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Transformer-driven automated analysis of social media narrative structure: An exploration based on sentiment framing and thematic agenda

With the rapid development of social media, narrative texts in public event scenarios have become important carriers of public opinion, making the automatic analysis of social media narrative structures increasingly crucial. Existing research on this task suffers from insufficient integration of multi-dimensional information such as sentiment, topic and time, and poor adaptability to complex scenarios like cross-events and noisy texts. To address these issues, this study proposes a sentiment-topic-temporal attention fusion model (ST-TAN), which takes RoBERTa as the basic semantic encoding module and integrates three core modules to realize joint modeling of sentiment and topic and capture temporal dependence of narrative units. Experimental results show that the ST-TAN model comprehensively outperforms four types of baseline models in narrative structure recognition, sentiment classification and topic classification tasks, with good cross-event generalization ability and noisy text robustness. This research enriches the theoretical connotation of social media narrative analysis and provides effective technical support for practical fields such as public event governance and public opinion monitoring. The study further incorporates a comprehensive discussion of ethical considerations, addressing user privacy, data anonymization, potential biases, and responsible use, thereby ensuring alignment with responsible innovation principles.

Unveiling the impact of workpiece temperature on Ti6Al4V micro-drilling through a comparative analysis of cryogenic and thermally assisted drilling

Abstract Titanium alloys are widely classified as difficult-to-cut materials due to their low thermal conductivity and high chemical reactivity, which present significant machining challenges. This study provides a pioneering comparative analysis of the micro-drilling machinability under cryogenic cooling (−196 °C) and thermally assisted drilling (TAD) using PID-controlled induction heating at 250 °C and 500 °C. Experimental results revealed that while cryogenic drilling increased thrust forces by approximately 38% compared to room temperature conditions, it exhibited superior performance in terms of chip morphology and surface integrity by reducing surface roughness (R a ) values to as low as 0.34 μm. Conversely, TAD at 250 °C offered no machinability benefits, representing a thermal dead zone within the experimental constraints of the present study. However, at 500 °C, thrust forces decreased by approximately 13% due to thermal softening, leading to significant improvements in burr morphology despite the increased risk of chip clogging and severe built-up layer formation due to adhesion. Additionally, regardless of the thermal environment, lower cutting speeds were found to be essential for maintaining process stability.

High-rate phase association with travel time neural fields

Influence of symptom burden on physical activity among patients with atrial fibrillation: The chain-mediating roles of exercise sensitivity and kinesiophobia

Objective Grounded in an integrated fear-avoidance and cognitive-behavioural framework, this study aimed to examine the independent and sequential mediating roles of exercise sensitivity and kinesiophobia in the relationship between symptom burden and physical activity in patients with atrial fibrillation. Methods This cross-sectional study recruited 533 patients with atrial fibrillation from five tertiary hospitals in Chongqing, China, between April and October 2024 using convenience sampling. Symptom burden was assessed using the University of Toronto Atrial Fibrillation Severity Scale (symptom subscale), exercise sensitivity with the Exercise Sensitivity Questionnaire, kinesiophobia with the Tampa Scale for Kinesiophobia Heart, and physical activity with the International Physical Activity Questionnaire-Short Form. Data were analysed using SPSS 25.0, and chain-mediation analysis was performed with the PROCESS macro (Model 6, 5000 bootstrap resamples). Results Symptom burden was significantly and directly associated with reduced physical activity (accounting for 37.00% of the total effect). Three significant indirect pathways were identified: through exercise sensitivity alone (27.60% of the total effect), through kinesiophobia alone (16.17%), and sequentially through exercise sensitivity and kinesiophobia (19.22%). The total indirect effect accounted for 62.99% of the total effect; the 95% bootstrap confidence intervals for all indirect effects did not contain zero. Conclusion Exercise sensitivity and kinesiophobia were identified as statistically significant independent and sequential mediators between symptom burden and physical activity in patients with atrial fibrillation. These results suggest that routine screening and interventions addressing these psychological factors may be beneficial in atrial fibrillation management.

