Respiratory drive heterogeneity associated with systemic inflammation and vascular permeability in acute respiratory distress syndrome

Background In acute respiratory distress syndrome (ARDS), respiratory drive often differs among patients with similar clinical characteristics. Readily observable factors like acid–base state, oxygenation, mechanics, and sedation depth do not fully explain drive heterogeneity. This study evaluated the relationship of systemic inflammation and vascular permeability markers with respiratory drive and clinical outcomes in ARDS. Methods ARDS patients enrolled in the multicenter EPVent-2 trial with requisite data and plasma biomarkers were included. Neuromuscular blockade recipients were excluded. Respiratory drive was measured as PES0.1, the change in esophageal pressure during the first 0.1 s of inspiratory effort. Plasma angiopoietin-2, interleukin-6, and interleukin-8 were measured concomitantly, and 60-day clinical outcomes evaluated. Results 54.8% of 124 included patients had detectable respiratory drive (PES0.1 range of 0–5.1 cm H₂O). Angiopoietin-2 and interleukin-8, but not interleukin-6, were associated with respiratory drive independently of acid–base, oxygenation, respiratory mechanics, and sedation depth. Sedation depth was not significantly associated with PES0.1 in an unadjusted model, or after adjusting for mechanics and chemoreceptor input. However, upon adding angiopoietin-2, interleukin-6, or interleukin-8 to models, lighter sedation was significantly associated with higher PES0.1. Risk of death was less with moderate drive (PES0.1 of 0.5–2.9 cm H₂O) compared to either lower drive (hazard ratio 1.58, 95% CI 0.82–3.05) or higher drive (2.63, 95% CI 1.21–5.70) (p = 0.049). Conclusions Among patients with ARDS, systemic inflammatory and vascular permeability markers were independently associated with higher respiratory drive. The heterogeneous response of respiratory drive to varying sedation depth may be explained in part by differences in inflammation and vascular permeability

Vapor grown carbon nanofiber based cotton fabrics with negative thermoelectric power

Vapor grown carbon nanofiber (CNF) based ink dispersions were used to dip-coat woven cotton fabrics with different constructional parameters, and their thermoelectric (TE) properties studied at room temperature. Unlike the positive thermoelectric power (TEP) observed in TE textile fabrics produced with similar carbon-based nanostructures, the CNF-based cotton fabrics showed negative TEP, caused by the compensated semimetal character of the CNFs and the highly graphitic nature of their outer layers, which hinders the p-type doping with oxygen groups onto them. A dependence of the electrical conductivity (r) and TEP as a function of the woven cotton fabric was also observed. The cotton fabric with the largest linear density (tex) showed the best performance with negative TEP values around - 8 lV K-1 , a power factor of 1.65 9 10-3 lW m-1 K-2 , and a figure of merit of 1.14 9 10-6 . Moreover, the possibility of a slight e- charge transfer or n-doping from the cellulose onto the most external CNF graphitic shells was also analysed by computer modelling. This study presents n-type carbon-based TE textile fabrics produced easily and without any functionalization processes to prevent the inherent doping with oxygen, which causes the typical p-type character found in most carbon-based TE materialsFEDER funds through COMPETE and by national funds through FCT – Foundation for Science and Technology within the project POCI-01-0145- FEDER-007136. E. M. F. Vieira is grateful for financial support through FCT with CMEMS-UMinho Strategic Project UIDB/ 04436/202

High Velocity Impact and Blast Loading of Composite Sandwich Panels with Novel Carbon and Glass Construction

This research investigates whether the layup order of the carbon-fibre/glass-fibre skins in hybrid composite sandwich panels has an effect on impact response. Composite sandwich panels with carbon-fibre/glass-fibre hybrid skins were subjected to impact at velocities of 75 ± 3 and 90 ± 3 m s−1. Measurements of the sandwich panels were made using high-speed 3D digital image correlation (DIC), and post-impact damage was assessed by sectioning the sandwich panels. It was concluded that the introduction of glass-fibre layers into carbon-fibre laminate skins reduces brittle failure compared to a sandwich panel with carbon-fibre reinforced polymer skins alone. Furthermore, if the impact surface is known, it would be beneficial to select an asymmetrical panel such as Hybrid-(GCFGC) utilising glass-fibre layers in compression and carbon-fibre layers in tension. This hybrid sandwich panel achieves a specific deflection of 0.322 mm kg−1 m2 and specific strain of 0.077% kg−1 m2 under an impact velocity of 75 ± 3 m s−1. However, if the impact surface is not known, selection of a panel with a symmetric yet more dispersed hybridisation would be effective. By distributing the different fibre layers more evenly within the skin, less surface and core damage is achieved. The distributed hybrid investigated in this research, Hybrid-(GCGFGCG), achieved a specific deflection of 0.394 mm kg−1 m2 and specific strain of 0.085% kg−1 m2 under an impact velocity of 75 ± 3 m s−1. Blast loading was performed on a large scale version of Hybrid-(GCFGC) and it exhibited a maximum deflection of 75 mm following a similar deflection profile to those observed for the impact experiments

Synergistic toughening of composite fibres by self-alignment of reduced graphene oxide and carbon nanotubes

The extraordinary properties of graphene and carbon nanotubes motivate the development of methods for their use in producing continuous, strong, tough fibres. Previous work has shown that the toughness of the carbon nanotube-reinforced polymer fibres exceeds that of previously known materials. Here we show that further increased toughness results from combining carbon nanotubes and reduced graphene oxide flakes in solution-spun polymer fibres. The gravimetric toughness approaches 1,000 J g−1, far exceeding spider dragline silk (165 J g−1) and Kevlar (78 J g−1). This toughness enhancement is consistent with the observed formation of an interconnected network of partially aligned reduced graphene oxide flakes and carbon nanotubes during solution spinning, which act to deflect cracks and allow energy-consuming polymer deformation. Toughness is sensitive to the volume ratio of the reduced graphene oxide flakes to the carbon nanotubes in the spinning solution and the degree of graphene oxidation. The hybrid fibres were sewable and weavable, and could be shaped into high-modulus helical springs

Opportunities and Challenges in Applying AI to Evolutionary Morphology

Synopsis: Artificial intelligence (AI) is poised to revolutionize many aspects of science, including the study of evolutionary morphology. While classical AI methods such as principal component analysis and cluster analysis have been commonplace in the study of evolutionary morphology for decades, recent years have seen increasing application of deep learning to ecology and evolutionary biology. As digitized specimen databases become increasingly prevalent and openly available, AI is offering vast new potential to circumvent long-standing barriers to rapid, big data analysis of phenotypes. Here, we review the current state of AI methods available for the study of evolutionary morphology, which are most developed in the area of data acquisition and processing. We introduce the main available AI techniques, categorizing them into 3 stages based on their order of appearance: (1) machine learning, (2) deep learning, and (3) the most recent advancements in large-scale models and multimodal learning. Next, we present case studies of existing approaches using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, and phylogenetics. We then discuss the prospectus for near-term advances in specific areas of inquiry within this field, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. In particular, we note key areas where AI remains underutilized and could be used to enhance studies of evolutionary morphology. This combination of current methods and potential developments has the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, leading to an era of “big data” that aligns the study of phenotypes with genomics and other areas of bioinformatics