Effects of the Non-Newtonian Rheology on the Fluid-Structure Interactions in Biological Flows

Fluid-structure interactions (FSIs) in non-Newtonian fluid flows are found in many industrial processes and biological systems, and the non-Newtonian rheology has significant effects on FSIs. However, it is a challenge to model FSIs involving non-Newtonian flows due to their complex characteristics. In this work, FSIs involving non-Newtonian flows are investigated by the immersed boundary-lattice Boltzmann method (IB-LBM). Firstly, an efficient and accurate IB-LBM solver for FSIs involving non-Newtonian fluids is developed. The governing equations for non-Newtonian fluids are solved by the lattice Boltzmann method. Several solid structures including 2D and 3D rigid and deformable particles, filaments and flags are considered, and the interaction between the fluid and solid structures is achieved by the immersed boundary method. Validation cases against previous experimental and numerical results confirm the accuracy of the present solver. Secondly, the dynamics of 2D and 3D capsules in Newtonian and viscoelastic shear flows is studied. The results show that the Reynolds number, the non-dimensional shear rate, the bending stiffness and the internal-to-external viscosity ratio may influence the behaviours of a capsule in a Newtonian shear flow. In addition, the capsules in viscoelastic shear flows are found to experience smaller deformations at lower Weissenberg numbers (Wi) and continuous increasing deformation when Wi is sufficiently high. Thirdly, the behaviours of a flexible filament in Newtonian and viscoelastic (Giesekus and FENE-CR) uniform flows are investigated. It is found that the Reynolds number promotes the flapping motion of the filament. The viscoelasticity of the Giesekus fluid facilitates the flapping motion of the filament. In contrast, the viscoelasticity of the FENE-CR fluid hinders the flapping motion. Finally, the behaviours of a capsule in a contraction-expansion microchannel are studied. The results show that the capsule tends to focus to different equilibrium trajectories in a Newtonian fluid at a lower confinement depending on the initial position. In contrast, the viscoelasticity of the fluid caused the same equilibrium trajectory which is independent of the initial position of the capsule. In addition, at higher confinement, the capsule migrates to a lower equilibrium trajectory with increasing Wi

Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we still know little about how they perform in terms of interpolation and extrapolation. In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. Firstly, we examine existing ad-hoc search benchmarks from the two perspectives. We investigate the distribution of training and test data and find a considerable overlap in query entities, query intent, and relevance labels. This finding implies that the evaluation on these test sets is biased toward interpolation and cannot accurately reflect the extrapolation capacity. Secondly, we propose a novel evaluation protocol to separately evaluate the interpolation and extrapolation performance on existing benchmark datasets. It resamples the training and test data based on query similarity and utilizes the resampled dataset for training and evaluation. Finally, we leverage the proposed evaluation protocol to comprehensively revisit a number of widely-adopted neural retrieval models. Results show models perform differently when moving from interpolation to extrapolation. For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation. Therefore, it is necessary to separately evaluate both interpolation and extrapolation performance and the proposed resampling method serves as a simple yet effective evaluation tool for future IR studies.Comment: CIKM 2022 Full Pape

Increased KIF15 Expression Predicts a Poor Prognosis in Patients with Lung Adenocarcinoma

Background/Aims: Lung cancer is the leading cause of cancer-related deaths worldwide. The outcome of patients with non-small cell lung cancer remains poor; the 5-year survival rate for stage IV non-small cell lung cancer is only 1.0%. KIF15 is a tetrameric kinesin spindle motor that has been investigated for its regulation of mitosis. While the roles of kinesin motor proteins in the regulation of mitosis and their potentials as therapeutic targets in pancreatic cancer have been described previously, the role of KIF15 in lung cancer development remains unknown. Methods: Paired lung carcinoma specimens and matched adjacent normal tissues were used for protein analysis. Clinical data were obtained from medical records. We first examined KIF15 messenger RNA expression in The Cancer Genome Atlas database, and then determined KIF15 protein levels using immunohistochemistry and western blotting. Differences between the groups were analyzed using repeated measures analysis of variance. Overall survival was analyzed using the Kaplan–Meier method. Cell-cycle and proliferation assays were conducted using A549, NCI-H1299, and NCI-H226 cells. Results: KIF15 was significantly upregulated at both the messenger RNA and protein levels in human lung tumor tissues. In patients with lung adenocarcinoma, KIF15 expression was positively associated with disease stages; high KIF15 expression predicted a poor prognosis. KIF15 knockdown using short hairpin RNA in two human lung adenocarcinoma cell lines induced G1/S phase cell cycle arrest and inhibited cell growth, but there was no effect in human lung squamous cell carcinoma. Conclusion: Our findings show that KIF15 is involved in lung cancer carcinogenesis. KIF15 could therefore serve as a specific prognostic marker for patients with lung adenocarcinoma

Cellular immunotherapy as maintenance therapy prolongs the survival of the patients with small cell lung cancer in extensive stage

AbstractBackgroundSmall cell lung cancer (SCLC) is the most devastating type of human lung cancer. Patients usually present with disseminated disease to many organs (extensive stage). This study was to investigate the efficacy and safety of cellular immunotherapy (CIT) with autologous natural killer (NK), γδT, and cytokine-induced killer (CIK) cells as maintenance therapy for extensive-stage SCLC (ES-SCLC) patients.MethodsA pilot prospective cohort study was conducted with ES-SCLC patients who had responded to initial chemotherapy. Patients received either CIT as maintenance therapy (CIT group), or no treatment (control group). Progression-free survival (PFS), overall survival (OS), and adverse effects were compared.ResultsForty-nine patients were recruited in this study, with 19 patients in the CIT group and 30 patients in the control group. The patient characteristics of the 2 groups were comparable except for age, as patients in the CIT group were older than those in the control group (P < 0.05). PFS in the CIT group was superior to the control group (5 vs. 3.1 months, P = 0.020; HR, 0.489, 95% CI, 0.264–0.909, P = 0.024). OS of the CIT group was also longer than that of the control group (13.3 vs. 8.2 months, P = 0.044; HR, 0.528, 95% CI, 0.280–0.996, P = 0.048, respectively). No significant adverse reactions occurred in patients undergoing CIT.ConclusionsCIT maintenance therapy in ES-SCLC prolonged survival with only minimal side effects. Integrating CIT into the current treatment may be a novel strategy for ES-SCLC patients, although further multi-center randomized trials are needed