTS: Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants' approach to the intersection and not their departure. CONCLUSION: Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver's perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires.

Introduction: Trust has emerged as a prevalent construct to describe relationships between people and between people and technology in myriad domains. Across disciplines, researchers have relied on many different questionnaires to measure trust. The degree to which these questionnaires differ has not been systematically explored. In this paper, we use a word-embedding text analysis technique to identify the differences and common themes across the most used trust questionnaires and provide guidelines for questionnaire selection. Methods: A review was conducted to identify the existing trust questionnaires. In total, we included 46 trust questionnaires from three main domains (i.e., Automation, Humans, and E-commerce) with a total of 626 items measuring different trust layers (i.e., Dispositional, Learned, and Situational). Next, we encoded the words within each questionnaire using GloVe word embeddings and computed the embedding for each questionnaire item, and for each questionnaire. We reduced the dimensionality of the resulting dataset using UMAP to visualize these embeddings in scatterplots and implemented the visualization in a web app for interactive exploration of the questionnaires (https://areen.shinyapps.io/Trust_explorer/). Results: At the word level, the semantic space serves to produce a lexicon of trust-related words. At the item and questionnaire level, the analysis provided recommendation on questionnaire selection based on the dispersion of questionnaires' items and at the domain and layer composition of each questionnaire. Along with the web app, the results help explore the semantic space of trust questionnaires and guide the questionnaire selection process. Discussion: The results provide a novel means to compare and select trust questionnaires and to glean insights about trust from spoken dialog or written comments.

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences.

Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human-AI teaming perspectives on AI development similarly underscore. Co-development strategies may also help reconcile efforts to develop performance-based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences.

It's Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation.

OBJECTIVE: The objective of this study was to estimate trust from conversations using both lexical and acoustic data. BACKGROUND: As NASA moves to long-duration space exploration operations, the increasing need for cooperation between humans and virtual agents requires real-time trust estimation by virtual agents. Measuring trust through conversation is a novel and unintrusive approach. METHOD: A 2 (reliability) × 2 (cycles) × 3 (events) within-subject study with habitat system maintenance was designed to elicit various levels of trust in a conversational agent. Participants had trust-related conversations with the conversational agent at the end of each decision-making task. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). After training, model explanation was performed using variable importance and partial dependence plots. RESULTS: Results showed that a random forest algorithm, trained using the combined lexical and acoustic features, predicted trust in the conversational agent most accurately (Radj2=0.71). The most important predictors were a combination of lexical and acoustic cues: average sentiment considering valence shifters, the mean of formants, and Mel-frequency cepstral coefficients (MFCC). These conversational features were identified as partial mediators predicting people's trust. CONCLUSION: Precise trust estimation from conversation requires lexical cues and acoustic cues. APPLICATION: These results showed the possibility of using conversational data to measure trust, and potentially other dynamic mental states, unobtrusively and dynamically.

(Mis-)use of standard Autopilot and Full Self-Driving (FSD) Beta: Results from interviews with users of Tesla's FSD Beta.

Tesla's Full Self-Driving Beta (FSD) program introduces technology that extends the operational design domain of standard Autopilot from highways to urban roads. This research conducted 103 in-depth semi-structured interviews with users of Tesla's FSD Beta and standard Autopilot to evaluate the impact on user behavior and perception. It was found that drivers became complacent over time with Autopilot engaged, failing to monitor the system, and engaging in safety-critical behaviors, such as hands-free driving, enabled by weights placed on the steering wheel, mind wandering, or sleeping behind the wheel. Drivers' movement of eyes, hands, and feet became more relaxed with experience with Autopilot engaged. FSD Beta required constant supervision as unfinished technology, which increased driver stress and mental and physical workload as drivers had to be constantly prepared for unsafe system behavior (doing the wrong thing at the worst time). The hands-on wheel check was not considered as being necessarily effective in driver monitoring and guaranteeing safe use. Drivers adapt to automation over time, engaging in potentially dangerous behaviors. Some behavior seems to be a knowing violation of intended use (e.g., weighting the steering wheel), and other behavior reflects a misunderstanding or lack of experience (e.g., using Autopilot on roads not designed for). As unfinished Beta technology, FSD Beta can introduce new forms of stress and can be inherently unsafe. We recommend future research to investigate to what extent these behavioral changes affect accident risk and can be alleviated through driver state monitoring and assistance.

TLQP-21 is a low potency partial C3aR activator on human primary macrophages.

