Substitution model selection is central to phylogenetic inference and is commonly treated as a problem of identifying the substitutional complexity required to describe sequence evolution along a single tree. This framework implicitly assumes a shared genealogy across all sites, an assumption that is routinely violated in phylogenomic data by incomplete lineage sorting and other sources of gene-tree discordance. Recent work by Lozano et al. (2026) demonstrates that unmodeled genealogical heterogeneity can systematically distort substitution model selection, creating spurious support for parameter-rich models even when the underlying substitution process is simple. In this perspective, we examine the consequences of this confounding for phylogenetic estimation, divergence-time inference, and the biological interpretation of substitution model parameters, highlighting the need to more explicitly integrate genealogical heterogeneity into model selection and adequacy assessment in phylogenomics. This perspective reframes substitution model choice not only as a tool for describing molecular evolution, but also as a potential diagnostic for violations of shared-genealogy assumptions that are central to modern systematics.

While runtime parameters have been incorporated to enhance API-based malware detection, existing approaches still fall short in fully capturing the structural and temporal characteristics of API call sequences, thereby limiting their generalization capability. In this paper, we propose HAT, a novel detection method that jointly models API sequences from both structural and temporal perspectives. HAT leverages a hierarchical attention mechanism to learn the varying importance of API names and their parameters, and integrates two complementary temporal modules to uncover execution patterns of malware that are underexplored in prior work. Extensive experiments on multiple datasets demonstrate that HAT consistently outperforms existing methods. Compared to approaches relying only on API names, HAT improves the F1-score by 5.50% to 30.87%. Compared to parameter-augmented approaches, it achieves superior detection and generalization, with F1-score improvements of 4.10% to 7.07%, benefiting from its unified modeling of structural and temporal aspects.

PREMISE:Rafflesiaceae (Malpighiales) is an iconic parasitic plant clade of species that occur west of Wallace's line in tropical Southeast Asia. This clade has been notoriously difficult to place phylogenetically and is nested within an explosive ancient radiation in Malpighiales, rendering one of the thorniest nodes across the angiosperm Tree of Life. The parasitic family Apodanthaceae has recently been positioned as sister to Rafflesiaceae, offering hope of stabilizing their placements. METHODS:Here, using a data set of 2135 genes and complex methods for species tree inference, we aimed to resolve the phylogenetic placement and divergence time of Rafflesiaceae and Apodanthaceae. We also applied computational simulation to investigate the impact of incomplete lineage sorting (ILS) and long-branch attraction on resolving this ancient radiation. RESULTS:The clade comprising Rafflesiaceae and Apodanthaceae was variously placed with Euphorbiaceae, Peraceae, Putranjivaceae, and Pandaceae by coalescent and concatenation methods. Such unstable placements appear to be the result of excessive levels of rate heterogeneity and ILS, which contribute to a phylogenetic "danger zone" where simulation suggests that current methods and genomic data may never provide a tidy species tree. CONCLUSIONS:Despite the topological uncertainty, our divergence time estimation identified a mid-Cretaceous origin of stem group Rafflesiaceae and Apodanthaceae, not only making them the oldest parasitic plant lineage reported to date, but also suggesting a likely Gondwana vicariance scenario that explains their initial disjunct distribution in Northern India, Australia, South America, and Africa.

Understanding the transmission dynamics of infectious diseases is critical for effective public health intervention. Traditional models often rely on simplifying assumptions that overlook the complexity of real-world contact patterns. In this study, we present an extended Bayesian framework that integrates genomic, temporal, and network data to reconstruct transmission networks with greater accuracy. By incorporating network structure as a prior, the model accounts for social and spatial proximity, allowing transmission probabilities to vary with contact or social distance. We further enhance inference sensitivity through a hypothesis testing procedure optimized via constrained likelihood estimation. Simulation results demonstrate that network-informed models outperform non-network-informed models, particularly under limited genetic resolution. Application to a tuberculosis dataset from Kampala, Uganda reveals that the network-informed model resolves transmission ambiguities more effectively than models based solely on genetic and temporal data. Additionally, Exponential Random Graph Model (ERGM) analysis indicates that transmission is more likely to occur through weak social ties than within tightly connected clusters, aligning with sociological theories of information flow. While the framework shows strong performance, limitations such as data sparsity and computational demands remain. Future work will focus on integrating mobility data to further refine transmission inference. This integrative approach offers a robust tool for epidemiological analysis and supports more targeted public health decision-making.

