Explore the vast range of titles available.

The development and experimental performance of instrumentation to measure ageing-related spectroscopic changes in bitumen is described. Oxidation of bitumen at the surface increases the number of carbonyl (C=O) bonds, and this can be measured in the 6 μm region (1700 cm −1 ) of the mid-infrared. Standoff measurements of surface reflectivity were performed using 4 discrete wavelengths, 3 for the carbonyl absorption and the fourth as a spectral reference. The standoff height of 20 cm caused problems resulting from the presence of numerous strong absorption lines of atmospheric water in the optical path, which was solved by use of wavelengths centred within available “water windows” and a pathlength-matched reference channel. The instrument was tested using bitumen samples aged artificially using UV exposure. Results illustrating the instrument’s response to bitumen age, along with tolerance to changes in height and tilt, are shown. Measurements made during preliminary field trials on outdoor asphalt are also demonstrated. Part 1 of this paper describes the scientific challenges involved in designing this instrument.

A strategy is described to make in-situ measurements of a spectroscopic marker of ageing in bitumen binders used on asphalt-paved roads. Oxidation of bitumen at the surface increases the number of carbonyl (C=O) bonds, and this can be measured in the 6 μm region (1700 cm −1 ) of the mid-infrared. A measurement strategy is proposed to make standoff measurements of surface reflectivity in this region, despite the challenge presented by numerous strong absorption lines from atmospheric water vapour within the optical path. An instrument design is described to make measurements at 4 discrete laser wavelengths, namely 1593.0, 1641.4 and 1731.3 cm −1 (around 6 µm) and at 2633.6 cm −1 (3.8 µm), the first 3 responding to carbonyl absorption and the fourth acting as a spectral reference that is substantially unaffected by ageing. Part 2 of this paper describes the implementation of such an instrument and its experimental performance.

The field of computational creativity is beginning to investigate how co-creative agents might interface with the human creative process. These computer colleagues are a mix between creativity support tools helping users achieve creative goals and creative algorithms that generate content autonomously. Computer colleagues have enormous potential because during creative improvisational collaboration, a new form of distributed creativity arises that can lead to emergent, dynamic, and unexpected meaning to support creativity in new ways. However, there is a gap in the literature about cognitive accounts of the interaction dynamics of open-ended creative collaboration, e.g. the rhythm of interaction, style of turn taking, and manner in which participants are mutually making sense of a situation. An empirically grounded cognitive framework would greatly aid in the design and evaluation of co-creative systems. With this dissertation, I begin to address that gap by asking the overarching research question: How do humans collaborate in open-ended improvisational creativity, and how can we design co-creative agents to achieve similar benefits as human collaboration?The thesis statement is that co-creative agents, such as collaborative drawing partners, can inspire new ideas and motivate interaction during open-ended and improvisational creative collaboration on a shared canvas. Additionally, I claim using participatory sense-making as a theoretical lens to model and quantify co-creation (e.g. interaction dynamics, emergent meaning, coupling, autonomy) can help objectively evaluate the effectiveness of interaction designs and technical approaches in co-creative systems. The methods for exploring these claims are:• Empirical Investigation: Performing experiments to study the open-ended improvisational collaboration between humans in the rich domain of narrativebased pretend object play and using participatory sense-making to characterize collaboration dynamics.• Modeling Interaction Dynamics: Creating a new qualitative data coding technique to perform continuous interaction analysis. Using this technique to classify different types of sense-making strategies and collaboration styles; testing the validity of these classifications through inter-rater reliability and comparison to the ground truth of the observational data.• Design and Evaluate a Technical Probe: Developing a co-creative drawing agent based on the core principles of participatory sense-making, such as real-time feedback and dynamic meaning construction, to evaluate the effectiveness of different machine learning approaches, interaction designs, and interface design techniques by testing the system with users on open-ended collaborative drawing experiences with the co-creative drawing agent and wizard of oz setups.This dissertation extends the cognitive science theory of enaction and participatory sense-making to the domain of open-ended creative collaboration to formalize this theory in computational models of creative collaboration. This knowledge primarily contributes to the fields of computational creativity, human-computer interaction, cognitive science, and creativity research. The creative sense-making framework proides a new method to rapidly and reliably quantify interaction dynamics such that they can be mathematically analyzed using continuous functions (i.e. moving averages, integrations) to understand how collaborations flow through time (versus the typical discrete, event-based qualitative coding and descriptive statistics). Finally, this dissertation produced a web-based co-creative drawing agent, the Drawing Apprentice, that learns through interaction and can serve as an experimental platform for studying different techniques of interactive machine learning, human-computer collaboration, and human-human collaboration.

