Explore the vast range of titles available.

Helicobacter pylori (HP) is a major cause of gastritis, yet the epithelial mechanisms linking infection-induced stress to mast cell and Treg responses remain poorly defined. Three datasets (GSE5081, GSE27411, and GSE233973) were integrated and analyzed using weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Mast cell-related hub gene expressions were evaluated with quantitative real-time polymerase chain reaction (qRT-PCR), and inflammatory cytokines were quantified using the enzyme-linked immunosorbent assay (ELISA). Histopathological changes were evaluated using hematoxylin and eosin (HE), Giemsa, and Warthin-Starry silver staining. Cell apoptosis was assessed by flow cytometry, and mast cell and Treg cell activities were analyzed by Transwell assays, histamine detection, and immunohistochemistry (IHC). Fos proto-oncogene (FOS), ribonucleotide reductase regulatory subunit M2 (RRM2), and RAD51 recombinase (RAD51) were identified as mast cell-related hub genes, all of which were upregulated in HP-induced gastritis mice. In vitro, HP infection or CagA stimulation increased FOS expression in gastric epithelial cells. FOS knockdown in HP-infected mice alleviated gastric mucosal injury, reduced bacterial burden, and decreased pro-inflammatory cytokine levels. FOS silencing enhanced GES-1 cell viability and suppressed apoptosis. In HP-infected GES-1 cells, FOS silencing inhibited mast cell migration, cytokine secretion, including C-C motif chemokine ligand 2 (CCL2), interleukin-33 (IL-33), and stem cell factor (SCF), as well as histamine release, accompanied by reduced Treg polarization and decreased expression of transforming growth factor-β and forkhead box P3. FOS silencing inhibited mast cell activation and Treg cell polarization in HP-induced gastritis, suggesting its promising value as an intervention point in HP-driven gastritis.

This study investigates the combined effects of partial saturation and aligned fractures on P- and S-wave velocities and anisotropy in tight sandstones. Ultrasonic measurements (0.5 MHz) were conducted on three synthetic samples with a matrix porosity of 11.7% ± 1.2% and controlled fracture densities (0%, 3.12%, and 6.24%) under a full range of water saturation rate (Sw), from dry to fully water-saturated. Experimental results reveal that for fractured samples, the P-wave anisotropy parameter ε increases sharply as Sw decreases from 100% to approximately 60%, followed by a gentler variation at lower saturation. In contrast, fracture-induced shear-wave splitting (SWS) is predominantly governed by fracture density and exhibits weak dependence on Sw. To interpret these observations, we developed a coupled rock physics framework by integrating the MJGW partial saturation model with the Galvin fracture model, introducing a distribution coefficient to account for the non-uniform water distribution between the matrix and fractures. The coupled model accurately explains the Vp and SWS trends, while the overestimation of Vs and ε is attributed to near-dry surface effects at grain contacts and mutual interaction between fractures. This work provides experimental data and modeling insights for seismic-based characterization of multiphase-fluid-saturated fractured reservoirs.

Stratigraphic forward modeling (SFM) is a numerical approach used to reconstruct sedimentary basin evolution by simulating the infilling and tectonic evolution process of strata. The challenge is that existing approaches inevitably require trade-offs among modeling fidelity and computational cost. We present a novel clastic stratigraphic forward modeling (CSFM) approach to reducing computational cost while retaining key flow and transport behaviors relevant to stratigraphic architecture. In CSFM, Lagrangian water particles affect momentum and sediment, while a fixed Eulerian grid stores topographic elevation and lithologic fractions. A simplified form of the Navier–Stokes equations is proposed to compute the trajectories of fluid particles, which can greatly reduce the computational cost. Sediment dynamics are represented by coupled suspended load and bedload modules. To validate CSFM, we constructed a synthetic alluvial fan model and performed stratigraphic forward modeling on it. Five lake-level cycles were imposed and results showed that cyclic sand–clay couplets and isolated channel sand bodies were formed during repeated progradation and backstepping. These results are consistent with established sedimentological knowledge, confirming the geological plausibility of CSFM.