TLQP-21 is a 21-amino acid neuropeptide derived from the VGF precursor protein. TLQP-21 is expressed in the nervous system and neuroendocrine glands, and demonstrates pleiotropic roles including regulating metabolism, nociception and microglial functions. Several possible receptors for TLQP-21 have been identified, with complement C3a receptor (C3aR) being the most commonly reported. However, few studies have characterised the activity of TLQP-21 in immune cells, which represent the major cell type expressing C3aR. In this study, we therefore aimed to define the activity of both human and mouse TLQP-21 on cell signalling in primary human and mouse macrophages. We first confirmed that TLQP-21 induced ERK signalling in CHO cells overexpressing human C3aR, and did not activate human C5aR1 or C5aR2. TLQP-21 mediated ERK signalling was also observed in primary human macrophages. However, the potency for human TLQP-21 was 135,000-fold lower relative to C3a, and only reached 45% at the highest dose tested (10 µM). Unlike in humans, mouse TLQP-21 potently triggered ERK signalling in murine macrophages, reaching near full activation, but at ~10-fold reduced potency compared to C3a. We further confirmed the C3aR dependency of the TLQP-21 activities. Our results reveal significant discrepancy in TLQP-21 C3aR activity between human and murine receptors, with mouse TLQP-21 being consistently more potent than the human counterpart in both systems. Considering the supraphysiological concentrations of hTLQP-21 needed to only partially activate macrophages, it is likely that the actions of TLQP-21, at least in these immune cells, may not be mediated by C3aR in humans.

On the relationship between occlusion times and in-car glance durations in simulated driving.

Drivers have spare visual capacity in driving, and often this capacity is used for engaging in secondary in-car tasks. Previous research has suggested that the spare visual capacity could be estimated with the occlusion method. However, the relationship between drivers' occlusion times and in-car glance duration preferences has not been sufficiently investigated for granting occlusion times the role of an estimate of spare visual capacity. We conducted a driving simulator experiment (N = 30) and investigated if there is an association between drivers' occlusion times and in-car glance durations in a given driving scenario. Furthermore, we explored which factors and variables could explain the strength of the association. The findings suggest an association between occlusion time preferences and in-car glance durations in visually and cognitively low demanding unstructured tasks but that this association is lost if the in-car task is more demanding. The findings might be explained by the inability to utilize peripheral vision for lane-keeping when conducting in-car tasks and/or by in-car task structures that override drivers' preferences for the in-car glance durations. It seems that the occlusion technique could be utilized as an estimate of drivers' spare visual capacity in research - but with caution. It is strongly recommended to use occlusion times in combination with driving performance metrics. There is less spare visual capacity if this capacity is used for secondary tasks that interfere with the driver's ability to utilize peripheral vision for driving or preferences for the in-car glance durations. However, we suggest that the occlusion method can be a valid method to control for inter-individual differences in in-car glance duration preferences when investigating the visual distraction potential of, for instance, in-vehicle infotainment systems.

Potential mechanisms to modify impaired glucose metabolism in neurodegenerative disorders.

Neurodegeneration refers to the selective and progressive loss-of-function and atrophy of neurons, and is present in disorders such as Alzheimer's, Huntington's, and Parkinson's disease. Although each disease presents with a unique pattern of neurodegeneration, and subsequent disease phenotype, increasing evidence implicates alterations in energy usage as a shared and core feature in the onset and progression of these disorders. Indeed, disturbances in energy metabolism may contribute to the vulnerability of neurons to apoptosis. In this review we will outline these disturbances in glucose metabolism, and how fatty acids are able to compensate for this impairment in energy production in neurodegenerative disorders. We will also highlight underlying mechanisms that could contribute to these alterations in energy metabolism. A greater understanding of these metabolism-neurodegeneration processes could lead to improved treatment options for neurodegenerative disease patients.

SARS-CoV-2 drives NLRP3 inflammasome activation in human microglia through spike protein.

Coronavirus disease-2019 (COVID-19) is primarily a respiratory disease, however, an increasing number of reports indicate that SARS-CoV-2 infection can also cause severe neurological manifestations, including precipitating cases of probable Parkinson's disease. As microglial NLRP3 inflammasome activation is a major driver of neurodegeneration, here we interrogated whether SARS-CoV-2 can promote microglial NLRP3 inflammasome activation. Using SARS-CoV-2 infection of transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) as a COVID-19 pre-clinical model, we established the presence of virus in the brain together with microglial activation and NLRP3 inflammasome upregulation in comparison to uninfected mice. Next, utilising a model of human monocyte-derived microglia, we identified that SARS-CoV-2 isolates can bind and enter human microglia in the absence of viral replication. This interaction of virus and microglia directly induced robust inflammasome activation, even in the absence of another priming signal. Mechanistically, we demonstrated that purified SARS-CoV-2 spike glycoprotein activated the NLRP3 inflammasome in LPS-primed microglia, in a ACE2-dependent manner. Spike protein also could prime the inflammasome in microglia through NF-κB signalling, allowing for activation through either ATP, nigericin or α-synuclein. Notably, SARS-CoV-2 and spike protein-mediated microglial inflammasome activation was significantly enhanced in the presence of α-synuclein fibrils and was entirely ablated by NLRP3-inhibition. Finally, we demonstrate SARS-CoV-2 infected hACE2 mice treated orally post-infection with the NLRP3 inhibitory drug MCC950, have significantly reduced microglial inflammasome activation, and increased survival in comparison with untreated SARS-CoV-2 infected mice. These results support a possible mechanism of microglial innate immune activation by SARS-CoV-2, which could explain the increased vulnerability to developing neurological symptoms akin to Parkinson's disease in COVID-19 infected individuals, and a potential therapeutic avenue for intervention.

