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Eukaryotic precursor tRNAs (pre-tRNAs) often have an intron between positions 37 and 38 of the anticodon loop. However, atypical introns are found in some eukaryotes and archaea. In an early-diverged red alga Cyanidioschyzon merolae, the tRNAIle(UAU) gene contains three intron coding regions, located in the D-, anticodon, and T-arms. In this study, we focused on the relationship between the intron removal and formation of pseudouridine (Ψ), one of the most universally modified nucleosides. It had been reported that yeast Pus1 is a multiple-site-specific enzyme that synthesizes Ψ34 and Ψ36 in tRNAIle(UAU) in an intron-dependent manner. Unexpectedly, our biochemical experiments showed that the C. merolae ortholog of Pus1 pseudouridylated an intronless tRNAIle(UAU) and that the modification position was determined to be 55 which is the target of Pus4 but not Pus1 in yeast. Furthermore, unlike yeast Pus1, cmPus1 mediates Ψ modification at positions 34, 36, and/or 55 only in some specific intron-containing pre-tRNAIle(UAU) variants. cmPus4 was confirmed to be a single-site-specific enzyme that only converts U55 to Ψ, in a similar manner to yeast Pus4. cmPus4 did not catalyze the pseudouridine formation in pre-tRNAs containing an intron in the T-arm.

AIM: Approaches to calculating beta diversity (β) include classical measures based on alpha (α) and gamma (γ) diversity, and multivariate distance‐based measures. Species–area relationships cause measurements of γ to vary, making comparisons of classical β among regions contingent on sampling effort. A recent null‐modelling approach has attempted to account for variation in γ by calculating the degree to which β deviates from a random expectation. Here, we clarify the mathematical links between classical and multivariate approaches to measuring β, to derive predictions regarding the reliability of classical, null‐model and multivariate approaches. Next, we use four ecological datasets and simulated data to test the consistency of these approaches across sampling effort and γ. We focus on an issue that arises when making comparisons among regions, namely that even small changes to the area sampled can differentially increase measured γ in each region, potentially causing artefacts in β that are driven by methodology rather than biology. INNOVATION: Comparisons among regions using classical and null‐model measures change dramatically as sampling effort and γ increase. This change is understood for classical β because of species–area relationships, but not for null‐model measures, making comparisons among regions impossible using the null‐model approach. Multiple‐site dissimilarity shows a similar sensitivity to γ as classical measures. In contrast, pairwise multivariate distances show no systematic effect of sampling effort and γ: increasing the number of sample plots decreases variability but does not alter mean β. MAIN CONCLUSIONS: Multivariate pairwise distances are independent of sample size, offering the most robust comparison among regions. The widespread influence of sampling effort and γ indicate that only scale‐dependent measures of classical and multiple‐site β are comparable, whereas null‐model β may not be comparable among regions. However, in cases where γ is well known, multiple‐site dissimilarity metrics offer several advantages, and should be strongly considered.

Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations.

A large passenger airplane experienced an inflight rapid decompression and was forced to make an emergency landing. Post-accident inspection revealed that a section of the fuselage skin on the top portion of the airplane (crown) had fractured and flapped open during the flight. The National Transportation Safety Board (NTSB) Materials Laboratory examination of the fuselage structure determined that multiple-site damage (fatigue cracking) originated at the skin panel of a lap joint from areas of improperly drilled rivet holes. Fatigue crack growth analysis on the fracture face of the skin panel via quantitative fractography was performed by scanning electron microscopy revealed that cracking started approximately when the airplane entered service.

Aim Although species-occupancy distributions (SODs) and species-area relationships (SARs) arise from the two marginal sums of the same presence/absence matrices, the two biodiversity patterns are usually explored independently. Here, we aim to unify the two patterns for isolate-based data by constraining the SAR to conserve information from the SOD. Location Widespread. Methods Focusing on the power-model SAR, we first developed a constrained form that conserved the total number of occupancies from the SOD. Next, we developed an additive-constrained SAR that conserves the entire shape of the SOD within the power-model SAR function, using a single parameter (the slope of the endemics-area relationship). We then relate this additive-constrained SAR to multiple-sites similarity measures, based on a probabilistic view of Sørensen similarity. We extend the constrained and additive-constrained SAR framework to 23 published SAR functions. We compare the fit of the original and constrained forms of 12 SAR functions using 154 published data sets, covering various spatial scales, taxa and systems. Main conclusions In all 23 SAR functions, the constrained form had one parameter less than the original form. In all 154 data sets the model with the highest weight based on the corrected Akaike's information criteria (wAICc) had a constrained form. The constrained form received higher wAICc than the original form in 98.79% of valid pairwise cases, approaching the wAICc expected under identical log-likelihood. Our work suggests, both theoretically and empirically, that all SAR functions may have one unnecessary parameter, which can be excluded from the function without reduction in goodness-of-fit. The more parsimonious constrained forms are also easier to interpret as they reflect the probability of a randomly chosen occupancy to be found in an isolate. The additive-constrained SARs accounts for two complimentary turn-over components of occupancies: turnover between species and turnover between sites.

