Highly dispersed gold(Au) species exhibit unique catalytic properties that differ considerably from those of bulk Au or larger particles. However, achieving such dispersion while preserving their intrinsic characteristics remains a challenge. This difficulty often arises because conventional methods can alter the characteristics of the Au itself. This study addresses this issue using silica, a support material known for its weak interaction with active metal species. Specifically, we developed a novel nanoporous silica support functionalised with hydrosilyl groups using a cage-siloxane as a building block. These groups enable on-site reduction of Au3+ ions, allowing highly dispersed Au loading without introducing strongly interacting ligands. Importantly, the dispersion was retained even after calcination, indicating a confinement effect within the porous structure. This approach offers a promising strategy for stabilising Au species on inert supports, potentially advancing the design of robust and efficient Au-silica catalysts for heterogeneous catalysis.

Crossover randomized controlled trials (RCTs) are common in exercise and nutrition sciences. Since researchers randomize participants to different sequences of the treatment and comparator/control conditions, crossover RCTs are powerful for detecting mean treatment effects under certain circumstances. We aim to review the information that can be derived from crossover RCTs about treatment response heterogeneity-a fundamental issue in precision medicine for tailoring treatments to individuals. After covering the fundamental design issues, we describe the variance components that underlie observed data. The crucial person-by-treatment variance component can be quantified from a repeated or "replicate" crossover RCT by exposing participants to multiple cycles of trial conditions. As a type of n-of-1 trial, replicate crossover RCTs have important design and statistical power considerations, which we describe. By synthesizing findings from our six published replicate crossover RCTs, we also compare various data analysis approaches. We find general agreement between these approaches, and a link between within-person consistency of response and the detection of person-by-treatment interactions. We postulate that a paired "variance comparison," for example, the Pitman-Morgan test, provides some preliminary information regarding response heterogeneity from a typical single-cycle crossover RCT. Nevertheless, underlying assumptions are critical, rendering these comparisons as merely exploratory until an n-of-1 or replicate crossover RCT is undertaken. Multiple-cycle n-of-1 trials and replicate crossover RCTs are underused but are informative for treatment response heterogeneity. However, these trials are still only one component of the process for predicting individual magnitude of response from any personal traits, which is the "holy grail" of personalized treatment.

Cleavage of aromatic C-C bonds remains a formidable challenge in organic synthesis due to their exceptional stability. We describe an electrooxidative protocol that enables selective cleavage of peripheral C-C bonds in various polycyclic aromatic hydrocarbons (PAHs) under anodic oxidation conditions in DMF, affording dicarbonyl products. In addition to phenacenes, this method cleaves the peripheral bond of corannulene and the inner bond of dibenzo[g,p]chrysene, demonstrating a broad applicability for PAH bond activation.

The interaction between staphylococcal protein A (SpA) and human immunoglobulin G (IgG) is pivotal in treating diseases such as cancer, inflammation, infections, and autoimmune disorders. However, acquiring natural SpA variants is labor-intensive, traditional protein design methods often depend on extensive datasets and detailed structural information, limiting their efficiency and applicability. To overcome these limitations, we propose a deep learning-based approach that directly targets desired binding functions by introducing mutations at selected SpA sites to optimize its properties. Specifically, we present a de novo protein design strategy that integrates a diffusion-based generative model with Kidera factor representations to create SpA variants. The framework comprises three modules: sequence generation, where protein sequences are encoded via Kidera factors and novel variants are generated using a diffusion model; computational screening, employing tools like AlphaFold3 to assess structural properties, solubility, and physicochemical characteristics, thereby selecting high-potential candidates; and experimental validation, involving wet-lab experiments to evaluate the biological activities and binding affinities of the designed proteins. The generated SpA variants demonstrated high success rates and strong binding affinities toward IgG. These findings confirm the effectiveness of our method in producing functional proteins comparable to natural counterparts, offering a scalable and data-efficient alternative to protein engineering.

Telomerase is a ribonucleoprotein complex that elongates telomeric DNA, ensuring germline immortality. In this study, we identified the Caenorhabditis elegans telomerase RNA component 1 (terc-1), as the first known telomerase RNA expressed as an intronic long noncoding RNA (lncRNA), embedded in an intron of germline-up-regulated gene nmy-2. terc-1 undergoes splicing, polyadenylation, and nuclear RNA exosome-dependent maturation, stabilized by H/ACA small nucleolar ribonucleoproteins, thus co-opting the H/ACA small nucleolar RNA (snoRNA) biogenesis machinery. Mutations in terc-1 led to progressive telomere shortening and sterility in successive generations. Artificially transplanting the nmy-2 intron into the introns of germline-expressed genes but not non-germline-expressed genes restored germline immortality, highlighting the importance of genomic context. Our findings suggest that nematode telomerase RNA is a snoRNA-like intronic lncRNA that exploits the introns of germline-up-regulated genes to ensure species survival.

The real-time active control of plasmonic properties is desirable for advanced applications in photochemical reactions and nano-optical devices. In this study, we investigated the morphology and optical properties of a single gold nanorod under laser irradiation. We observed a blue shift in the plasmon resonance after laser irradiation. Furthermore, electron micrograph analysis revealed that this shift occurred due to the transformation of the nanorod shape. We also examined the time-evolution of the resonance wavelength under laser irradiation, and we found that the resonance wavelength could be reversibly modulated through controlled laser irradiation. Electromagnetic field simulations showed that the reversible modulation behavior was achieved by the thermal expansion and shrinking of the nanorod. Moreover, we demonstrated the ultrafine modulation of the resonance as well as the physical shape of the nanorod with the subnanometer accuracy. The method developed in this study paves the way for the precise and ultrafine modulation of plasmon resonance and is indispensable for advanced control of light-matter interactions at the nanoscale.

Cognitive performance is significantly affected by sleep, but mild chronic sleep deprivation in daily life remains difficult to measure. Laboratory-enforced sleep restriction may not fully replicate real-life conditions. This study investigates whether Social Jet Lag (SJL), an indicator of misalignment between biological and social time, can used as a proxy for mild chronic sleep deprivation and its impact on cognitive function. Participants leading typical social lives were selected based on their SJL scores, and cognitive performance was assessed using an online experiment incorporating a Psychomotor Vigilance Task (PVT) and a Sustained Attention to Response Task (SART) at different times (post-wake and pre-sleep) and across multiple days (Sunday, Monday, and Friday). Generalised linear modelling (GLM) revealed that SJL was consistently the most explanatory factor for cognitive performance, while test timing also had a significant impact. Cognitive performance impairments due to SJL remained stable across days, suggesting a stable influence of sleep timing irregularity. Additionally, SJL was associated with increased false-positive rates in the SART, indicating reduced response inhibition ability. While SJL proved to be a useful measure compared to average sleep duration and the Sleep Regularity Index (SRI), its effectiveness may be specific to populations following structured work schedules.