National Research Council (Canada)

Protein-protein interactions play key roles in leukocyte extravasation process into the brain and have been attractive therapeutic targets for inhibiting brain inflammation using blocking (or neutralizing) antibodies. These targets include protein-protein interactions between cytokines (or chemokines) and their receptors on leukocytes and between adhesion molecules of leukocyte and brain endothelium. While a number of therapeutics against these targets are currently used in clinic for treatment of brain autoimmune and inflammatory disorders (e.g., multiple sclerosis), they are associated with side effects partly due to the off-target actions (i.e., nonspecific targets). There is a need for novel targets involved in the leukocyte extravasation process that are specific to leukocyte subsets or to individual inflammatory disorder and are amenable for drug development (i.e., druggable). We recently described the blood-brain barrier (BBB) Carta Project as a comprehensive collection of molecular "maps" consisting of multiple experimental omics (including RNA sequencing, proteomics, glycoproteomics, glycomics, metabolomics) and in silico informatics analyses on a number of mammalian species from hundreds of internal, publically available, or curated datasets. Utilizing the datasets and tools from the BBB Carta Project, we describe a methodology to identify novel "druggable" targets involving protein-protein interactions between activated leukocytes and brain endothelial cells using a combination of proteomics, bioinformatics, and in silico interactomics. The result is a prioritized list of protein-protein interactions in a network consisting of leukocyte-brain endothelial cell communication and contacts. These interactions can be further pursued for development of therapeutics such as neutralizing antibodies and their validation through preclinical testing. In addition to targeting brain inflammation, the method described here is applicable for peripheral inflammation and provides the opportunity to target important cell-cell interactions and communications that are more specific/selective for inflammatory disorders and improve currently available therapies.

We present the discovery of a superjovian planet around the young A5 star HIP 54515, detected using precision astrometry from the Hipparcos Gaia Catalogue of Accelerations and high-contrast imaging with SCExAO/CHARIS from the recently commenced OASIS program. SCExAO/CHARIS detects HIP 54515 b in five epochs 0 . ″ 145–0 . ″ 192 from the star (∼3–4 λ / D at 1.65 μ m), exhibiting clockwise orbital motion. HIP 54515 b lies near the M/L transition with a luminosity of log( L / L ⊙ ) ∼−3.52 ± 0.03. Dynamical modeling constrains its mass and mass ratio to be 17.7 − 4.9 + 7.6 M Jup and 0.0090 − 0.0024 + 0.0036 and favors a ∼25 au semimajor axis. HIP 54515 b adds to a growing list of superjovian planets with moderate eccentricities ( e ≈ 0.4). Now, the third planet discovered from surveys combining high-contrast extreme adaptive optics imaging with precision astrometry, HIP 54515 b, should help improve empirical constraints on the luminosity evolution and eccentricity distribution of the most massive planets. It may also provide a key technical test of the Roman Space Telescope Coronagraph Instrument’s performance in the low stellar flux, small angular separation limit, and a demonstration of its ability to yield constrainable planet spectral properties.

This study introduces a novel approach for fabricating ceramic structures using a silicon oxycarbide (SiOC) preceramic resin enhanced with boron nitride nanotubes (BNNTs) through digital light processing (DLP). These ceramics feature intricate shapes and high-resolution triply periodic minimal surface (TPMS) architectures with low relative density structures but dense (low-porosity) ceramic features. Incorporating BNNTs at low concentrations (0.2, 0.4, and 0.8 wt%) into a commercially available SiOC precursor, which was then formulated for DLP printing, resulted in a significant reduction in porosity and improved mechanical performance in the polymer-derived SiOC. This combined effect preserved original designs with higher accuracy and significantly enhanced energy absorption and compressive strength of the 3D-printed ceramics compared to baseline SiOC lattices, by factors of 4.4 and 6 times, respectively. Characterization revealed modest changes in storage and loss moduli with BNNT addition, while the BNNT-modified formulation exhibited excellent printability, and ceramic density measurements confirmed a slight increase with BNNT incorporation. This innovative approach, paired with the versatility of additive digital manufacturing, enables the creation of customizable, bio-inspired ceramic structures with tunable properties for aerospace, energy, and biomedical applications.

Climate change has significantly increased the frequency, intensity, and magnitude of heatwaves, leading to numerous deaths in recent decades. While environmental parameters are known contributors to heat-related mortality, the specific impacts of socioeconomic factors remain less clear. This study introduces a new Building Heat Vulnerable Index (BHVI) to assess and map urban overheating mortality risk during heatwaves at the building level. Using data from the 2018 Montreal heatwave, we employed penalized logistic regression (PLR) to analyze the correlation between heat-related mortality and both environmental and socioeconomic parameters across Montreal. The City Building Energy Model (CityBEM) was used to simulate indoor overheating conditions, providing detailed exposure data. Socioeconomic variables were collected from Censusmapper and Geoportail Quebec. Our analysis revealed that “dwelling density” and “average income” are the most significant factors affecting heat-related mortality. Utilizing the BHVI, we generated a detailed heat vulnerability map identifying risk regions across Montreal, highlighting vulnerable areas with high dwelling density and low average income. Additionally, we thoroughly evaluated the impacts of increasing air conditioning (AC) capacity on mitigating heat vulnerability through bootstrap simulations. The results demonstrated that enhancing AC capacity significantly reduces heat-related mortality risk, particularly in high and critical risk areas. The findings underscore the importance of integrating socioeconomic factors and building-level data into heatwave mortality risk assessments. They suggest that targeted interventions, such as improving AC accessibility in vulnerable neighborhoods, can effectively mitigate heat-related health risks. This study provides valuable insights for policymakers to implement effective heat mitigation strategies in urban environments facing escalating climate challenges.

Large Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP's influence to other fields of study. However, there is little to no work examining the degree to which LLMs influence other research fields. This work empirically and systematically examines the influence and use of LLMs in fields beyond NLP. We curate 106 LLMs and analyze ∼148k papers citing LLMs to quantify their influence and reveal trends in their usage patterns. Our analysis reveals not only the increasing prevalence of LLMs in non-CS fields but also the disparities in their usage, with some fields utilizing them more frequently than others since 2018, notably Linguistics and Engineering together accounting for ∼45% of LLM citations. Our findings further indicate that most of these fields predominantly employ task-agnostic LLMs, proficient in zero or few-shot learning without requiring further fine-tuning, to address their domain-specific problems. This study sheds light on the cross-disciplinary impact of NLP through LLMs, providing a better understanding of the opportunities and challenges.