Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and fluency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Specifically, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentence-tree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output. Experimental results demonstrate that the proposed X2Tree framework outperforms baseline methods over 11.15% increase of acceptance ratio

Research progress on the relationship between lung microbiome and lung cancer

Lung cancer is the leading cause of cancer-related deaths worldwide. Lung microbiome is defined as the microbial organisms located in the lung. With rapid development of next-generation sequencing， microbiome has been found to affect the host’s nutrition， immunity， metabolism and the occurrence or development of diseases including cancer. Dysbiosis of lung microbiome causes persistent airway inflammation and immune disorders， which affects the occurrence and development of lung cancer. In-depth exploration of lung microecological characteristics of lung cancer patients can be applied to early diagnosis， treatment and prognosis of lung cancer， which is beneficial to improve clinical efficacy of lung cancer. In this article， research progress on lung microbiome in lung cancer was reviewed

Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment

PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point. Recent studies solved this PointGoal navigation task with near-perfect success rate in photo-realistically simulated environments, under the assumptions with noiseless actuation and most importantly, perfect localization with GPS and compass sensors. However, accurate GPS signalis difficult to be obtained in real indoor environment. To improve the PointGoal navigation accuracy without GPS signal, we use visual odometry (VO) and propose a novel action integration module (AIM) trained in unsupervised manner. Sepecifically, unsupervised VO computes the relative pose of the agent from the re-projection error of two adjacent frames, and then replaces the accurate GPS signal with the path integration. The pseudo position estimated by VO is used to train action integration which assists agent to update their internal perception of location and helps improve the success rate of navigation. The training and inference process only use RGB, depth, collision as well as self-action information. The experiments show that the proposed system achieves satisfactory results and outperforms the partially supervised learning algorithms on the popular Gibson dataset.Comment: 12 pages, 6 figure

Trace-Based Dynamic Gas Estimation of Loops in Smart Contracts

Smart contracts on Ethereum can be used to encode business logic and have been applied to many different areas, such as token exchanges and games. Unlike general programs, the computations of contracts on Ethereum are restricted by the gas limit. If a transaction runs out of the gas limit before an execution finishes, the Ethereum virtual machine throws an out-of-gas exception, and the entire transaction fails, which reverts to the state before the transaction started, although the transaction fee is still deducted. It is therefore, essential to conduct a gas estimation before sending a transaction. Existing studies have mostly failed in estimating the gas for a loop function because the number of iterations of the loops cannot be statically determined. However, we found that a quarter of all contracts have loop functions, and the gas cost for the loops is higher than for the other functions. Therefore, it is necessary to apply a gas estimation for the loop functions. In this study, we propose a gas estimation approach based on the transaction trace to dynamically estimate the gas for the loop functions. Our belief is that we can learn the relationship between the historical transaction traces and their gas costs to estimate the gas for new transactions. We considered three different abstractions of the original transaction trace and fed them to different machine learning models. The results show that our approach is effective in gas estimation and that a random forest can achieve the most accurate estimation

Mechanism of inflammatory cancer-associated fibroblast-mediated drug resistance in colorectal cancer cells

Background and purpose: Colorectal cancer (CRC) is one of the common malignancies, but the mechanism by which it develops resistance to drug remains unclear. The tumor microenvironment (TME), especially cancer-associated fibroblast (CAF), plays an important role in the occurrence, development and drug resistance of tumors. This study aimed to investigate the effect of inflammatory cancer-associated fibroblasts (iCAF) on drug resistance in CRC cells and its possible mechanism. Methods: The primary CAFs were collected from CRC patients underwent surgery in Putuo Hospital, Shanghai University of Traditional Chinese Medicine from Aug. 2022 to Sep. 2022, and the primary cells were sorted according to the surface marker of CAF[approved by the Ethics Committee of Putuo Hospital Affiliated to Shanghai University of Traditional Chinese Medicine: PTEC-A-2023-5(S)-1], platelet derived growth factor receptor alpha (PDGFRA), to screen iCAF. Human intestinal fibroblast (HIF) and iCAF cells were cultured using serum-free medium to obtain conditioned medium. According to the treatment method, colon cancer cells were divided into control group (no treatment), experimental group 1(treated with HIF-CM) and experimental group 2 (treated with iCAF-CM). We observed the changes in the survival rate and apoptotic rate of CRC cells, the changes in protein and mRNA levels and the effect on the Wnt/β-catenin signaling pathway after stimulation with HIF-CM or iCAF-CM. Results: After iCAFs stimulation, the half inhibition concentration (IC50) of CRC cells was higher compared with the control group and HIF-CM group (P<0.05). Compared with the control group and HIF-CM group, the apoptotic rate of tumor cells in iCAF-CM group decreased significantly, the expression of caspase-3 was decreased, and the expressions of Bcl-2, Bcl-xL and survivin were increased (P<0.05). The Wnt/β-catenin signaling pathway was activated in the iCAF-CM group. Conclusion: iCAFs can mediate drug resistance in CRC cells, and the mechanism is related to the activation of Wnt/β-catenin signaling pathway

