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Cervical cancer is one of the most frequent cancers in women worldwide, yet the early detection and treatment of lesions via regular cervical screening have led to a drastic reduction in the mortality rate. However, the routine examination of screening as a regular health checkup of women is characterized as time-consuming and labor-intensive, while there is lack of characteristic phenotypic profile and quantitative analysis. In this research, over the analysis of a privately collected and manually annotated dataset of 130 cytological whole-slide images, the authors proposed a deep-learning diagnostic system to localize, grade, and quantify squamous cell abnormalities. The system can distinguish abnormalities at the morphology level, namely atypical squamous cells of undetermined significance, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, and squamous cell carcinoma, as well as differential phenotypes of normal cells. The case study covered 51 positive and 79 negative digital gynecologic cytology slides collected from 2016 to 2018. Our automatic diagnostic system demonstrated its sensitivity of 100% at slide-level abnormality prediction, with the confirmation with three pathologists who performed slide-level diagnosis and training sample annotations. In the cellular-level classification, we yielded an accuracy of 94.5% in the binary classification between normality and abnormality, and the AUC was above 85% for each subtype of epithelial abnormality. Although the final confirmation from pathologists is often a must, empirically, computer-aided methods are capable of the effective extraction, interpretation, and quantification of morphological features, while also making it more objective and reproducible.

The scalable synthesis of high-quality graphite materials remains a formidable challenge due to the inherent trade-off between crystalline perfection and manufacturing efficiency. Existing forms of graphite, such as highly oriented pyrolytic graphite (HOPG) and Kish graphite, suffer from sluggish pyrolytic processes, limited carbon diffusion rates and energy-intensive protocols, often requiring several days for production. Here, we report a pulsed Joule heating-induced carburization (PJHIC) strategy that exploits transient non-equilibrium states to enable rapid carbon diffusion and segregation in metal substrates. By applying instantaneous thermal shocks ( > 1300 °C, > 300 °C/s heating rate) to solid carbon precursor-coated nickel and cobalt foils, we demonstrate the rapid carbon transport in bulk metals and achieve a vertical graphite growth rate of 730 nm/min, which is an order of magnitude faster than conventional methods. Cyclic temperature pulses further enable the synthesis of 1–5 μm-thick ABA-stacked graphite films with millimeter-scale grain sizes. The resulting rapid epitaxially grown graphite films exhibit a highly ordered crystalline structure and exceptional thermal conductivity (1314 W m –1 K –1 ), comparable to high-quality HOPG and Kish graphite. This work establishes a non-equilibrium synthesis paradigm for high-quality layered materials, bridging atomic-scale precision with industrial-scale manufacturing. Here, a pulsed Joule heating strategy for the synthesis of high-quality graphite is developed achieving a growth rate of up to 730 nm min -1 , an order of magnitude faster than conventional approaches, and producing large-area films with excellent thermal conductivity.

spp., facultative intracellular pathogens that cause brucellosis, drive pathogenesis by invading host cells and establishing intracellular persistence. While their molecular mechanisms are well-characterized, how induces chromatin restructuring in host cells remains poorly understood, representing a critical gap in host-pathogen interaction research. Using an established infection model of -infected RAW264.7 murine macrophages, we integrated Hi-C, ATAC-seq, and RNA-seq to generate multi-omics datasets. Multidimensional comparative genomics approaches were employed to systematically map infection-induced changes in host chromatin architecture and functional genomic organization. Our findings unveiled substantial alterations in the host chromatin architecture, characterized by a reduction in B-B compartment regions interactions, an increase in A-B compartment interactions, and diminished long-range chromatin contacts. Crucially, reshaped chromatin compartmentalization, activating interferon-stimulated genes (ISGs) in regions transitioning from compartment B to A. Enhanced sub-TADs interactions within ISG clusters further facilitated their coordinated expression. Additionally, infection remodeled chromatin loop structures, strengthening interactions linked to immune-related gene activation. These results demonstrate that host cells undergo substantial chromatin remodeling during acute infection as a defense mechanism against pathogen invasion. Our findings provide critical insights into host-pathogen interactions and suggest potential epigenetic targets for managing brucellosis.