Background Gastric cancer (GC) encompasses many different histological and molecular subtypes. It is a major driver of cancer mortality because of poor survival and limited treatment options. Personalised medicine in the form of patient-derived organoids (PDOs) represents a promising approach for improving therapeutic outcomes. The goal of this study was to overcome the limitations of current models by ameliorating organoid cultivation. Methods Organoids derived from cancer tissue were evaluated by haematoxylin and eosin staining, immunohistochemistry, mRNA, and whole-exome sequencing. Three representative chemotherapy drugs, 5-fluorouracil, docetaxel, and oxaliplatin, were compared for their efficacy against different subtypes of gastric organoids by ATP assay and apoptosis staining. In addition, drug sensitivity screening results from two publicly available databases, the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopaedia, were pooled and applied to organoid lines. Once key targeting genes were confirmed, chemotherapy was used in combination with poly (ADP ribose) polymerase (PARP)-targeted therapy. Results We successfully constructed GC PDOs surgically resected from GC patient tissue. PDOs closely reflected the histopathological and genomic features of the corresponding primary tumours. Whole-exosome sequencing and mRNA analysis revealed that changes to the original tumour genome were maintained during long-term culture. The drugs caused divergent responses in intestinal, poorly differentiated intestinal, and diffuse gastric cancer organoids, which were confirmed in organoid lines. Poorly differentiated intestinal GC patients benefited from a combination of 5-fluorouracil and veliparib. Conclusion The present study demonstrates that combining chemotherapy with PARP targeting may improve the treatment of chemotherapy-resistant tumours.

A novel biomass-based magnetic nanoparticle (Fe3O4-P-CMC/PAMAM) was synthesized by crosslinking carboxymethyl chitosan (CMC) and poly(amidoamine) (PAMAM), followed by phosphorylation with the incorporation of magnetic ferric oxide nanoparticles. The characterization results verified the successful functionalization and structural integrity of the adsorbents with a surface area of ca. 43 m2/g. Batch adsorption experiments revealed that the adsorbent exhibited a maximum adsorption capacity of 1513.47 mg·g−1 for U(VI) at pH 5.5 and 298.15 K, with Fe3O4-P-CMC/G1.5-2 showing the highest affinity among the series. The adsorption kinetics adhered to a pseudo-second-order model (R2 = 0.99, qe,exp = 463.81 mg·g−1, k2 = 2.15×10−2 g·mg−1·min−1), indicating a chemically driven process. Thermodynamic analysis suggested that the adsorption was endothermic and spontaneous (ΔH° = 14.71 kJ·mol−1, ΔG° = −50.63 kJ·mol−1, 298. 15 K), with increasing adsorption capacity at higher temperatures. The adsorbent demonstrated significant selectivity for U(VI) in the presence of competing cations, with Fe3O4-P-CMC/G1.5-2 showing a high selectivity coefficient. The performed desorption and reusability tests indicated that the adsorbent could be effectively regenerated using 1M HCl, maintaining its adsorption capacity after five cycles. XPS analysis highlighted the role of phosphonate and amino groups in the complexation with uranyl ions, and validated the existence of bimodal U4f peaks at 380.1 eV and 390.1 eV belonging to U 4f7/2 and U 4f5/2. The results of this study underscore the promise of the developed adsorbent as an effective and selective material for the treatment of uranium-contaminated wastewater.

In this paper, we consider a first-order autoregressive process with a general autoregressive coefficient. Asymptotic behaviors of an M-estimator of the autoregressive coefficient are established for the nearly stationary and mildly explosive cases, respectively. The rate of convergence of the robust estimators for the two cases are provided. The results extend ones for the least squares and least absolute deviation estimators to the robust estimator under the weaker initial conditions in the literature. Some simulations are carried out to assess the performance of our procedure.