Android malware detection continues to face persistent challenges stemming from long-term concept drift and class imbalance, as evolving malicious behaviors and shifting usage patterns dynamically reshape feature distributions. Although continual learning (CL) mitigates drift, existing replay-based methods suffer from inherent bias. Specifically, their reliance on classifier uncertainty for sample selection disproportionately prioritizes the dominant benign class, causing overfitting and reduced generalization to evolving malware. To address these limitations, we propose a novel uncertainty-guided CL framework. First, we introduce a hierarchical balanced sampler that employs a dual-phase uncertainty strategy to dynamically balance benign and malicious samples while simultaneously selecting high-information, high-uncertainty instances within each class. This mechanism ensures class equilibrium across both replay and incremental data, thereby enhancing adaptability to emerging threats. Second, we augment the framework with a vector retrieval mechanism that exploits historical malware embeddings to identify evolved variants via similarity-based retrieval, thereby complementing classifier updates. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art methods under strict low-label conditions (50 labels per phase). It achieves a true positive rate (TPR) of 92.95% and a mean accuracy (mACC) of 94.26%, which validates its efficacy for sustainable Android malware detection.

Background:Tuberculosis persists today in many resource-limited countries in the southern hemisphere because unobserved transmission of M. tuberculosis occurs in undefined contact networks of infectious cases. Methods:To study the transmission dynamics of M. tuberculosis in an African city with endemic tuberculosis, we built out a sociocentric network in the Lubaga Division of Kampala, Uganda, using the personal networks of 130 index cases and 123 community controls frequency-matched by age, sex, and parish. Clusters of genetically related strains were identified using whole genome sequencing was from 99 isolates of the cases. The social distance between cases with related pairs was estimated from the sociocentic network. Findings:We found that characteristics of this sociocentric network account, in part, for tuberculosis persistence. These characteristics included highly connected network members, or hubs, where mixing among contacts may occur; predominant transmission among contacts with weak, or distant, ties to the index case; and geographic structural holes in the network that may link cases with these unknown contacts. Interpretation:These findings suggest that active case finding within the social networks of index cases may result in marginal gains in reducing transmission of tuberculosis. To achieve greater gains, transmission in the community may be reduced through population-based strategies that disrupt transmission in geographic hubs of transmission where mixing may occur between infectious cases and community contacts. Funding:This research was conducted with support from the National Institute of Health (AI093856, AI147319, P30 AI 68386, D43TW010045, D43TW012481).

Macroevolutionary forces, such as rare catastrophes, have repeatedly disrupted and reset the evolutionary trajectories of Earth's major organismal groups. The Cretaceous-Paleogene (K/Pg) extinction event, approximately 66 Ma, resulted in the demise of ∼75% of all species at the time, yet despite its magnitude, many major organismal lineages successfully passed through this mass extinction. The evolutionary origins of modern birds (crown-group Aves) remain a subject of substantial debate, as they are often thought to have undergone their primary diversification following the K/Pg boundary. In this review, we summarize the various approaches that have been applied to understanding the timing of avian diversification. We examine the inferred divergence times derived from modern phylogenomic studies based on datasets comprising 50 to over 300 whole genomes. Additionally, we evaluate the factors contributing to the continued discrepancies in divergence time estimates. Furthermore, we discuss significant new fossil discoveries from the Late Jurassic and Late Cretaceous periods that reshape our understanding of key evolutionary events in early avian diversification. Taken together, the paleontological evidence increasingly supports a Cretaceous origin for many extant bird lineages, with the major burst of ordinal diversification likely occurring prior to the K/Pg boundary-concurrent with the early radiations of flowering plants, pollinating insects, mammals, fishes and other groups that characterized the Cretaceous Angiosperm Terrestrial Revolution.

