Novel Insights into the Role of the Cytoskeleton in Cancer

The cytoskeleton is a complex network of highly ordered intracellular filaments that plays a central role in controlling cell shape, division, functions, and interactions in human organs and tissues, but dysregulation of this network can contribute to numerous human diseases, including cancer. To clarify the various functions of the cytoskeleton and its role in cancer progression, in this chapter, we will discuss the microfilament (actin cytoskeleton), microtubule (β‐tubulin), and intermediate filament (keratins, cytokeratins, vimentin, and lamins) cytoskeletal structures; analyze the physiological functions of the cytoskeleton and its regulation of cell differentiation; and investigate the roles of the cytoskeleton in cancer progression, the epithelial‐mesenchymal transition process (EMT), and the mechanisms of multidrug resistance (MDR) in relation to the cytoskeleton. Importantly, the cytoskeleton, as a key regulator of the transcription and expression of many genes, is known to be involved in various physiological and pathological processes, which makes the cytoskeleton a novel and highly effective target for assessing the response to antitumor therapy in cancer

CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation

Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. Unsupervised metrics can only provide a task-agnostic evaluation result which correlates weakly with human judgments, whereas supervised ones may overfit task-specific data with poor generalization ability to other datasets. In this paper, we propose an unsupervised reference-free metric called CTRLEval, which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks. On top of these tasks, the metric assembles the generation probabilities from a pre-trained language model without any model training. Experimental results show that our metric has higher correlations with human judgments than other baselines, while obtaining better generalization of evaluating generated texts from different models and with different qualities.Comment: Accepted by ACL 2022 (Main Conference

DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering

Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. Specifically, most of the well-performed metrics are required to train on evaluation datasets of specific NLG tasks and evaluation dimensions, which may cause over-fitting to task-specific datasets. Furthermore, existing metrics only provide an evaluation score for each dimension without revealing the evidence to interpret how this score is obtained. To deal with these challenges, we propose a simple yet effective metric called DecompEval. This metric formulates NLG evaluation as an instruction-style question answering task and utilizes instruction-tuned pre-trained language models (PLMs) without training on evaluation datasets, aiming to enhance the generalization ability. To make the evaluation process more interpretable, we decompose our devised instruction-style question about the quality of generated texts into the subquestions that measure the quality of each sentence. The subquestions with their answers generated by PLMs are then recomposed as evidence to obtain the evaluation result. Experimental results show that DecompEval achieves state-of-the-art performance in untrained metrics for evaluating text summarization and dialogue generation, which also exhibits strong dimension-level / task-level generalization ability and interpretability.Comment: Accepted by ACL 2023 (Main Conference

Memory Load Influences Taste Sensitivities

Previous literature reports have demonstrated that taste perception would be influenced by different internal brain status or external environment stimulation. Although there are different hypotheses about the cross-modal interactive process, it still remains unclear as of how the brain modulates and processes taste perception, particularly with different memory load. Here in this study we address this question. To do so we assign the participants different memory loads in the form of varying lengths of alphanumerical items, before tasting different concentrations of sweet or bitter tastants. After tasting they were asked to recall the alphanumerical items they were assigned. Our results show that the memory load reduces sweet and bitter taste sensitivities, from sub-threshold level to high concentration. Higher the memory load, less is the taste sensitivity. The study has extended our previous results and supports our previous hypothesis that the cognitive status, such as the general stress of memory load, influences sensory perception

A unified formula for calculating bending capacity of solid and hollow concrete-filled steel tubes under normal and elevated temperature

Bending is one of the most common forms of deformation that may cause failure of a structural member, such as a column, especially when the member is exposed to fire. Fire resistance design is therefore an important factor that must be considered in the design process of modern building structures. Based on the authors' previous work on the unified formulation of axially loaded CFST hollow and solid columns with circular and polygonal sections, a unified formula for calculating the ultimate bending moment of solid and hollow CFST columns at room temperature is proposed first in this paper. The formula is then extended to include elevated temperature using the average temperature method. Finally, a unified formula for both room and elevated temperature are presented. Validations are carried out through comparisons with the results from experimental tests and finite element simulations