National Research Council Canada

Ultrafast electron diffraction (UED) provides direct information on changes in molecular structure following photoexcitation, but identifying the individual vibrational motions contributing to these dynamics remains challenging. Here, we introduce frequency-resolved UED, where Fourier transformation of the time-dependent difference pair distribution function (ΔPDF) gives a two-dimensional frequency-distance representation of the structural dynamics. We apply this approach to allene and 1,2-butadiene following photoexcitation to the S_1(ππ^*) state at 200 nm, using electron scattering signals simulated from previously-published ab initio multiple spawning trajectories (S. P. Neville et al., J. Chem. Phys., 2016, 144, 014305). We identify C=C stretching and CCC bending motions and the internuclear distances over which they contribute, and separate overlapping CH_2 vibrational motions. By changing the pump-probe delay range used for the Fourier transform, we identify when specific frequency components contribute during the excited-state dynamics. Methyl substitution reduces the C=C stretching and CCC bending frequencies and introduces an additional frequency component in 1,2-butadiene during the first 90 fs. We further show the importance of sub-20-fs, and ultimately few-femtosecond, temporal resolution for retrieving these frequency components. Frequency-resolved UED therefore provides the vibrational frequencies, internuclear distances, and reaction times associated with photoinduced structural dynamics.

Peripherally administered therapeutics for neurological indications are challenged with anatomical and physiological barriers that limit their ability to access their site of action in the central nervous system (CNS). This is particularly true for complex therapeutics such as antibodies, immunotherapeutics, and gene therapies. The blood–brain barrier is the specialized structure that functionally regulates the ability of blood constituents to access the CNS. Blood–brain barrier delivery technologies for protein therapeutics have been established in pre-clinical models and are beginning to be verified in clinical studies. Technologies reliant on the transcellular pathway across the blood–brain barrier utilize the receptor-mediated transcytosis mechanism. Research into the use of lipid nanoparticles (LNPs) to deliver complex therapeutics has tremendously expanded in recent years. Lipid nanoparticles represent a compelling alternative to viral vectors for the delivery of various gene therapy modalities, including messenger RNA, small interfering RNA, and antisense oligonucleotides. Functionalization of LNPs with blood–brain barrier-penetrant moieties is being explored as a means to enable CNS delivery of LNP-based therapeutics. The recent innovations and validation of LNP-based delivery systems have hastened the fulfillment of the promise of facile CNS-targeted gene therapies. This review focuses on functional aspects of the blood–brain barrier and how they relate to recent advances in LNP technologies for CNS delivery, as well as their potential impact on gene therapy.

A haplotype-resolved, chromosome-scale AAC Mountainview sainfoin genome sequence along with organelle assemblies, methylome, and comparative tissue/stress-specific transcriptomes provide a solid foundation to support downstream sainfoin breeding efforts. Sainfoin (Onobrychis viciifolia Scop.) is an outcrossing, perennial forage crop valued for its leguminous nature, nutritional quality, palatability, and production of condensed tannins, which reduce the incidence of pasture bloat in ruminants and improve feed efficiency. However, despite its potential, there remains a paucity of research and breeding efforts focusing on this species. In this study, we report the generation of a haplotype-resolved, chromosome-scale reference genome sequence for a genotype of the tetraploid sainfoin cultivar AAC Mountainview, which was assembled using PacBio HiFi, Oxford Nanopore, Illumina short read, and Hi-C data. The 2.36 Gb assembly resolves all 28 pseudochromosomes, corresponding to 4 haplotypes of the 7 base chromosomes, with high contiguity and gene completeness, as well as reference-grade long terminal repeat (LTR) assembly index (LAI) scores across all haplotypes. Nanopore-based methylation profiling revealed typical gene body CG methylation, as well as high levels of transposable element methylation. We annotated 117,890 high-confidence protein-coding genes and identified large tandem arrays of rDNA localized on unanchored but chromosome-associated scaffolds. Mitochondrial and chloroplast genomes were also successfully assembled for this genotype. Furthermore, tissue- and stress-specific transcriptomic profiling revealed both shared and distinct gene expression responses across tissue and stress types. Allele-specific expression analysis showed largely balanced haplotype activity, with subtle but consistent shifts in allelic dominance under stress. The data provided in this study offer a valuable resource for downstream breeding endeavors in this promising forage crop.

Mental health disorders pose a significant global challenge, motivating growing interest in natural language processing (NLP) methods for automated mental health assessment. In recent years, the field has evolved rapidly from traditional feature-based approaches to deep learning architectures and pre-trained foundation models. However, a comprehensive understanding of their relative strengths, limitations, and practical implications remains limited. This paper presents a survey of NLP methodologies for mental health assessment, covering commonly used data sources and representative modeling approaches, and analyzing how advances in representation learning have influenced assessment capability, interpretability, and deployment feasibility. Given that existing studies often rely on disparate datasets and metrics, a direct comparison of these methodologies remains difficult. To complement the literature synthesis, we conduct a unified empirical comparison of representative methods under a consistent experimental setting, providing an additional perspective on performance and efficiency trade-offs. Based on both the literature survey and empirical observations, we discuss key insights that shape the practical use of NLP in mental health, including trade-offs between model complexity and scalability, the role of instruction adherence in prompting-based reasoning, and persistent limitations of current datasets and benchmarks. Building on these observations, this survey outlines important challenges and future research directions toward the responsible and scalable application of NLP technologies in mental health assessment.

Manipulating the frequency and bandwidth of light is crucial in classical and quantum applications including communication, spectroscopy, imaging, and signal processing. Such capabilities also offer potential for interfacing disparate quantum systems in quantum networking and for quantum information processing. We experimentally demonstrate deterministic, broadband frequency control of heralded telecom-band single photons via cross-phase modulation in a short length of single-mode fiber. An intense, ultrafast pump pulse imposes a transient, intensity-dependent refractive-index gradient which imparts a tunable phase shift on the single photons. We present absolute frequency shifts of up to +6.42±0.06  THz and -5.82±0.02  THz, and bandwidth manipulation ranging from a factor of 0.76±0.03 to 7.1±0.4 times that of the input. Spectral measurements are acquired with a time-of-flight spectrometer and superconducting nanowire detectors. Our scheme offers a compact and scalable route to spectral routing and bandwidth engineering for ultrafast quantum networking and quantum information processing.