The settlement of piers and subgrade bending deformation are widely recognized as common issues in the transition zones of high-speed railway bridges. This study aims to investigate the settlement behavior within these transition zones and its impact on the dynamic interaction between trains and the track. To achieve this, a vehicle-track-transition zone mapping relationship model is developed to analyze both the settlement behavior and the resulting dynamic response characteristics. The study employs the finite element method and multi-body dynamics to construct the simulation model. Settlement effects are simulated using the Newton-Raphson iterative method, with the additional rail deformation caused by foundation settlement serving as the excitation for the vehicle-track-transition zone dynamic interaction system. In the numerical analysis, the dynamic effects of three key factors—train speed, transition zone length, and the amplitude of foundation settlement—are examined based on the performance of the vehicle-track-transition zone interaction. The time-frequency technique is utilized to comprehensively reveal and clarify the spatial-frequency characteristics of system responses influenced by settlement excitation. Moreover, the relationship between the safety-based settlement threshold and these three factors is calibrated.

The dynamic response of a railway bridge can be affected by the properties of its foundations, particularly if founded on soft soils. Thus, this work aims to establish a coupled dynamic model to investigate the vibration of train-track-bridge systems considering piled foundations embedded in soft soil. Firstly, to construct the simulation framework, the finite element and multi-body methods are used to model the dynamic behavior of a train-track-bridge interaction (TTBI) system and a pier-cap-pile-soil interaction system. The equilibrium of the two sub-systems is maintained through the bridge's bearing force and a multi-time-step integration strategy is introduced to improve computational efficiency. The proposed model is validated by comparing it to the results from commercial finite element software ABAQUS. Then results are computed using the proposed model and the conventional TTBI model without piles. It is concluded that when considering the piled foundations, the low-frequency vibration of the TTBI system is dominant. Moreover, the vibration energy in the track and bridge below 7 Hz is larger compared with the conventional TTBI model. The influence of train speed on the vibration characteristics of the pile and soil is analyzed. It is found that higher train speeds cause increased pile and soil accelerations at the frequencies associated with the train axle spacing. The novelty of the analysis is providing a new insight into the coupled vibration properties of TTBI systems considering the participation of piled foundations in soft soil.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy in need of effective (immuno)therapeutic treatment strategies. For the optimal application and development of cancer immunotherapies, a comprehensive understanding of local and systemic immune profiles in patients with PDAC is required. Here, our goal was to decipher the interplay between local and systemic immune profiles in treatment-naïve patients with PDAC. METHODS: The immune composition of PDAC, matched non-malignant pancreatic tissue, regional lymph nodes, spleen, portal vein blood, and peripheral blood samples (collected before and after surgery) from 11 patients with PDAC was assessed by measuring 41 immune cell markers by single-cell mass cytometry. Furthermore, the activation potential of tumor-infiltrating lymphocytes as determined by their ability to produce cytokines was investigated by flow cytometry. In addition, the spatial localization of tumor-infiltrating innate lymphocytes in the tumor microenvironment was confirmed by multispectral immunofluorescence. RESULTS: We found that CD103+CD8+ T cells with cytotoxic potential are infrequent in the PDAC immune microenvironment and lack the expression of activation markers and checkpoint blockade molecule programmed cell death protein-1 (PD-1). In contrast, PDAC tissues showed a remarkable increased relative frequency of B cells and regulatory T cells as compared with non-malignant pancreatic tissues. Besides, a previously unappreciated innate lymphocyte cell (ILC) population (CD127-CD103+CD39+CD45RO+ ILC1-like) was discovered in PDAC tissues. Strikingly, the increased relative frequency of B cells and regulatory T cells in pancreatic cancer samples was reflected in matched portal vein blood samples but not in peripheral blood, suggesting a regional enrichment of immune cells that infiltrate the PDAC microenvironment. After surgery, decreased frequencies of myeloid dendritic cells were found in peripheral blood. CONCLUSIONS: Our work demonstrates an immunosuppressive landscape in PDAC tissues, generally deprived of cytotoxic T cells and enriched in regulatory T cells and B cells. The antitumor potential of ILC1-like cells in PDAC may be exploited in a therapeutic setting. Importantly, immune profiles detected in blood isolated from the portal vein reflected the immune cell composition of the PDAC microenvironment, suggesting that this anatomical location could be a source of tumor-associated immune cell subsets.

In the era of the Internet of Things and social media; communities, governments, and corporations are increasingly eager to exploit new technological innovations in order to track and keep up to date with important new events. Examples of such events include the news, health related incidents, and other major occurrences such as earthquakes and landslides. This area of research commonly referred to as Topic Detection and Tracking (TDT) is proving to be an important component of the current generation of Internet-based applications, where it is of critical importance to have early detection and timely response to important incidents such as those mentioned above. The advent of Big data though beneficial to TDT applications also brings about the enormous challenge of dealing with data variety, velocity and volume (3Vs). A promising solution is to employ Cloud Computing, which enables users to access powerful and scalable computational and storage resources in a “pay-as-you-go” fashion. However, the efficient use of Cloud resources to boost the performance of mission critical applications employing TDT is still an open topic that has not been fully and effectively investigated. An important prerequisite is to build a performance analysis capable of capturing and explaining specific factors (for example; CPU, Memory, I/O, Network, Cloud Platform Service, and Workload) that influence the performances of TDT applications in the cloud. Within this paper, our main contribution, is that we present a multi-layered performance analysis for big data TDT applications deployed in a cloud environment. Our analysis captures factors that have an important effect on the performance of TDT applications. The novelty of our work is that it is a first kind of vertical analysis on infrastructure, platform and software layers. We identify key parameters and metrics in each cloud layer (including Infrastructure, Software, and Platform layers), and establish the dependencies between these metrics across the layers. We demonstrate the effectiveness of the proposed analysis via experimental evaluations using real-world datasets obtained from Twitter.