Background Intracellular neurofibrillary tangles (NFTs) consisting of aggregated and hyperphosphorylated tau is a major pathological hallmark of Alzheimer's disease and related tauopathies. In AD brain, tau pathology spreads through prion‐like propagation and follows a stereotypical pattern: it originates from the locus coeruleus and transentorhinal cortex (Braak stages I–II), then spreads to the limbic system (Braak stages III–IV), and eventually to the neocortex (Braak stages V–VI). Normally, the acidic N‐ and C‐termini of tau prefers to fold over the basic microtubule binding repeats (MTBR) to form a paperclip‐like conformation, preventing the protein from self‐aggregation. In AD brain, the extreme N‐terminus of tau is lost from NFTs and C‐terminal truncations are found in PHFs as well as mature and ghost tangles. Loss of the N‐ and C‐termini of tau may facilitate self‐aggregation and drive tau pathogenesis. Method A truncated form of tau, tau151‐391 was generated by deleting the acidic N‐ and C‐termini of tau and overexpressed in HEK‐293T cells. RIPA buffer insoluble tau151‐391 aggregates were obtained by ultracentrifugation. Its phosphorylation and proteopathic properties were determined in vitro and in vivo. Result We found that tau151‐391, but not full‐length tau (tauFL), can form aggregates in cultured cells and in vivo in mouse brain. Aggregated tau151‐391 is hyperphosphorylated at multiple sites, including Thr181, Ser199, AT8, Thr212, Ser214, Thr217, and Thr231, and is partially resistant to hydrolysis by proteinase K. Tau151‐391 aggregates potently induce tau aggregation and site‐specific hyperphosphorylation in cultured cells and in mouse brain. By repeatedly inducing aggregation in cultured cells, aggregated tau increases exponentially. The seeding activity of tau151‐391 aggregates is sensitive to repeated freezing and thawing. 3R‐tau151‐391, which contains three MTBRs, and 4R‐tau151‐391, which contains four MTBRs, can be induced to aggregate by each other's aggregates. Conclusion Deletion of the acidic N‐ and C‐termini facilitates tau aggregation and acquires proteopathic properties. Tau151‐391 aggregates effectively induce tau aggregation and site‐specific hyperphosphorylation in cultured cells and in vivo in mouse brain.

1. Zeleny (2008) demonstrated that the co-occurrence based assessment of species habitat specialization (introduced by Fridley et al. 2007 ) depends on the size of the species pool. To correct for the effect of the species pool on the estimation of species niche width, Zeleny suggested a modification of the original algorithm by replacing additive partitioning as a measure of beta diversity with Whittaker's beta. 2. We used simulated data to show that the alternative index proposed by Zeleny (2008) will poorly represent the niche widths of species inhabiting a set of plots with a highly skewed distribution of local richness values. We therefore expand on Zeleny's (2008) analysis by considering two additional metrics of beta diversity based on compositional similarity and by testing the performance of these indices under different local-regional richness relationships. 3. Synthesis. None of the four tested metrics of beta diversity produced unbiased estimates of niche width under curvilinear local-regional richness relationships. In this context, we provide additional guidance to potential users of co-occurrence based niche width estimates by specifying the conditions under which certain indices of beta diversity best represent niche width information.

Background The Global Alzheimer's Association Interactive Network (GAAIN) was established in 2013 as the first federated Alzheimer's disease data sharing platform of its kind. To enhance the interoperability of the GAAIN Interrogator, which allows users to explore data collected across multiple sites and modalities and run preliminary regression analyses, we have established connections to external data sharing platforms to validate access and save cohort attributes. Method Using HTTPS and authorization tokens, we set up a secure connection to the Imaging and Data Archive (IDA) hosted by the Laboratory of Neuro Imaging (LONI). This connection allows for data exchange between the two platforms so that users logged into the GAAIN Interrogator can directly identify which Data Partners they already have approved access to via the IDA. Result By connecting the GAAIN Interrogator to the IDA, users who have approved access to a Data Partner via the IDA can directly access the study download page without having to log in to the IDA. In addition, the subject IDs of custom cohorts defined in the Interrogator can be sent to the IDA so that users can download neuroimaging and comma separated values (CSV) data corresponding to their cohorts of interest. This process allows for a seamless transition from data discovery in the Interrogator to data access and download in the IDA. Conclusion The development of the GAAIN‐IDA connection improves the interoperability features of the GAAIN Interrogator, which can be expanded to other external platforms such as the AD Workbench hosted by the Alzheimer's Disease Data Initiative (ADDI). The analytical capabilities and remote processing pipelines in the Interrogator can also be improved upon by building on this model, ensuring GAAIN as a key resource in the Alzheimer's disease and related dementias research community.