Non-human primates (NHPs) can adapt to conditions outside of their natural habitat and climatic ranges but this can be influenced by inherent evolutionary traits or plasticity of species that evolved in diverse environmental conditions. In this study, we investigated how five species of NHPs that have natural distributions across a range of climatic conditions responded to seasonal temperature changes in a captive environment. The activity levels of NHPs were affected by temperature changes over the season, where activity levels were generally reduced at the lower and higher temperature ranges. Species that are naturally found within narrower and warmer climatic ranges, compared to those found in colder environments with wider fluctuations in temperature, showed more marked changes in activity levels in response to temperature changes. In lower temperature conditions, three out of five species showed significantly lower activity levels; whereas in higher temperature conditions, the activity levels of all species did not significantly decrease. The frequency of thermoregulation behaviours was higher, and use of artificial thermoregulatory sources lower, for species that did not substantially adjust their activity levels in different temperature conditions. Our results suggest that NHPs largely retained the evolutionary traits related to thermoregulation, according to the different ambient conditions they evolved in and may have low behavioural plasticity in adapting to conditions outside of their natural ranges. These results provide insights for improving conservation and captive management and may have implications for understanding NHP resilience to the increasing impact of global climate change.

Submerged macrophytes are the main components of lake ecosystems, and restoration of submerged macrophyte community is an important part of restoring lake ecosystems. Taking Water Ecological Restoration Projects in Wuhan Donghu Lake as an example, Combined with the current situation of the Donghu Lake environment, the submerged phytoremediation area, the restoration species of each sub-lake and the submerged plant configuration plan were determined. The planting of submerged macrophyte adopts the process of “selecting the area - removing debris and repelling fish ecologically - improving the substrate - setting the line planting”. Since the project was implemented , the submerged macrophyte grow well, the coverage of plants reaches more than 70%, and the transparency can reach more than 120cm, which meets the design requirements basically.

Cervical cancer is one of the most frequent cancers in women worldwide, yet the early detection and treatment of lesions via regular cervical screening have led to a drastic reduction in the mortality rate. However, the routine examination of screening as a regular health checkup of women is characterized as time-consuming and labor-intensive, while there is lack of characteristic phenotypic profile and quantitative analysis. In this research, over the analysis of a privately collected and manually annotated dataset of 130 cytological whole-slide images, the authors proposed a deep-learning diagnostic system to localize, grade, and quantify squamous cell abnormalities. The system can distinguish abnormalities at the morphology level, namely atypical squamous cells of undetermined significance, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, and squamous cell carcinoma, as well as differential phenotypes of normal cells. The case study covered 51 positive and 79 negative digital gynecologic cytology slides collected from 2016 to 2018. Our automatic diagnostic system demonstrated its sensitivity of 100% at slide-level abnormality prediction, with the confirmation with three pathologists who performed slide-level diagnosis and training sample annotations. In the cellular-level classification, we yielded an accuracy of 94.5% in the binary classification between normality and abnormality, and the AUC was above 85% for each subtype of epithelial abnormality. Although the final confirmation from pathologists is often a must, empirically, computer-aided methods are capable of the effective extraction, interpretation, and quantification of morphological features, while also making it more objective and reproducible.

