Recent advances in the diagnosis and management of reflux disease were the central focus of the inaugural Gatherings in Esophagology (GiE), which convened experts across gastroenterology, surgery, otolaryngology, pulmonology, and basic research. The sessions highlighted innovations in reflux monitoring-including high-resolution manometry, wireless pH monitoring, and novel salivary biomarkers-while critically evaluating their diagnostic accuracy and clinical utility. Presentations explored the limitations of traditional proton-pump inhibitor therapy, the emergence of potassium-competitive acid blockers as a new class of acid suppressants, and the evolving role of adjunctive treatments such as mucosal protectants, reflux reducers, and neuromodulators for refractory symptoms. The discourse extended to advanced interventional procedures, including transoral incisionless fundoplication, magnetic sphincter augmentation, and the RefluxStop device, with discussion of patient selection, efficacy, and complication management. Discussants emphasized the pathophysiology and management of extraesophageal manifestations of reflux, the interplay between reflux and pulmonary disease, and the diagnostic challenges in pediatric populations. The meeting also addressed the integration of behavioral therapies, the role of the microbiome, and the application of artificial intelligence in reflux diagnostics. Collectively, these insights underscore a shift toward precision medicine in reflux disease, emphasizing individualized diagnostic strategies and tailored therapeutic approaches to improve patient outcomes.

In buildings, steel-concrete composite beams with web openings save service space but weaken structural performance. To address this issue, this study proposes the use of a perfobond strip (PBL) connector in the opening area to compensate for the loss of connection performance and composite action caused by the web openings. A total of 25 specimens were subjected to experimental testing and finite element simulation to investigate the effects of 11 varying parameters-including connector type, hole radius of the perforated plate, hole position, and the diameter of through reinforcement-on the mechanical performance and composite behavior of the specimens. The results indicate that, compared to traditional stud connectors, PBL connectors increase the shear force transferred into the concrete by 13.57%-18.33%, thereby enhancing the structure's shear resistance. Specimens with PBL connectors in the opening area exhibited favorable ductile failure characteristics and improved shear capacity, with failure modes transitioning to concrete flange splitting and rebar shearing. Among the parameters studied, increasing the hole radius of the PBL plate resulted in the greatest load-bearing capacity, while increasing the diameter of the penetrating rebars most effectively reduced the relative slip behavior of the specimens.

We conducted a comprehensive first-principles investigation of the electronic structure, photoresponse, mechanical, and thermodynamic characteristics of the newly synthesized compound tetragonal-VN2 using density functional theory. The calculated lattice parameters closely align with both experimental and theoretical data. Electronic structure analysis reveals that VN2 exhibits metallic conductivity, with significant hybridization between V-3d and N-2p states, indicating strong covalent bonding. VN2 demonstrates isotropic optical properties, with a high static dielectric constant (56.9), notable reflectivity (59% at 0eV), and significant absorption in the ultraviolet-visible range. It is mechanically stable, exhibits a degree of elastic anisotropy, and possesses a brittle nature. VN2 has a Vickers hardness of 26.2GPa, approximately three times greater than that of VN, suggesting that it is suitable for use in wear-resistant coatings and cutting tools. From a thermodynamic perspective, VN2 is characterized by a high Debye temperature (1023.5 K) and significant lattice thermal conductivity (59.8 W·m- 1·K- 1). Its melting temperature, which exceeds 3177 K, distinguishes it as a promising candidate for applications that require extreme temperatures. These findings establish a strong foundation for the future applications of VN2 in advanced materials science, particularly in high-performance coatings and photonic devices.