RSV and seasonal influenza are two of the most prevalent causes of respiratory infection in the U.S. In this study, we used weekly positive case reports and genetic surveillance data to characterize the circulation of these viruses in the United States between 2011 and 2019 and a mathematical modeling approach to explore their potential interaction at a regional level. Our analyses showed that RSV and seasonal influenza co-circulate with different relative epidemic sizes and seasonal overlaps across regions and seasons. We found that RSV had a different evolutionary dynamic compared to seasonal influenza and that local persistence may play a role in underlying annual epidemics. Our analysis supports a potential competitive interaction between RSV and seasonal influenza in most regions across the United States. The multiple-pathogen modeling framework suggests that cross-immunity following infection of either virus might be one of the key drivers of viral competition. However, this finding is based on model-derived inferences and limited surveillance data; further investigation is needed to confirm its robustness and gain a better understanding of the underlying mechanisms. These findings underscore the importance of continued research into the immunological and ecological mechanisms of viral inference, which might be important for the development of more effective protective strategies against co-circulating respiratory viruses.

Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. This study presents a Bayesian transmission model that combines genomic and temporal data to reconstruct transmission networks for infectious diseases. The Bayesian transmission model incorporates the latent period and distinguishes between symptom onset and actual infection time, improving the accuracy of transmission dynamics and epidemiological models. It also assumes a homogeneous effective population size among hosts, ensuring that the coalescent process for within-host evolution remains unchanged, even with missing intermediate hosts. This allows the model to effectively handle incomplete samples. Simulation results demonstrate the model's ability to accurately estimate model parameters and transmission networks. Additionally, our proposed hypothesis test can reliably identify direct transmission events. The Bayesian transmission model was applied to a real dataset of Mycobacterium tuberculosis genomes from 69 tuberculosis cases. The estimated transmission network revealed two major groups, each with a superspreader who transmitted M. tuberculosis, either directly or indirectly, to 28 and 21 individuals, respectively. The hypothesis test identified 16 direct transmissions within the estimated network, demonstrating the Bayesian model’s advantage over a fixed threshold by providing a more flexible criterion for identifying direct transmissions. This Bayesian approach highlights the critical role of genetic data in reconstructing transmission networks and enhancing our understanding of the origins and transmission dynamics of infectious diseases.

The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understanding. This year's challenges span four vision-based tasks: (1) Team Ball Action Spotting, focused on detecting ball-related actions in football broadcasts and assigning actions to teams; (2) Monocular Depth Estimation, targeting the recovery of scene geometry from single-camera broadcast clips through relative depth estimation for each pixel; (3) Multi-View Foul Recognition, requiring the analysis of multiple synchronized camera views to classify fouls and their severity; and (4) Game State Reconstruction, aimed at localizing and identifying all players from a broadcast video to reconstruct the game state on a 2D top-view of the field. Across all tasks, participants were provided with large-scale annotated datasets, unified evaluation protocols, and strong baselines as starting points. This report presents the results of each challenge, highlights the top-performing solutions, and provides insights into the progress made by the community. The SoccerNet Challenges continue to serve as a driving force for reproducible, open research at the intersection of computer vision, artificial intelligence, and sports. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet.

Interspecific hybridization may trigger species radiation by creating allele combinations and traits. Cultivated potato and its 107 wild relatives from the Petota lineage all share the distinctive trait of underground tubers, but the underlying mechanisms for tuberization and its relationship to extensive species diversification remain unclear. Through analyses of 128 genomes, including 88 haplotype-resolved genomes, we revealed that Petota is of ancient hybrid origin, with all members exhibiting stable mixed genomic ancestry, derived from the Etuberosum and Tomato lineages ca. 8-9 million years ago. Our functional experiments further validated the crucial roles of parental genes in tuberization, indicating that interspecific hybridization is a key driver of this innovative trait. This trait, along with the sorting and recombination of hybridization-derived polymorphisms, likely triggered the explosive species diversification of Petota by enabling occupation of broader ecological niches. These findings highlight how ancient hybridization fosters key innovation and drives subsequent species radiation.