The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Therefore, in this study, we propose a novel algorithm, Stereo Mixture Density Network (SMNDNet), which can detect multiple types of deepfake face manipulations using a single network framework. SMNDNet is an end-to-end CNN-based network specially designed for detecting various manipulation types of deepfake face images. First, we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged features. Second, we introduce a Multi-Scale Forged Region Adaptive Module that dynamically adapts to extract forged features from images of varying synthesis scales. Third, we integrate a Nonlinear Expression Capability Enhancement Module to augment the model’s capacity for capturing intricate nonlinear patterns across various types of deepfakes. Collectively, these modules empower our model to efficiently extract forgery features from diverse manipulation types, ensuring a more satisfactory performance in multiple-types deepfake detection. Experiments show that the proposed method outperforms alternative approaches in detection accuracy and AUC across all four types of deepfake images. It also demonstrates strong generalization on cross-dataset and cross-type detection, along with robust performance against post-processing manipulations.

For the first-order autoregressive model, we establish the asymptotic theory of the weighted least squares estimations whether the underlying autoregressive process is stationary, unit root, near integrated or even explosive under a weaker moment condition of innovations. The asymptotic limit of this estimator is always normal. It is shown that the empirical log-likelihood ratio at the true parameter converges to the standard chi-square distribution. An empirical likelihood confidence interval is proposed for interval estimations of the autoregressive coefficient. The results improve the corresponding ones of Chan et al. (Econ Theory 28:705–717, 2012). Some simulations are conducted to illustrate the proposed method.

Understanding user interests is crucial for Click-Through Rate (CTR) prediction tasks. In sequential recommendation, pre-training from user historical behaviors through self-supervised learning can better comprehend user dynamic preferences, presenting the potential for direct integration with CTR tasks. Previous methods have integrated pre-trained models into downstream tasks with the sole purpose of extracting semantic information or well-represented user features, which are then incorporated as new features. However, these approaches tend to ignore the additional inference costs to the downstream tasks, and they do not consider how to transfer the effective information from the pre-trained models for specific estimated items in CTR prediction. In this paper, we propose a Sequential Recommendation Pre-training framework for CTR prediction (SRP4CTR) to tackle the above problems. Initially, we discuss the impact of introducing pre-trained models on inference costs. Subsequently, we introduced a pre-trained method to encode sequence side information concurrently.During the fine-tuning process, we incorporate a cross-attention block to establish a bridge between estimated items and the pre-trained model at a low cost. Moreover, we develop a querying transformer technique to facilitate the knowledge transfer from the pre-trained model to industrial CTR models. Offline and online experiments show that our method outperforms previous baseline models.

Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging inherent intents (e.g., always preferring high-quality items) and changing demand intents (e.g., wanting a T-shirt in summer but a down jacket in winter). However, both types of intents are implicitly expressed in recommendation scenario, posing challenges in leveraging them for accurate intent-aware recommendations. Fortunately, in search scenario, often found alongside recommendation on the same online platform, users express their demand intents explicitly through their query words. Intuitively, in both scenarios, a user shares the same inherent intent and the interactions may be influenced by the same demand intent. It is therefore feasible to utilize the interaction data from both scenarios to reinforce the dual intents for joint intent-aware modeling. But the joint modeling should deal with two problems: 1) accurately modeling users' implicit demand intents in recommendation; 2) modeling the relation between the dual intents and the interactive items. To address these problems, we propose a novel model named Unified Dual-Intents Translation for joint modeling of Search and Recommendation (UDITSR). To accurately simulate users' demand intents in recommendation, we utilize real queries from search data as supervision information to guide its generation. To explicitly model the relation among the triplet , we propose a dual-intent translation propagation mechanism to learn the triplet in the same semantic space via embedding translations. Extensive experiments demonstrate that UDITSR outperforms SOTA baselines both in search and recommendation tasks.