Pseudoscientific beliefs exert a profound influence on health behaviors, political decisions, and public trust in science, yet research has primarily identified correlates of pseudoscience acceptance rather than the mechanisms by which such beliefs form and update. In this study, we leveraged computational modeling to investigate how normative social feedback shapes pseudoscientific belief revision. A total of 300 US nationally representative participants conducted a learning task where they rated a set of 20 validated pseudoscientific statements while receiving trial-by-trial feedback. Behaviorally, participants showed systematic reductions in prediction errors across trials, consistent with iterative belief updating. Computational model comparison using hierarchical Bayesian inference revealed that learning was best captured by an anchored propagation model, in which prediction errors spread across correlated beliefs but were stabilized by an anchoring parameter reflecting initial convictions. Exploratory analyses further showed that belief updating depended on the alignment between prior expectations and normative feedback, amplifying congruent information and dampening incongruent inputs. These findings provide the first mechanistic account of how pseudoscientific beliefs are simultaneously receptive to new information and resistant to change, offering an integrative framework with implications for research in belief updating, social cognition, and interventions to reduce misinformation.

During the cooling process, some components of jet fuel react with oxygen dissolved in the fluid, resulting in thermal oxidation deposition. Here, we used a novel in-situ flow experimental system to achieve the decoupling of the intricate oxidation deposition reaction flow process within the pipeline and to investigate the interaction mechanism between fluid-dissolved metal and jet fuel during oxidation deposition. Experimental results indicated that when the surface roughness doubled, the content and distribution area of surface-active metals increased by 20-fold and the deposition amount increased significantly by 339.31%. The substantial proliferation of metal active sites accelerated thermal oxidation deposition, inducing changes in both deposit morphology and internal composition. At 450°C, a notable augmentation of active metal content in RP-3-the main fuel of aeroengines in China-appeared after the thermal oxidation deposition. The temperature effect intensified the interaction process between surface metals and fuel, leading to more severe surface deposition. This study provides guidance into the mechanistic linkages between metal-surface characteristics, temperature, and jet fuel oxidation deposition, which serves as a theoretical foundation for the subsequent design and maintenance of the aeroengine cooling system.

Deep neural networks are driving the iterative advancement of magnetoencephalography (MEG) decoding models. While explainable artificial intelligence, particularly traditional post-hoc feature attribution approaches, has made significant progress in interpreting the prediction mechanisms of individual models, a critical gap remains in understanding the differences in decision logic between various models, known as model differencing. By facilitating model selection, optimization updates, and practical applications such as error pattern analysis and decision fusion, this approach demonstrates significant research and application potential. However, existing approaches face fundamental limitations: insufficient accuracy in differencing measurements, often relying on binary simplification, and deficient localization of differencing decision boundaries, typically constrained to low-dimensional spaces. To address these challenges, we propose a rule-based MEG model differencing approach called boundary-optimized rules via predict-probability difference (BO-RPPD). Key innovations include (1) a novel measurement based on predict-probability differences between dual models, enabling the direct learning of differencing rules, and (2) integrated counterfactual generation and feature reduction to guide the exploration of optimal predict-probability difference boundaries, especially within low-sample, high-dimensional MEG feature spaces. Experiments on two MEG datasets demonstrate the overall superiority of our proposed approach: prediction performance significantly outperforms all benchmarks, achieving up to a 24% improvement in F1-score and effectively covering broader samples. The number of generated rules matches the optimal benchmark, ensuring strong explainability. The approach shows practical value in error pattern analysis and decision fusion. Model-agnostic, our approach generalizes effectively to electroencephalography (EEG) and structureddatasets.