The ideal vaccine against viruses such as influenza and SARS-CoV-2 must provide a robust, durable and broad immune protection against multiple viral variants. However, antibody responses to current vaccines often lack robust cross-reactivity. Here we describe a polymeric Toll-like receptor 7 agonist nanoparticle (TLR7-NP) adjuvant, which enhances lymph node targeting, and leads to persistent activation of immune cells and broad immune responses. When mixed with alum-adsorbed antigens, this TLR7-NP adjuvant elicits cross-reactive antibodies for both dominant and subdominant epitopes and antigen-specific CD8+ T-cell responses in mice. This TLR7-NP-adjuvanted influenza subunit vaccine successfully protects mice against viral challenge of a different strain. This strategy also enhances the antibody response to a SARS-CoV-2 subunit vaccine against multiple viral variants that have emerged. Moreover, this TLR7-NP augments antigen-specific responses in human tonsil organoids. Overall, we describe a nanoparticle adjuvant to improve immune responses to viral antigens, with promising implications for developing broadly protective vaccines.A nanoparticle-based adjuvant incorporating a Toll-like receptor 7 agonist elicits cross-reactive antibodies for both dominant and subdominant epitopes and enhances immune responses against multiple variants of influenza and SARS-CoV-2.

Introduction Data‐driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom‐specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on using connectomic methods that can be used to detect patient‐specific abnormal functional connectivity, guide treatment aimed at the most abnormal regions, and optimize the rapid development of new hypotheses for future study. Methods We used the resting‐state functional MRI data of 28 patients with medically refractory generalized anxiety disorder to perform agile target selection based on abnormal functional connectivity patterns between the Default Mode Network (DMN) and Central Executive Network (CEN). The most abnormal areas of connectivity within these regions were selected for subsequent targeted TMS treatment by a machine learning based on an anomalous functional connectivity detection matrix. Areas with mostly hyperconnectivity were stimulated with continuous theta burst stimulation and the converse with intermittent theta burst stimulation. An image‐guided accelerated theta burst stimulation paradigm was used for treatment. Results Areas 8Av and PGs demonstrated consistent abnormalities, particularly in the left hemisphere. Significant improvements were demonstrated in anxiety symptoms, and few, minor complications were reported (fatigue (n = 2) and headache (n = 1)). Conclusions Our study suggests that a left‐lateralized DMN is likely the primary functional network disturbed in anxiety‐related disorders, which can be improved by identifying and targeting abnormal regions with a rapid, data‐driven, agile aTBS treatment on an individualized basis.

Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies. Xenium spatial transcriptomic profiling of pulmonary fibrosis characterizes cell composition dynamics and histopathological features associated with the disease.

With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable. •DCM and spatial ICA can be combined to study the connectivity between networks.•The result was replicated in two independent datasets, demonstrating repeatability.•Our result confirms previous work on the connectivity between networks.•This work has a lot of potential applications to ageing and patient data.•The technique can be easily applied to commonly acquired resting state data.

Progranulin is a pro-protein that is necessary for maintaining lysosomal function. Loss-of-function progranulin ( GRN ) mutations are a dominant cause of frontotemporal dementia (FTD). Brains of people with FTD due to GRN mutations accumulate lysosomal storage material and exhibit increased expression of lysosomal transcripts, which may be driven by TFEB and related transcription factors. While this may be a compensatory response to lysosomal impairment, overproduction of lysosomal proteins may also contribute to FTD pathogenesis. To investigate how TFEB may contribute to disease in people with GRN mutations, we analyzed the effects of TFEB overexpression in progranulin-insufficient cells and mice. We generated GRN knockout HEK-293 cells ( GRN KO cells), which exhibited increased nuclear localization of TFEB and expression of lysosomal transcripts, but impaired autophagy. TFEB overexpression in GRN KO cells further increased lysosomal transcripts and partially normalized autophagy. We next injected an AAV vector expressing mouse Tfeb (AAV-TFEB) into the thalamus of Grn –/– mice, which accumulates lysosomal storage material. AAV-TFEB increased lysosomal transcripts and reduced immunoreactivity for SCMAS, a marker of lysosomal storage material, in Grn –/– thalamus. These data show that TFEB activity alleviates some autophagy-lysosomal deficits caused by progranulin insufficiency, suggesting potential utility of lysosome-based therapies for GRN -associated diseases.

Even if anthropogenic warming were constrained to less than 2 °C above pre-industrial, the Greenland and Antarctic ice sheets will continue to lose mass this century, with rates similar to those observed over the past decade. However, nonlinear responses cannot be excluded, which may lead to larger rates of mass loss. Furthermore, large uncertainties in future projections still remain, pertaining to knowledge gaps in atmospheric (Greenland) and oceanic (Antarctica) forcing. On millennial timescales, both ice sheets have tipping points at or slightly above the 1.5–2.0 °C threshold; for Greenland, this may lead to irreversible mass loss due to the surface mass balance–elevation feedback, whereas for Antarctica, this could result in a collapse of major drainage basins due to ice-shelf weakening.

Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.