The allosauroid theropod dinosaurs of the clade Carcharodontosauridae were the apex predators in terrestrial ecosystems of the Early Cretaceous but were replaced in this ecological niche by Tyrannnosauridae in the Late Cretaceous. Details of this turnover are poorly known because only two transitional ecosystems, containing both carcharodontosaurids and tyrannosauroids, had been recognized to date (Cenomanian Cedar Mountain Formation of Utah, USA, and Turonian Bissekty Formation of Uzbekistan). Moreover, the presence of carcharodontosaurids in the Bissekty Formation, based on a maxilla fragment identified as Ulughbegsaurus uzbekistanensis Tanaka et al., 2021, has been recently questioned. Here we report on the third ecosystem containing both clades of apex predators, the Cenomanian Khodzhakul Formation in Uzbekistan. This new occurrence of Carcharodontosauridae is based on a newly identified maxilla that closely resembles the holotype maxilla of U. uzbekistanensis and is identified as Ulughbegsaurus sp. The revised morphological characters of both specimens support attribution of Ulughbegsaurus to Carcharodontosauridae. We report a novel neurovascular feature of the theropod maxilla—a medial alveolar canal that supplied the alveoli medially and contained tributaries of the palatine vessels in Ulughbegsaurus.

Attenuation of shock waves through dense granular media with varying macro-scale and micro-scale parameters has been numerically studied in this work by a coupled Eulerian–Lagrangian approach. The results elucidate the correlation between the attenuation mechanism and the nature of shock-induced unsteady flows inside the granular media. As the shock transmission becomes trivial relative to the establishment of unsteady interpore flows, giving way to the gas filtration, the shock attenuation mechanism transitions from the shock dynamics and deduction of propagation area associated with the shock transmission, to the drag-related friction dissipation alongside the gas filtration. The ratio between the maximum shock transmission length and the thickness of the particle layer is found to be a proper indicator of the nature of shock-induced flows. More importantly, it is this ratio that successfully collapses the upstream and downstream pressures of shock impacted particle layers with widely ranging thickness and volume fraction, leading to a universal scaling law for the shock attenuation effect. We further propose a correlation between the structure of particle layer and the corresponding maximum shock transmission length, guaranteeing adequate theoretical predictions of the upstream and downstream pressures. These predictions are also necessary for an accurate estimation of the spread rate of shock dispersed particle bed through a pressure-gradient-based scaling method.

This manuscript compares gender equality in childcare leave policies across 21 countries and examines its relationship with gender equality in the labour market. To assess gender equality in childcare leave in each country, the duration gap and the uptake gap between genders in childcare leave are examined, and these two gaps are combined using Z-scores to measure the overall level of gender equality in childcare leave. Subsequently, the relationship between overall gender equality in childcare leave and labour market outcomes, such as gender employment and wage gaps, is explored. The results indicate that gender equality in childcare leave is generally highest in Scandinavian countries, moderate in Continental European countries, and mostly low in Eastern European countries. Furthermore, the degree of gender equality in childcare leave is negatively correlated with the gender employment gap, whereas no clear relationship is found with the gender wage gap.

Objectives/Goals: Understanding systematic review results help prioritise more valuable studies. However, evaluating whether a systematic review has conclusively answered a question is difficult, and it is unclear which tools are available for such assessments. Thus, we mapped the extent of methods for determining the conclusiveness of systematic review results. Methods/Study Population: We searched Medline (Ovid), EMBASE (Ovid), and Web of Science to find papers with methods to determine whether systematic review results were conclusive or should be updated. The characteristics of primary references for included methods are presented. We classified and summarized available methods. Results/Anticipated Results: A total of 58 unique methods were identified. Many have been published since 2010 and often did not include a worked example. We found 25 mathematical methods for the conclusiveness of meta-analyses, which included cumulative meta-analysis, fail-safe number, fragility index, prediction and machine learning model, simulation-based power, conditional power, and graphical approaches. There were 15 methods for the conclusiveness of cumulative meta-analyses, such as quality control approach, trial sequential analysis, sequential meta-analysis, and law of iterated logarithm. And, 18 methods assessed the conclusiveness of systematic reviews: GRADE framework, the strength of a body of evidence approach, methods for assessing the need to update a systematic review, and methods for specific clinical domains. Discussion/Significance of Impact: We found a wide range of methods that can be used to determine the conclusiveness of systematic review results. End-users of systematic reviews can review our results to find the most appropriate methods for their contexts and decisions.

