Weighted Cache Location Problem with Identical Servers

This paper extends the well-known p-CLP with one server to p-CLP with m≥2 identical servers, denoted by (p,m)-CLP. We propose the closest server orienting protocol (CSOP), under which every client connects to the closest server to itself via a shortest route on given network. We abbreviate (p,m)-CLP under CSOP to (p,m)-CSOP CLP and investigate that (p,m)-CSOP CLP on a general network is equivalent to that on a forest and further to multiple CLPs on trees. The case of m=2 is the focus of this paper. We first devise an improved O(ph2+n)-time parallel exact algorithm for p-CLP on a tree and then present a parallel exact algorithm with at most O((4/9)p2n2) time in the worst case for (p,2)-CSOP CLP on a general network. Furthermore, we extend the idea of parallel algorithm to the cases of m>2 to obtain a worst-case O((4/9)(n-m)2((m+p)p/p-1!))-time exact algorithm. At the end of the paper, we first give an example to illustrate our algorithms and then make a series of numerical experiments to compare the running times of our algorithms

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.Comment: CVPR 2023 accep

Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization

With the prosperity of e-commerce industry, various modalities, e.g., vision and language, are utilized to describe product items. It is an enormous challenge to understand such diversified data, especially via extracting the attribute-value pairs in text sequences with the aid of helpful image regions. Although a series of previous works have been dedicated to this task, there remain seldomly investigated obstacles that hinder further improvements: 1) Parameters from up-stream single-modal pretraining are inadequately applied, without proper jointly fine-tuning in a down-stream multi-modal task. 2) To select descriptive parts of images, a simple late fusion is widely applied, regardless of priori knowledge that language-related information should be encoded into a common linguistic embedding space by stronger encoders. 3) Due to diversity across products, their attribute sets tend to vary greatly, but current approaches predict with an unnecessary maximal range and lead to more potential false positives. To address these issues, we propose in this paper a novel approach to boost multi-modal e-commerce attribute value extraction via unified learning scheme and dynamic range minimization: 1) Firstly, a unified scheme is designed to jointly train a multi-modal task with pretrained single-modal parameters. 2) Secondly, a text-guided information range minimization method is proposed to adaptively encode descriptive parts of each modality into an identical space with a powerful pretrained linguistic model. 3) Moreover, a prototype-guided attribute range minimization method is proposed to first determine the proper attribute set of the current product, and then select prototypes to guide the prediction of the chosen attributes. Experiments on the popular multi-modal e-commerce benchmarks show that our approach achieves superior performance over the other state-of-the-art techniques

Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in the limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval

Effect of staurosporine on the mobility and invasiveness of lung adenocarcinoma A549 cells: an in vitro study

<p>Abstract</p> <p>Background</p> <p>Lung cancer is one of the most malignant tumors, representing a significant threat to human health. Lung cancer patients often exhibit tumor cell invasion and metastasis before diagnosis which often render current treatments ineffective. Here, we investigated the effect of staurosporine, a potent protein kinase C (PKC) inhibitor on the mobility and invasiveness of human lung adenocarcinoma A549 cells.</p> <p>Methods</p> <p>All experiments were conducted using human lung adenocarcinoma A549 cells that were either untreated or treated with 1 nmol/L, 10 nmol/L, or 100 nmol/L staurosporine. Electron microscopy analyses were performed to study ultrastructural differences between untreated A549 cells and A549 cells treated with staurosporine. The effect of staurosporine on the mobility and invasiveness of A549 was tested using Transwell chambers. Western blot analyses were performed to study the effect of staurosporine on the levels of PKC-α, integrin β1, E-cadherin, and LnR. Changes in MMP-9 and uPA levels were identified by fluorescence microscopy.</p> <p>Results</p> <p>We demonstrated that treatment of A549 cells with staurosporine caused alterations in the cell shape and morphology. Untreated cells were primarily short spindle- and triangle-shaped in contrast to staurosporine treated cells which were retracted and round-shaped. The latter showed signs of apoptosis, including vacuole fragmentation, chromatin degeneration, and a decrease in the number of microvilli at the surface of the cells. The A549 cell adhesion, mobility, and invasiveness significantly decreased with higher staurosporine concentrations. E-cadherin, integrin β1, and LnR levels changed by a factor of 1.5, 0.74, and 0.73, respectively compared to untreated cells. In addition, the levels of MMP-9 and uPA decreased in cells treated with staurosporine.</p> <p>Conclusion</p> <p>In summary, this study demonstrates that staurosporine inhibits cell adhesion, mobility, and invasion of A549 cells. The staurosporine-mediated inhibition of PKC-α, induction of E-Cad expression, and decreased integrin β1, LnR, MMP-9, and uPA levels could all possibly contribute to this biological process. These results represent a significant step forward in the ongoing effort to understand the development of lung carcinoma and to design novel strategies to inhibit metastasis of the tumor by targeting the cell-adhesion, mobility and invasion of tumor cells.</p