SARS-CoV-2 inhibitory, anti-inflammatory, antibacterial activity of black chokeberry (Aronia melanocarpa (Michx.) Elliott) branches’ extracts and proanthocyanidins, and their synergistic interaction with antibiotics

Impaired IFNγ responsiveness of monocyte-derived lung cells limits immunity to Mycobacterium tuberculosis

Abstract Lung mononuclear phagocyte subsets differ in their ability to restrict Mycobacterium tuberculosis (Mtb) during chronic infection, yet the mechanisms underlying this difference are not well defined. Here, we show that CD11c lo monocyte-derived cells, the subset of lung cells that is most permissive for Mtb viability during chronic infection, express lower levels of interferon-gamma (IFNγ) signaling proteins, resulting in reduced responses to IFNγ compared to alveolar macrophages and CD11c hi monocyte-derived cells. Moreover, type I IFN signaling suppresses IFNγ-mediated MHC class II expression, impairing antigen-specific CD4 T cell activation by CD11c lo monocyte-derived cells. Importantly, prior immunity conferred by contained Mtb infection enhances IFNγ responsiveness of monocyte-derived cells, reducing bacterial burdens in lungs and within monocyte-derived cell subsets. Our findings indicate that heterogeneous IFNγ responsiveness is exploited by Mtb for persistence in vivo. Overcoming or bypassing impaired IFNγ responsiveness may guide the development of more effective tuberculosis vaccines and host-directed therapies.

Correction: The association between RDW-to-platelet ratio and in-hospital mortality in critically ill stroke patients: A retrospective cohort study based on the eICU database

Long-term treatment outcomes of immune checkpoint inhibitor-related neuropathies: a French multicenter cohort study

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor

Abstract The two-qubit controlled-not (C-NOT) gate is an essential component for gate-based quantum circuits. In fact, its operation, combined with single qubit rotations allows to realise any quantum circuit. Several strategies have been adopted in order to build quantum gates. Among them, photonics offers the dual advantage of excellent isolation from the environment and ease of manipulation at the single qubit level. Here we adopt a scalable time-multiplexed approach in order to build a fully reconfigurable architecture capable of implementing a post-selected C-NOT gate with a fidelity of (93.8 ± 1.4)%. We then show how our time-multiplexed platform can be employed to combine a C-NOT and a single qubit gate in order to generate the four Bell states.

Correction: Resilient distributed model predictive control for cooperative microgrids under communication loss with demand response integration

One-dimensional mid-IR defect-mode photonic crystal biosensor for oral cancer detection

Abstract A graphene-assisted one-dimensional photonic crystal biosensor for oral cancer detection in the mid-infrared region is theoretically proposed and analyzed using the transfer matrix method. The designed structure consists of alternating high- and low-refractive-index dielectric layers surrounding an oral-tissue defect cavity that supports a highly localized defect-mode resonance inside the photonic band gap. Variations in the refractive index of healthy and cancerous oral tissues modify the optical path length of the cavity and induce measurable resonance wavelength shifts, forming the basis of the sensing mechanism. The proposed biosensor exhibits a high refractive-index sensitivity of 1629.82 nm/RIU with excellent linearity (R 2  = 0.9997). In addition, the structure demonstrates a narrow resonance linewidth with an average full width at half maximum of 17.01 nm and a high quality factor of 470.25. The obtained figure of merit, detection accuracy, and limit of detection are 95.81 RIU − 1 , 0.0588 nm − 1 , and 0.0104 RIU, respectively. Reflectance and transmittance analyses confirm the formation of a stable localized defect mode, while electric-field distribution maps reveal strong electromagnetic confinement inside the oral-tissue cavity, leading to enhanced light–matter interaction and improved sensing performance. Furthermore, angular-response analysis, defect-thickness optimization, and fabrication-tolerance evaluation demonstrate the robustness and stability of the proposed design under practical operating conditions. The obtained results indicate that the proposed graphene-assisted photonic crystal biosensor provides a promising platform for highly sensitive and reliable oral cancer detection in the mid-infrared spectral region.