e15136 Background: PEONY (NCT02586025) is a randomized, multicenter, double-blind, placebo-controlled, phase III trial evaluating neoadjuvant and adjuvant pertuzumab/placebo with trastuzumab and docetaxel in HER2-positive early/locally advanced breast cancer. We previously reported that pertuzumab showed an improved efficacy vs. placebo in terms of total pathological complete response (tpCR), event-free survival and disease-free survival (DFS), as well as in pre-defined biomarker subgroups. Limited data are available on biomarker changes after neoadjuvant treatment in the Asian population. Thus, we aim to (1) describe the biomarker changes from baseline to surgical samples after HER2 targeted neoadjuvant therapy, and (2) explore the association of surgical biomarkers and DFS. Methods: This study will focus on biomarkers of tumor tissue samples collected at baseline and surgery among 218 non-tpCR patients, due to limited samples collected at disease progression. Samples were tested using Immunohistochemistry (IHC) for HER3, PTEN, CD8 and PD-L1. Both HER2/HER3 mRNA expression and PIK3CA mutation were assessed using a PCR based methodology. The statistical analyses were using R (4.1.3). Biomarkers were categorized as high/low either by median derived from baseline samples, or with well-established cutoffs, e.g. HER2 IHC of 3+ vs. 1/2+ and PD-L1 at 1%. The changes of biomarkers were examined among the paired samples using Fisher’s exact test and depicted by alluvial plots. Cox proportional hazard regression analysis was performed to examine the association between surgical biomarkers and DFS in pooled arms. Results: Since the surgical biomarkers are only evaluable among patients with residual disease, patients in this analysis had a higher stage, and were more likely to be node+. Of 166 paired samples, more patients show a decrease in HER2 and HER3 mRNA/IHC, PTEN IHC from baseline to surgery. PIK3CA mutation status remained the same among the majority of patients (n = 154, 95%). Immune-related biomarkers, PD-L1 and CD8, are more enriched in surgical samples compared to baseline. All these observations are similar between the two arms. No clear association has been shown in most of the biomarkers, except for PIK3CA, with DFS. Surgical tumor samples with PIK3CA mutation detected trended with a worse DFS than those with no PIK3CA mutation detected [HR: 1.81 (95% CI: 0.94 – 3.49)]. Conclusions: Of baseline and surgery paired samples, most biomarkers showed a change, except for PIK3CA mutation status. In pooled analyses, we only observed a trend of PIK3CA mutation detected in surgical samples with a worse DFS, which is in line with the association observed with baseline PIK3CA mutation status. No other associations were found between surgical biomarkers and DFS. These findings may suggest that despite biomarker changes patients continue to benefit From HER2 therapies and dual blockade. Clinical trial information: NCT02586025 .

Independent censoring is usually assumed in survival data analysis. However, dependent censoring, where the survival time is dependent on the censoring time, is often seen in real data applications. In this project, we model the vector of survival time and censoring time marginally through semiparametric heteroscedastic accelerated failure time models and model their association by the vector of errors in the model. We show that this semiparametric model is identified, and the generalized estimating equation approach is extended to estimate the parameters in this model. It is shown that the estimators of the model parameters are consistent and asymptotically normal. Simulation studies are conducted to compare it with the estimation method under a parametric model. A real dataset from a prostate cancer study is used for illustration of the new proposed method.

The generation of optimal defense strategies in dynamic adversarial environments is crucial for cybersecurity. Recently, defense approaches based on evolutionary game theory have gained significant achievements. However, they would fail when facing complex networks and sophisticated attack strategies, due to the fatal drawbacks of defense strategy generation considering atomic attacks only. To relieve this issue, a generic approach for generating defense strategies using evolutionary game theory is proposed in this paper. Initially, a novel payoff quantification method for network attack-defense games based on attack graphs is designed. Innovatively, two factors concerning the decision-maker's degree of irrationality (DI) and the level of environmental security (LES) are introduced into the replicator dynamics equation to model the impacts on equilibrium solutions. Noting that Active Directory (AD) domain service is one of the most used and representative information security management system in Windows domains, from which attack graphs and paths can be plainly extracted and analyzed. Therefore, it is necessary and imperative to anchor AD to unfold the theoretical analyses and experiments validation based on a real environment. Case studies on a real-world AD network demonstrate that the proposed approach is effective and can generate stable and efficient defense strategies.