DLX3 is a homeobox transcription factor essential for multiple organogenesis processes. Mutations in DLX3 cause trichodentoosseous syndrome (TDO), characterized by curly hair, sclerotic bone, enamel, and dentin defects as well as taurodontism. Phenotypic variability in TDO has been well documented, but its pathogenesis remains poorly understood. Here, we characterized three TDO families with distinct clinical features and identified a known DLX3 deletion (c.561_562del) and the first pathogenic splice-site variant (c.516+1_516+2insA). The proband with the splice-site mutation displayed a mesenchymal-dominant phenotype with severe dentin hypoplasia, enlarged pulp chambers, and hypertaurodontism but nearly normal enamel, whereas the mother and sister showed epithelial-dominant anomalies, including enamel hypoplasia and kinky hair. Minigene analysis demonstrated that c.516+1_516+2insA generated two aberrant transcripts encoding p.Val173Aspfs*28 and p.Arg120_Val173del. These mutant proteins localized mainly in the cytoplasm and showed markedly reduced transactivation activity. In cultured human dental pulp cells, DLX3 overexpression upregulated the odontoblastic markers DSPP, MMP20, and WNT10A. Chromatin immunoprecipitation and reporter assays further revealed that DLX3 directly activates WNT10A via a conserved enhancer (chr2:218,878,973_218,879,302) and three upstream binding sites. These findings expand the TDO mutational spectrum and suggest that differential mutant DLX3 expression may contribute to phenotypic variability, whereas disrupted regulation of WNT10A underlies dentin defects and taurodontism.

Courtship is often viewed as a linear process where males display and female assessment of this display shapes her mating decisions. However, communication can be far more dynamic and interactive, particularly in species that develop long-term relationships. Interactional complexity is not well captured by traditional models of animal communication. Here, we tested whether interactional elements shape female preferences in the monogamous zebra finch. We used selective calling as a measure of female preference. First, we asked whether females' most-preferred song (based on passive song playback) matched her most-preferred male (based on a live interaction). We found a mismatch in the preferences for song versus live males, and female preferences for a live male did not appear to be linked to how much he sang. Next, to experimentally manipulate male responsiveness, we habituated females to the song of their most-preferred or least-preferred male. This song habituation caused females to change their preferences differently depending on whether they were habituated to their most- or least-preferred male. Together, these results highlight that additional interactional elements, beyond male song, impact female social preferences. More broadly, our results contribute to growing evidence that models of communication should incorporate interactional and distributed elements.

Self-supervised learning (SSL) can capture intrinsic features from extensive unlabeled data, significantly reducing dependence on labels and performing well in human activity recognition (HAR). However, existing SSL frameworks depend excessively on data augmentation paradigms, and often mistakenly treat noise as learning objectives during mask reconstruction. Moreover, the data set scale often constrains accuracy and hinders real-world applicability. To address these issues, this paper proposes a new SSL objective that integrates an attention mechanism with an adaptive time series mixer. Without relying on data augmentation, the proposed model assigns lower weights to noise for capturing the global dependencies and extracting the local feature within inertial measurement unit (IMU) series. The proposed model was validated through comprehensive evaluations of three public data sets (UCI, Motion, and HHAR) and one self-collected data set (named CQJTU-FCE). The experimental results fully demonstrate that, on the self-collected data set, the proposed model achieves an average improvement of 6.54%, 8.55%, and 7.88% in accuracy, F1 score, and Cohen's kappa coefficient, respectively, compared with the baseline models. Similarly, on the public data sets, the average enhancements reached 10.63%, 11.77%, and 13.39% across the same evaluation metrics. These results confirm the generalizability of the model to various data sets, offering a more efficient and reliable solution for HARtasks.

Group synchrony in the animal kingdom is usually associated with mating. Being in sync is likely advantageous, as it may help in luring the opposite sex. Yet there are also disadvantages-such as the homogenization of the group-which make it harder for individuals to stand-out. Here we address this trade-off, bringing together the Kuramoto model with concepts from evolutionary game theory. We focus on the existence of self-interested cheaters, which have been extensively studied in a variety of species. In our scenario, cheating individuals take part in the synchronous group display but position themselves (in terms of phase) slightly ahead of or behind the pack. This allows them to enjoy both the group benefit of advertisement and the individual benefit of being unique. But a group can only tolerate a limited number of such individuals while still achieving synchrony. We therefore incorporate a form of policing into our model: If an individual strays too far form the group's synchronous phase, they reveal themselves as dishonest and are punished. Our model offers testable predictions regarding natural population compositions, and will hopefully spur further investigation into not only how, but also why, natural systemssynchronize.