Deep residual learning for molecular force fields

Abstract Accurate descriptions of interactions between atoms are essential for molecular simulations used to study biology and support drug discovery. Existing force fields often face a trade-off between physical reliability, computational efficiency, and accuracy across unfamiliar molecules. Here we show that Residual Learning Force Field, a hybrid machine learning force field, can reduce this trade-off by combining simple physics-based descriptions of bonded interactions with learned corrections for remaining energetic effects. The two components are trained together through a three-step strategy so that each contributes complementary information. In tests covering drug-like molecules, molecular dimers, torsional energy profiles, energy-minimum structures, and biomolecular simulations, Residual Learning Force Field gives accurate and stable predictions across diverse systems. These results suggest that combining physical constraints with data-driven corrections can provide a practical route toward more reliable and efficient molecular simulation for biological research and drug discovery.

Evaluation of coupling and coordination, obstacle diagnosis, and optimization pathways of medical–preventive integration in primary healthcare institutions in Hebei Province from 2017 to 2024

Medical–prevention integration constitutes a core governance strategy for advancing the Healthy China initiative, with primary healthcare institutions serving as the foundational delivery platforms for its implementation. To systematically evaluate the development level, spatiotemporal evolution, and key constraining factors of medical–prevention integration at the primary level, this study takes all township health centers and community health service centers in Hebei Province as the research sample. Drawing on panel data from the Hebei Health Statistical Yearbook covering 2017–2024, an evaluation index system for the coupling and coordinated development of primary medical services and public health services was constructed and authoritatively validated using a modified Delphi method. Indicator weights were determined through the entropy method, and an integrated quantitative analytical framework was employed, incorporating the coupling coordination degree model, the relative development model, and the obstacle degree model. The results indicate that primary medical–prevention integration in Hebei Province exhibits a typical pattern of “high coupling but low coordination”. Although medical and public health services have established a relatively stable institutional linkage, their overall coordinated development remains at a persistently low level, with medical services generally outperforming public health services. Township health centers demonstrate significantly higher composite development levels and coupling coordination degrees than community health service centers, forming a pronounced urban–rural dual structure in primary-level medical–prevention integration. Furthermore, substantial heterogeneity exists between the two types of institutions in terms of relative development structures and core obstacle factors. Lagging public health service capacity, along with misaligned financing and incentive mechanisms, constitutes the principal bottleneck constraining high-quality development of medical–prevention integration. By uncovering the underlying structural and institutional tensions within primary-level medical–prevention integration, this study provides robust empirical evidence and policy-relevant insights for optimizing integration strategies and advancing the construction of an integrated healthcare delivery system in Hebei Province and other comparable regions in China.

Evaluating the curcumin-loading ability of a complex of zein and nuclei formed from fibrillar whey protein isolate

Atomically dispersed Ce species adjacent to Ni nanoparticles as hydrogen scavengers for efficient catalytic ammonia decomposition

Stress hyperglycemia ratio predicts mid- to long-term mortality in first-hospitalized type 2 diabetes: Nonlinear threshold and prognostic value

Background The stress hyperglycemia ratio (SHR) can more accurately reflect acute glycemic dysregulation by incorporating chronic glycemic levels. However, its association with mid- to long-term all-cause mortality in first-hospitalized patients with type 2 diabetes mellitus (T2DM), as well as its nonlinear characteristics, threshold effect and incremental predictive value, remain unclear. Methods This single-center retrospective cohort study enrolled 1,147 first-hospitalized T2DM patients. The prognostic value of SHR was evaluated using restricted cubic spline analysis, threshold effect analysis, Cox regression, subgroup analysis, sensitivity analysis and time-dependent receiver operating characteristic (ROC) curves. Results SHR was nonlinearly associated with all-cause mortality at all follow-up time points, with an optimal threshold of 1.08 (both P < 0.05). Each 0.1-unit increase in SHR was associated with a 10% higher risk of 3-year and 5-year mortality (HR = 1.10). SHR ≥ 1.08 was associated with increased risks of 3-year and 5-year mortality (HR = 2.38 and 2.28, both P < 0.001). Sensitivity analysis confirmed the robustness of results. Subgroup analysis showed that myocardial infarction, congestive heart failure and cerebrovascular disease significantly modified the prognostic effect of SHR. Time-dependent ROC demonstrated favorable predictive performance with AUC > 0.80 at all time points. Moreover, SHR resulted in modest improvements in AUC (0.825 → 0.835; 0.813 → 0.823) and C-index (0.792 → 0.802), outperforming traditional glycemic indicators. Conclusion SHR is independently and nonlinearly associated with mid- to long-term all-cause mortality in first-hospitalized T2DM patients (threshold = 1.08), and may provide incremental prognostic information for risk stratification.