Objectives/Goals: Transmission-blocking vaccines hold promise for malaria elimination by reducing community transmission. But a major challenge that limits the development of efficacious vaccines is the vast parasite’s genetic diversity. This work aims to assess the genetic diversity of the Pfs25 vaccine candidate in complex infections across African countries. Methods/Study Population: We employed next-generation amplicon deep sequencing to identify nonsynonymous single nucleotide polymorphisms (SNPs) in 194 Plasmodium falciparum samples from four endemic African countries: Senegal, Tanzania, Ghana, and Burkina Faso. The individuals aged between 1 and 74 years, but most of them ranged from 1 to 19 years, and all presented symptomatic P. falciparum infection. The genome amplicon sequencing was analyzed using Geneious software and P. falciparum 3D7 as a reference. The SPNs were called with a minimum coverage of 500bp, and for this work, we used a very sensitive threshold of 1% variant frequency to determine the frequency of SNPs. The identified SNPs were threaded to the crystal structure of the Pfs25 protein, which allowed us to predict the impact of the novel SNP in the protein or antibody binding. Results/Anticipated Results: We identified 26 SNPs including 24 novel variants, and assessed their population prevalence and variant frequency in complex infections. Notably, five variants were detected in multiple samples (L63V, V143I, S39G, L63P, and E59G), while the remaining 21 were rare variants found in individual samples. Analysis of country-specific prevalence showed varying proportions of mutant alleles, with Ghana exhibiting the highest prevalence (44.6%), followed by Tanzania (12%), Senegal (11.8%), and Burkina Faso (2.7%). Moreover, we categorized SNPs based on their frequency, identifying dominant variants (>25%), and rare variants (Discussion/Significance of Impact: We identified additional SNPs in the Pfs25 gene beyond those previously reported. However, the majority of these newly discovered display low variant frequency and population prevalence. Further research exploring the functional implications of these variations will be important to elucidate their role in malaria transmission.

Turbulent flow widely exists in the aerospace field, and it is still challenging to realise the accurate prediction in the numerical simulation. To realise the high-fidelity numerical simulation of compressible turbulent flow, a high-order accurate self-adaptive turbulence eddy simulation (SATES) method is developed on the PHengLEI-HyOrder open-source solver, combining with the high-order accurate weighted compact nonlinear schemes (WCNS). The compressible flow in the subsonic and transonic is numerically simulated, including some typical cases, such as subsonic flow past a circular cylinder and flow past a square cylinder, high-lift configuration DLR-F11, transonic flow around a circular cylinder. The results predicted by the current high-order accurate SATES are in good agreement with the available experimental and numerical data. The present numerical method can also accurately capture the interactions between shock waves and turbulence while accurately simulating flow separation, shear layer instability and large-scale vortex shedding. The results obtained show that the current high-order accurate SATES simulations based on PHengLEI-HyOrder solver can accurately simulate complex turbulent flows with high reliability.

This study explores lexical borrowing and loanword nativization from a neuro-cognitive perspective testing bi-dialectal speakers of Standard Chinese and Shanghainese Chinese. We created holistic and morpheme-based cross-dialectal loanwords for auditory sentence processing and compared them with Shanghainese-specific words, code-switches, and pre-existing etymologically related words. Participants rated their acceptance of each word, indicating Shanghainese-specific lexical nativeness. GAM analysis of EEG signals revealed that reduced acceptance correlated with frontal positive shifts in ERPs. Holistic loanwords triggered P300-like shifts associated with form-mismatch, whereas morpheme-based loanwords produced LPC-like shifts, suggesting sentence-level re-analysis, and N400-like early frontal negative shifts, indicating lexical integration challenges. Our results indicate that both lexical acceptance and adaptation strategies are pivotal in the cognitive integration of loanwords, revealing distinct neuropsychological stages and pathways in loanword nativization.

We introduce a double framing construction for moduli spaces of quiver representations. This allows us to reduce certain sheaf cohomology computations involving the universal representation, to computations involving line bundles, making them amenable to methods from geometric invariant theory. We will use this to show that in many good situations the vector fields on the moduli space are isomorphic as vector spaces to the first Hochschild cohomology of the path algebra. We also show that considering the universal representation as a Fourier–Mukai kernel in the appropriate sense gives an admissible embedding of derived categories.

Recent studies utilizing AI-driven speech-based Alzheimer’s disease (AD) detection have achieved remarkable success in detecting AD dementia through the analysis of audio and text data. However, detecting AD at an early stage of mild cognitive impairment (MCI), remains a challenging task, due to the lack of sufficient training data and imbalanced diagnostic labels. Motivated by recent advanced developments in Generative AI (GAI) and Large Language Models (LLMs), we propose an LLM-based data generation framework, leveraging prior knowledge encoded in LLMs to generate new data samples. Our novel LLM generation framework introduces two novel data generation strategies, namely, the cross-lingual and the counterfactual data generation, facilitating out-of-distribution learning over new data samples to reduce biases in MCI label prediction due to the systematic underrepresentation of MCI subjects in the AD speech dataset. The results have demonstrated that our proposed framework significantly improves MCI Detection Sensitivity and F1-score on average by a maximum of 38% and 31%, respectively. Furthermore, key speech markers in predicting MCI before and after LLM-based data generation have been identified to enhance our understanding of how the novel data generation approach contributes to the reduction of MCI label prediction biases, shedding new light on speech-based MCI detection under low data resource constraint. Our proposed methodology offers a generalized data generation framework for improving downstream prediction tasks in cases where limited and/or imbalanced data have presented significant challenges to AI-driven health decision-making. Future study can focus on incorporating more datasets and exploiting more acoustic features for speech-based MCI detection.