澳大利亚国立大学

In the sufficiently sparse case, we find the probability that a uniformly random bipartite graph with given degree sequence contains no edge from a specified set of edges. This enables us to enumerate loop-free digraphs and oriented graphs with given in-degree and out-degree sequences, and obtain subgraph probabilities. Our theorems are not restricted to the near-regular case. As an application, we determine the expected permanent of sparse or very dense random matrices with given row and column sums; in the regular case, our formula holds over all densities. We also draw conclusions about the degrees of a random orientation of a random undirected graph with given degrees, including its number of Eulerian orientations.

A comparison of Precambrian striate, discoid acritarchs with Circulisporites parvus de Jersey originally described from the Triassic of Queensland, significantly expands the stratigraphic distribution of a morphotype previously shown to be aligned with cysts of species belonging to the Euglenophyceae. These organic walled microfossils (owms) are described systematically and placed into three species of Ouroboros gen. nov.: O. soroboruo gen. et sp. nov., O. vulgaris gen et sp. nov. and O. torus gen. et sp. nov. These species are widespread in the 1078 Ma Nonesuch Formation in the United States with minor occurrences in the Torridonian Sequence of northwest Scotland. These microfossil cysts were originally buried in the bottom sediments of a rift valley lake, and they share a common ecology with modern freshwater euglenids, including an association with cyanobacteria. As a member of the Discobid clade, the euglenids are positioned close to the base of the eukaryote tree in some molecular phylogenetic studies, so their occurrence as documented here would appear to support models of earlier rather than later Neoproterozoic diversification of eukaryote lineages. Ouroboros gen. nov. becomes one of only a handful of examples of Precambrian owms that have found a place in crown group eukaryotic lineages.

This study systematically investigates copper gettering in silicon by heavily doped n+ and p+ diffused layers fabricated on high-resistivity float-zone silicon wafers of both polarities, following intentional copper contamination of the wafer bulk. Experimental carrier lifetime analysis and photoluminescence imaging before and after dopant diffusion, corroborated by numerical gettering simulations based on the reported copper solubilities and diffusivities, demonstrate the gettering effect of n+ and p+ diffused layers for copper. The gettering effectiveness increases with increasing doping, as expected. Lightly doped regions exhibit limited gettering effects, for instance, an n+ region with a sheet resistance of 150 Omega/square can only getter 10% of the bulk Cu. This implies that such lightly doped surface regions may be insufficient barriers against copper in-diffusion from metallized layers to the silicon wafer bulk. Furthermore, the impact of gettered Cu on the surface passivation quality of SiNx-coated n+ and Al2O3-coated p+ regions was evaluated. For heavily doped samples, Auger recombination remains the dominant effect that masks potential Cu-induced degradation. For lightly doped samples, surface recombination was mostly limited by the applied spin-on-dopant rather than the gettered Cu.

Phylogenetic inference is fundamental to modern biology, with many applications including evolutionary biology, epidemiology, and comparative genomics. While maximum likelihood and Bayesian methods remain the gold standard for phylogenetic analysis, they rely on simplifying assumptions and are computationally intensive. Recent machine learning approaches for phylogenetics offer speed advantages, but have several limitations: exclusive reliance on simulated data for training, inadequate handling of gaps, and sensitivity to input sequence order. Here, we introduce IQ-NET (Intelligent Quartet NETwork), a deep learning framework that solves these limitations to infer four-taxon trees. IQ-NET estimates both tree topology and branch lengths directly from gapped alignments. IQ-NET outperforms existing machine learning methods in terms of accuracy, and obtained a 24-fold speedup compared with the widely used maximum likelihood software, IQ-TREE. We finally introduce a pipeline using IQ-NET and the ASTRAL software to reconstruct a larger species tree, i.e., with more than four taxa.

Trait anxiety is typically associated with impaired performance on cognitive tasks. In contrast, here we found that trait cognitive anxiety was associated with superior performance in detecting rare visual search targets. Specifically, the risk of missing searched-for targets is substantially higher when they appear infrequently (i.e., under low prevalence conditions) compared with when the same targets appear frequently (i.e., under high prevalence conditions). This phenomenon is called the Low Prevalence Effect (LPE). In a sample of 315 adults, we assessed the relationship between anxiety (cognitive and somatic, trait and state) and visual search performance under low and high prevalence conditions. Higher trait cognitive anxiety predicted a reduced LPE, driven specifically by a lower miss rate under low prevalence conditions. This relationship was mediated by a longer quitting threshold, such that individuals with higher levels of trait cognitive anxiety spent longer searching each array for the target under low prevalence conditions compared with individuals with lower levels of trait cognitive anxiety. Importantly, their time to detect present targets under high prevalence conditions was not impaired. These findings indicate that trait cognitive anxiety can be associated with superior task performance under conditions in which a more cautious task strategy is advantageous.