Attention-guided low-light image enhancement only via dark instances

Abstract High-quality images are essential for computer vision tasks. Low-light images often suffer from various degradations due to limited capture conditions, which adversely affect subsequent tasks. In this work, we propose a simple yet effective low-light image enhancement(LLIE) method, termed UALIE, which is inspired by the Retinex theory and decomposes the original image into illumination and reflectance components. First, to extract rich features at multiple levels, the network adopts a U-Net inspired design that fuses features from multiple branches to capture multi-level information. Second, to further guide the network toward more informative features, we introduce the attention mechanism that enables the model to automatically focus on important regions. Finally, our method does not require normal-light reference images during training, but instead learns from paired low-light instances only. This significantly reduces the dependency on extensive labeled data. Extensive experiments demonstrate that UALIE outperforms state-of-the-art techniques on several public datasets.

Targeted Protein Degradation of NUDT5 Dissociates Catalytic Inhibition from Protein Loss in 6-Thioguanine Response

Abstract 6-Thioguanine (6-TG) is an FDA-approved antimetabolite drug that is widely used clinically, including for the treatment of leukemia. Its cellular effects require metabolic activation and are regulated through interactions with various proteins such as NUDT15, which catalyzes the hydrolysis of the active 6-TG metabolites 6-thio-deoxyGTP (6-thio-dGTP) and 6-thio-GTP. Recent genome-wide CRISPR loss-of-function studies have identified another NUDIX hydrolase, NUDT5, as a crucial mediator of 6-TG toxicity. Here, we develop and validate a selective, cell-active NUDT5 degrader toolkit and orthogonally characterize target engagement, ternary complex formation, degradation kinetics, and proteome-wide selectivity. These degraders, in conjunction with orthogonal CRISPR knock-out and reconstitution experiments, support a non-enzymatic role for NUDT5 in modulating the cellular response to 6-TG. Depletion of NUDT5 protein is antagonistic to NUDT15 inhibition, suggesting a distinct mode-of-action with potential implications for patient therapy.

High-grade endometrial stromal sarcoma heterogeneity: Fusion-driven, fusion-negative, and related undifferentiated uterine sarcomas

Robust policy evaluation from large-scale observational studies

Under the current policy decision making paradigm we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal relations. However, in one-to-one matching, when a treatment or control unit has multiple pair assignment options with similar match quality, different matching algorithms often assign different pairs. Since all the matching algorithms assign pairs without considering the outcomes, it is possible that with the same data and same hypothesis, different experimenters can reach different conclusions creating an uncertainty in policy decision making. This problem becomes more prominent in the case of large-scale observational studies as there are more pair assignment options. Recently, a robust approach has been proposed to tackle the uncertainty that uses an integer programming model to explore all possible assignments. Though the proposed integer programming model is very efficient in making robust causal inference, it is not scalable to big data observational studies. With the current approach, an observational study with 50,000 samples will generate hundreds of thousands binary variables. Solving such integer programming problem is computationally expensive and becomes even worse with the increase of sample size. In this work, we consider causal inference testing with binary outcomes and propose computationally efficient algorithms that are adaptable for large-scale observational studies. By leveraging the structure of the optimization model, we propose a robustness condition that further reduces the computational burden. We validate the efficiency of the proposed algorithms by testing the causal relation between the Medicare Hospital Readmission Reduction Program (HRRP) and non-index readmissions (i.e., readmission to a hospital that is different from the hospital that discharged the patient) from the State of California Patient Discharge Database from 2010 to 2014. Our result shows that HRRP does not have a causal relation with the increase in non-index readmissions. The proposed algorithms proved to be highly scalable in testing causal relations from large-scale observational studies.