The Buckley-James method for the classical accelerated failure time model has been extended to accommodate heteroscedastic survival data in two ways. The first is the weighted least squares method [Yu et al. Weighted least-squares method for right-censored data in accelerated failure time model. Biometrics. 2013;69:358-365], which estimates the heteroscedasticity nonparametrically, while the second is the local Buckley-James method [Pang et al. Local Buckley-James estimation for heteroscedastic accelerated failure time model. Stat Sin. 2015;25:863-877], which uses local Kaplan-Meier method to estimate the heteroscedasticity. However, no comparisons have been done for these two methods. Furthermore, there is no hypothesis testing procedure for this heteroscedastic accelerated failure time model. This paper is then aimed to fill these two gaps to compare the two methods theoretically and numerically with extensive simulation studies. In addition, we propose a class of hypothesis tests for the parameters to provide a complete procedure for analysing heteroscedastic survival data. Two real data examples are used for practical illustration of the comparison and the new proposed tests.

Estimating transmission rates is a challenging yet essential aspect of comprehending and controlling the spread of infectious diseases. Various methods exist for estimating transmission rates, each with distinct assumptions, data needs, and constraints. This study introduces a novel phylogenetic approach called transRate, which integrates genetic information with traditional epidemiological approaches to estimate inter-population transmission rates. The phylogenetic method is statistically consistent as the sample size (i.e. the number of pathogen genomes) approaches infinity under the multi-population susceptible-infected-recovered model. Simulation analyses indicate that transRate can accurately estimate the transmission rate with a sample size of 200 ~ 400 pathogen genomes. Using transRate, we analyzed 40,028 high-quality sequences of SARS-CoV-2 in human hosts during the early pandemic. Our analysis uncovered significant transmission between populations even before widespread travel restrictions were implemented. The development of transRate provides valuable insights for scientists and public health officials to enhance their understanding of the pandemic's progression and aiding in preparedness for future viral outbreaks. As public databases for genomic sequences continue to expand, transRate is increasingly vital for tracking and mitigating the spread of infectious diseases.

Understanding the network characteristics of IP nodes and identifying their potential usage scenarios are crucial for cyberspace applications, such as asset evaluation, fraud prevention, and network attack prevention. However, many studies related to IP nodes primarily focused on IP geolocation and anomaly detection, with very little attention given to IP usage. Identifying the usage scenarios of IP node can facilitate network optimization, enhance security and improve resource allocation, which is crucial for network management and security. In this work, we propose a novel framework named GraphCyber based on graph neural network to identify street-level IP usage for cyberspace mapping. We first design a topology rule-based approach dividing a large number of IP nodes into regional blocks. Then we devise regional blocks to fuse the self-information of IP nodes and various neighborhood relationships into the graph. Last, based on an uncertainty-aware graph neural network, we identify the usage scenarios of IP nodes within regional blocks. Extensive experiments conducted on three large-scale real-world data sets demonstrate the superiority of GraphCyber over several state-of-the-art baselines in accurately identifying the IP usage scenarios.

The phylogeny and divergence timing of the Neoavian radiation remain controversial despite recent progress. We analyzed the genomes of 124 species across all Neoavian orders, using data from 25,460 loci spanning four DNA classes, including 5,756 coding sequences, 12,449 conserved nonexonic elements, 4,871 introns, and 2,384 intergenic segments. We conducted a comprehensive sensitivity analysis to account for the heterogeneity across different DNA classes, leading to an optimal tree of Neoaves with high resolution. This phylogeny features a novel Neoavian dichotomy comprising two monophyletic clades: a previously recognized Telluraves (land birds) and a newly circumscribed Aquaterraves (waterbirds and relatives). Molecular dating analyses with 20 fossil calibrations indicate that the diversification of modern birds began in the Late Cretaceous and underwent a constant and steady radiation across the KPg boundary, concurrent with the rise of angiosperms as well as other major Cenozoic animal groups including placental and multituberculate mammals. The KPg catastrophe had a limited impact on avian evolution compared to the Paleocene-Eocene Thermal Maximum, which triggered a rapid diversification of seabirds. Our findings suggest that the evolution of modern birds followed a slow process of gradualism rather than a rapid process of punctuated equilibrium, with limited interruption by the KPg catastrophe. This study places bird evolution into a new context within vertebrates, with ramifications for the evolution of the Earth's biota.