Influence function, a technique rooted in robust statistics, has been adapted in modern machine learning for a novel application: data attribution -- quantifying how individual training data points affect a model's predictions. However, the common derivation of influence functions in the data attribution literature is limited to loss functions that can be decomposed into a sum of individual data point losses, with the most prominent examples known as M-estimators. This restricts the application of influence functions to more complex learning objectives, which we refer to as non-decomposable losses, such as contrastive or ranking losses, where a unit loss term depends on multiple data points and cannot be decomposed further. In this work, we bridge this gap by revisiting the general formulation of influence function from robust statistics, which extends beyond M-estimators. Based on this formulation, we propose a novel method, the Versatile Influence Function (VIF), that can be straightforwardly applied to machine learning models trained with any non-decomposable loss. In comparison to the classical approach in statistics, the proposed VIF is designed to fully leverage the power of auto-differentiation, hereby eliminating the need for case-specific derivations of each loss function. We demonstrate the effectiveness of VIF across three examples: Cox regression for survival analysis, node embedding for network analysis, and listwise learning-to-rank for information retrieval. In all cases, the influence estimated by VIF closely resembles the results obtained by brute-force leave-one-out retraining, while being up to \\(10^3\\) times faster to compute. We believe VIF represents a significant advancement in data attribution, enabling efficient influence-function-based attribution across a wide range of machine learning paradigms, with broad potential for practical use cases.

Time series generation (TSG) is widely used across domains, yet most existing methods assume regular sampling and fixed output resolutions. These assumptions are often violated in practice, where observations are irregular and sparse, while downstream applications require continuous and high-resolution TS. Although Neural Controlled Differential Equation (NCDE) is promising for modeling irregular TS, it is constrained by a single dynamics function, tightly coupled optimization, and limited ability to adapt learned dynamics to newly generated samples from the generative model. We propose Diff-MN, a continuous TSG framework that enhances NCDE with a Mixture-of-Experts (MoE) dynamics function and a decoupled architectural design for dynamics-focused training. To further enable NCDE to generalize to newly generated samples, Diff-MN employs a diffusion model to parameterize the NCDE temporal dynamics parameters (MoE weights), i.e., jointly learn the distribution of TS data and MoE weights. This design allows sample-specific NCDE parameters to be generated for continuous TS generation. Experiments on ten public and synthetic datasets demonstrate that Diff-MN consistently outperforms strong baselines on both irregular-to-regular and irregular-to-continuous TSG tasks. The code is available at the link https://github.com/microsoft/TimeCraft/tree/main/Diff-MN.

The advancement of generative AI has given rise to pressing copyright challenges, especially within the music industry. This paper focuses on the economic aspects of these challenges, emphasizing that the economic impact constitutes a central issue in the copyright arena. Furthermore, the complexity of the black-box generative AI technologies not only suggests but necessitates algorithmic solutions. Yet, such solutions have been largely missing, exacerbating regulatory hurdles in this landscape. We seek to address this gap by proposing viable royalty models for revenue sharing on AI music generation platforms. We start by examining existing royalty models utilized by platforms like Spotify and YouTube, and then discuss how to adapt them to the unique context of AI-generated music. A significant challenge emerging from this adaptation is the attribution of AI-generated music to influential copyrighted content in the training data. To this end, we present algorithmic solutions employing data attribution techniques. We also conduct a range of experiments to verify the effectiveness and robustness of these solutions. This research is one of the early attempts to integrate technical advancements with economic and legal considerations in the field of music generative AI, offering a computational copyright solution for the challenges posed by the opaque nature of AI technologies.

Training data attribution (TDA) plays a critical role in understanding the influence of individual training data points on model predictions. Gradient-based TDA methods, popularized by influence function for their superior performance, have been widely applied in data selection, data cleaning, data economics, and fact tracing. However, in real-world scenarios where commercial models are not publicly accessible and computational resources are limited, existing TDA methods are often constrained by their reliance on full model access and high computational costs. This poses significant challenges to the broader adoption of TDA in practical applications. In this work, we present a systematic study of TDA methods under various access and resource constraints. We investigate the feasibility of performing TDA under varying levels of access constraints by leveraging appropriately designed solutions such as proxy models. Besides, we demonstrate that attribution scores obtained from models without prior training on the target dataset remain informative across a range of tasks, which is useful for scenarios where computational resources are limited. Our findings provide practical guidance for deploying TDA in real-world environments, aiming to improve feasibility and efficiency under limited access.