Trusting Automation: Designing for Responsivity and Resilience.

OBJECTIVE: This paper reviews recent articles related to human trust in automation to guide research and design for increasingly capable automation in complex work environments. BACKGROUND: Two recent trends-the development of increasingly capable automation and the flattening of organizational hierarchies-suggest a reframing of trust in automation is needed. METHOD: Many publications related to human trust and human-automation interaction were integrated in this narrative literature review. RESULTS: Much research has focused on calibrating human trust to promote appropriate reliance on automation. This approach neglects relational aspects of increasingly capable automation and system-level outcomes, such as cooperation and resilience. To address these limitations, we adopt a relational framing of trust based on the decision situation, semiotics, interaction sequence, and strategy. This relational framework stresses that the goal is not to maximize trust, or to even calibrate trust, but to support a process of trusting through automation responsivity. CONCLUSION: This framing clarifies why future work on trust in automation should consider not just individual characteristics and how automation influences people, but also how people can influence automation and how interdependent interactions affect trusting automation. In these new technological and organizational contexts that shift human operators to co-operators of automation, automation responsivity and the ability to resolve conflicting goals may be more relevant than reliability and reliance for advancing system design. APPLICATION: A conceptual model comprising four concepts-situation, semiotics, strategy, and sequence-can guide future trust research and design for automation responsivity and more resilient human-automation systems.

Virtual nature experiences and mindfulness practices while working from home during COVID-19: Effects on stress, focus, and creativity.

In this study, we focus on the impact of daily virtual nature experiences combined with mindfulness practices on remote workers' creativity, stress, and focus over an extended period (9 weeks) during the COVID-19 pandemic. Our results show a positive effect of virtual reality (VR) nature experience on increasing focus and reducing stress. When VR nature and mindfulness practices were combined, we also found an increase in convergent thinking task performance. Our findings demonstrate that 10-minute daily exposure to VR nature and mindfulness practices could compensate for some of the adverse effects of working remotely by improving some aspects of workers' well-being and creativity.

Attribution Errors by People and Intelligent Machines.

OBJECTIVE: To explore the ramifications of attribution errors (AEs), initially in the context of vehicle collisions and then to extend this understanding into the broader and diverse realms of all forms of human-machine interaction. BACKGROUND: This work focuses upon a particular topic that John Senders was examining at the time of his death. He was using the lens of attribution, and its associated errors, to seek to further understand and explore dyadic forms of driver collision. METHOD: We evaluated the utility of the set of Senders' final observations on conjoint AE in two-vehicle collisions. We extended this evaluation to errors of attribution generally, as applicable to all human-human, human-technology, and prospectively technology-technology interactions. RESULTS: As with Senders and his many other contributions, we find evident value in this perspective on how humans react to each other and how they react to emerging forms of technology, such as autonomous systems. We illustrate this value through contemporary examples and prospective analyses. APPLICATIONS: The comprehension and mitigation of AEs can help improve all interactions between people, between intelligent machines and between humans and the machines they work with.

SARS-CoV-2 triggers complement activation through interactions with heparan sulfate.

Objectives: To determine whether SARS-CoV-2 can trigger complement activation, the pathways that are involved and the functional significance of the resultant effect. Methods: SARS-CoV-2 was inoculated into a human lepirudin-anticoagulated whole blood model, which contains a full repertoire of complement factors and leukocytes that express complement receptors. Complement activation was determined by measuring C5a production with an ELISA, and pretreatment with specific inhibitors was used to identify the pathways involved. The functional significance of this was then assessed by measuring markers of C5a signalling including leukocyte C5aR1 internalisation and CD11b upregulation with flow cytometry. Results: SARS-CoV-2 inoculation in this whole blood model caused progressive C5a production over 24 h, which was significantly reduced by inhibitors for factor B, C3, C5 and heparan sulfate. However, this phenomenon could not be replicated in cell-free plasma, highlighting the requirement for cell surface interactions with heparan sulfate. Functional analysis of this phenomenon revealed that C5aR1 signalling and CD11b upregulation in granulocytes and monocytes was delayed and only occurred after 24 h. Conclusion: SARS-CoV-2 is a noncanonical alternative pathway activator that progressively triggers complement activation through interactions with heparan sulfate.