The development of an efficient electrocatalyst toward the hydrogen evolution reaction (HER) is of significant importance in transforming renewable electricity to pure and clean hydrogen by water splitting. However, the construction of an active electrocatalyst with multiple sites that can promote the dissociation of water molecules still remains a great challenge. Herein, a partial‐single‐atom, partial‐nanoparticle composite consisting of nanosized ruthenium (Ru) nanoparticles (NPs) and individual Ru atoms as an energy‐efficient HER catalyst in alkaline medium is reported. The formation of this unique composite mainly results from the dispersion of Ru NPs to small‐size NPs and single atoms (SAs) on the Fe/N codoped carbon (Fe–N–C) substrate due to the thermodynamic stability. The optimal catalyst exhibits an outstanding HER activity with an ultralow overpotential (9 mV) at 10 mA cm−2 (η10), a high turnover frequency (8.9 H2 s−1 at 50 mV overpotential), and nearly 100% Faraday efficiency, outperforming the state‐of‐the‐art commercial Pt/C and other reported HER electrocatalysts in alkaline condition. Both experimental and theoretical calculations reveal that the coexistence of Ru NPs and SAs can improve the hydride coupling and water dissociation kinetics, thus synergistically enhancing alkaline hydrogen evolution performance. A nanocomposite of partial‐single‐atom and partial‐nanoparticle formed within the Fe–N–C matrix serves as a multiple‐site electrocatalyst toward hydrogen evolution reaction with an ultralow overpotential of 9 mV to achieve 10 mA cm−2, a high turnover frequency, and ≈100% Faradaic efficiency. Theoretical calculations reveal that ruthenium single‐atoms effectively facilitate water dissociation, and ruthenium nanoparticles promote hydrogen desorption.

Background Data repositories are critical to advancing open science, ensuring data persistence, and promoting reuse for novel scientific discoveries. In dementia research, platforms such as Dementias Platform Australia, Dementias Platform UK, and the Alzheimer's Disease Data Initiative, have emphasized making data Findable, Accessible, Interoperable, and Reusable (FAIR). However, the decentralized nature of these platforms requires researchers to navigate multiple sites, compile metadata manually, and face challenges in comparing and accessing studies across platforms. Method To address these challenges, we developed the Global Ageing and Dementia Study Explorer (GLADSE), an open‐source R Shiny application. The tool collects and standardizes commonly used metadata from partner platforms, including applying the C‐SURV data model themes and domains, to provide rich, detailed and comparable information. The application was built using R Shiny modules under the golem framework, ensuring modularity for scalability and ease of maintenance. GLADSE offers an interactive dashboard with multiple functionalities, including platform and study overviews, filters, visualization plots, and report generation. Additionally, it can be embedded via iframe within partner platform websites, with optional user authentication. A metadata submission tool was developed separately to encourage data custodians to contribute new datasets. Result GLADSE centralizes metadata exploration, enabling users to identify and compare relevant datasets without navigating multiple platforms. Its modular design allows seamless updates and future expansions. The tool improves data accessibility by featuring diverse metadata and presenting it in an interactive interface. Early feedback highlights its effectiveness in improving data discovery and enabling cross‐platform collaboration, including federated analysis. By integrating global resources, the tool maximizes the utility of existing research infrastructure and facilitates global partnerships. Conclusion GLADSE advances dementia research making data FAIR and fostering collaboration across dementia platforms. Planned future developments, such as incorporating measurement‐level metadata standardised using the C‐SURV ontology will enhance the utility of study filters and visualization plots. Expanding to include detailed imaging and genomic metadata will further strengthen its utility. By proposing a solution to the decentralized data platforms and promoting global collaboration, GLADSE maximizes the value of public investments in research infrastructures, supports critical progress in understanding, preventing, and treating dementia, a pressing public health challenge.