BRD4 Inhibitor Inhibits Colorectal Cancer Growth and Metastasis

Post-translational modifications have been identified to be of great importance in cancers and lysine acetylation, which can attract the multifunctional transcription factor BRD4, has been identified as a potential therapeutic target. In this paper, we identify that BRD4 has an important role in colorectal cancer; and that its inhibition substantially wipes out tumor cells. Treatment with inhibitor MS417 potently affects cancer cells, although such effects were not always outright necrosis or apoptosis. We report that BRD4 inhibition also limits distal metastasis by regulating several key proteins in the progression of epithelial-to-mesenchymal transition (EMT). This effect of BRD4 inhibitor is demonstrated via liver metastasis in animal model as well as migration and invasion experiments in vitro. Together, our results demonstrate a new application of BRD4 inhibitor that may be of clinical use by virtue of its ability to limit metastasis while also being tumorcidal

T cell–derived inducible nitric oxide synthase switches off TH17 cell differentiation

RORγt is necessary for the generation of TH17 cells but the molecular mechanisms for the regulation of TH17 cells are still not fully understood. We show that activation of CD4+ T cells results in the expression of inducible nitric oxide synthase (iNOS). iNOS-deficient mice displayed enhanced TH17 cell differentiation but without major effects on either TH1 or TH2 cell lineages, whereas endothelial NOS (eNOS) or neuronal NOS (nNOS) mutant mice showed comparable TH17 cell differentiation compared with wild-type control mice. The addition of N6-(1-iminoethyl)-l-lysine dihydrochloride (L-NIL), the iNOS inhibitor, significantly enhanced TH17 cell differentiation, and S-nitroso-N-acetylpenicillamine (SNAP), the NO donor, dose-dependently reduced the percentage of IL-17–producing CD4+ T cells. NO mediates nitration of tyrosine residues in RORγt, leading to the suppression of RORγt-induced IL-17 promoter activation, indicating that NO regulates IL-17 expression at the transcriptional level. Finally, studies of an experimental model of colitis showed that iNOS deficiency results in more severe inflammation with an enhanced TH17 phenotype. These results suggest that NO derived from iNOS in activated T cells plays a negative role in the regulation of TH17 cell differentiation and highlight the importance of intrinsic programs for the control of TH17 immune responses

5-Fluorouracil targets thymidylate synthase in the selective suppression of TH17 cell differentiation

While it is well established that treatment of cancer patients with 5-Fluorouracil (5-FU) can result in immune suppression, the exact function of 5-FU in the modulation of immune cells has not been fully established. We found that low dose 5-FU selectively suppresses TH17 and TH1 cell differentiation without apparent effect on Treg, TH2, and significantly suppresses thymidylate synthase (TS) expression in TH17 and TH1 cells but has a lesser effect in tumor cells and macrophages. Interestingly, the basal expression of TS varies significantly between T helper phenotypes and knockdown of TS significantly impairs TH17 and TH1 cell differentiation without affecting the differentiation of either Treg or TH2 cells. Finally, low dose 5-FU is effective in ameliorating colitis development by suppressing TH17 and TH1 cell development in a T cell transfer colitis model. Taken together, the results highlight the importance of the anti-inflammatory functions of low dose 5-FU by selectively suppressing TH17 and TH1 immune responses