Removal Impurity and Whitening of Phosphogypsum via Calcination and Acid Leaching

This is an article in the field of metallurgical engineering. Large production and storage capacity of phosphogypsum has become an important factor restricting the green and healthy development of phosphorus chemical industry. Removing impurity and whitening of phosphogypsum is a necessary way for its high consumption. Analysis results showed that there was still a small amount of black organic matter in the phosphogypsum after removing impurity and desilication by flotation, which affected its whiteness. In the paper, calcination and acid leaching were adopted to treat the phosphogysum. The whiteness of whitened phosphogysum significantly increased from 51.5% to 92.7% at the conditions of calcination temperature 600 ℃, calcination time 70 min, acid leaching time 2 h, sulfuric acid concentration 1.5 mol/L, acid leaching liquid to solid ratio 5∶1 and acid leaching temperature 90 ℃. The whitened phosphogypsum was mainly CaSO4, and contained a small amount of CaSO4·2H2O and CaSO4·0.5H2O. The particles of the whitened phosphogypsum were irregular in shape of 15～20 μm, and the surface was smooth. The study provided a guidance for the comprehensive utilization of phosphogypsum with high consumption

Dimensionality reduction based on determinantal point process and singular spectrum analysis for hyperspectral images

Dimensionality reduction is of high importance in hyperspectral data processing, which can effectively reduce the data redundancy and computation time for improved classification accuracy. Band selection and feature extraction methods are two widely used dimensionality reduction techniques. By integrating the advantages of the band selection and feature extraction, the authors propose a new method for reducing the dimension of hyperspectral image data. First, a new and fast band selection algorithm is proposed for hyperspectral images based on an improved determinantal point process (DPP). To reduce the amount of calculation, the dual-DPP is used for fast sampling representative pixels, followed by k-nearest neighbour-based local processing to explore more spatial information. These representative pixel points are used to construct multiple adjacency matrices to describe the correlation between bands based on mutual information. To further improve the classification accuracy, two-dimensional singular spectrum analysis is used for feature extraction from the selected bands. Experiments show that the proposed method can select a low-redundancy and representative band subset, where both data dimension and computation time can be reduced. Furthermore, it also shows that the proposed dimensionality reduction algorithm outperforms a number of state-of-the-art methods in terms of classification accuracy

Influenza vaccination rates among healthcare workers: a systematic review and meta-analysis investigating influencing factors

IntroductionHealthcare workers risk of exposure to the influenza virus in their work, is a high-risk group for flu infections. Thus WHO recommends prioritizing flu vaccination for them–an approach adopted by >40 countries and/or regions worldwide.MethodsCross-sectional studies on influenza vaccination rates among healthcare workers were collected from PubMed, EMBASE, CNKI, and CBM databases from inception to February 26, 2023. Influenza vaccination rates and relevant data for multiple logistic regression analysis, such as odds ratios (OR) and 95% confidence intervals (CI), were extracted.ResultsA total of 92 studies comprising 125 vaccination data points from 26 countries were included in the analysis. The meta-analysis revealed that the overall vaccination rate among healthcare workers was 41.7%. Further analysis indicated that the vaccination rate was 46.9% or 35.6% in low income or high income countries. Vaccination rates in the Americas, the Middle East, Oceania, Europe, Asia, and Africa were 67.1, 51.3, 48.7, 42.5, 28.5, and 6.5%, respectively. Influencing factors were age, length of service, education, department, occupation, awareness of the risk of influenza, and/or vaccines.ConclusionThe global influenza vaccination rate among healthcare workers is low, and comprehensive measures are needed to promote influenza vaccination among this population.Systematic review registrationwww.inplysy.com, identifier: 202350051