PERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neural Network

This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between individuals and certain consistency in each individual is the cornerstone of the framework. We focus on relations between documents for user-sensitive predictions. Individual’s lexical choices act as indicators for individuality, thus we use a concept-based system which utilizes neural networks to embed concepts and associated topics in text. Furthermore, a recurrent neural network is used to memorize the history of user’s opinions, to discover user-topic dependence, and to detect implicit relations between users. PERSEUS also offers a solution for data sparsity. At the first stage, we show the benefit of inquiring a user-specified system. Improvements in performance experimented on a combined Twitter dataset are shown over generalized models. PERSEUS can be used in addition to such generalized systems to enhance the understanding of user’s opinions

Metabolic Syndrome During Perinatal Period in Sows and the Link With Gut Microbiota and Metabolites

In humans, the metabolic and immune changes occurring during perinatal period also describe metabolic syndrome. Gut microbiota can cause symptoms of metabolic syndrome in pregnant women. Increased gut permeability is also involved in metabolic disorders in non-pregnant hosts. However, longitudinal studies investigating the changes in metabolic characteristics, gut microbiota, and gut permeability of sows throughout pregnancy and lactation are lacking. The correlation between gut microbiota and metabolic status of sows is also poorly known. The present study was conducted to investigate the temporal variations in sow metabolic characteristics, gut microbiota, gut permeability, and gut inflammation at days 30 (G30) and 109 (G109) of gestation and days 3 (L3) and 14 (L14) of lactation. Results showed that insulin sensitivity was decreased in L3. Circulating concentrations of pro-inflammatory cytokine IL-6 increased in G109 and L3. 16S rRNA gene sequencing of the V3-V4 region showed that gut microbiota changed dramatically across different reproductive stages. The bacterial abundance and alpha diversity in L3 were the lowest. The phyla Proteobacteria and Fusobacteria exhibited the highest relative abundance in L3. Among the genera, Bacteroides, Escherichia_Shigella, and Fusobacterium were highest, but Oscillospira the lowest, in relative abundance in L3. The fecal levels of acetate and total short-chain fatty acids were increased in G109, but fecal butyrate concentrations were markedly decreased in L3. The plasma zonulin concentrations, a biomarker for gut permeability, were increased in G109 and L3. The plasma endotoxin concentrations were increased in L3. Furthermore, levels of fecal lipocalin-2 and pro-inflammatory cytokines IL-6 and TNF-α were increased in G109 and L3. In contrast, fecal levels of anti-inflammatory cytokine IL-10 were significantly decreased in G109 and L3. Additionally, the increased relative abundances of Fusobacterium in L3 were positively correlated with plasma zonulin and fecal endotoxin but negatively correlated with fecal IL-10. These findings indicate that the mother sow exhibits a metabolic syndrome and dramatical changes in gut microbiota during perinatal period, especially in early lactation. Besides, increased gut permeability and plasma endotoxin concentrations caused by negative microbial changes would possibly be the potential mechanisms under which sow’s metabolic disorders and inflammatory status were exacerbated during early lactation

Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning

In the context of online education, designing an automatic solver for geometric problems has been considered a crucial step towards general math Artificial Intelligence (AI), empowered by natural language understanding and traditional logical inference. In most instances, problems are addressed by adding auxiliary components such as lines or points. However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when pivotal decisions have to be made. The state-of-the-art performance has been achieved by exhausting all possible strategies from the category library to identify the one with the maximum likelihood. However, an extensive strategy search have to be applied to trade accuracy for ef-ficiency. To add auxiliary components automatically and efficiently, we present deep reinforcement learning framework based on the language model, such as BERT. We firstly apply the graph attention mechanism to reduce the strategy searching space, called AttnStrategy, which only focus on the conclusion-related components. Meanwhile, a novel algorithm, named Automatically Adding Auxiliary Components using Reinforcement Learning framework (A3C-RL), is proposed by forcing an agent to select top strategies, which incorporates the AttnStrategy and BERT as the memory components. Results from extensive experiments show that the proposed A3C-RL algorithm can substantially enhance the average precision by 32.7% compared to the traditional MCTS. In addition, the A3C-RL algorithm outperforms humans on the geometric questions from the annual University Entrance Mathematical Examination of China

SOX on Tumors, a Comfort or a Constraint?

The sex-determining region Y (SRY)-related high-mobility group (HMG) box (SOX) family, composed of 20 transcription factors, is a conserved family with a highly homologous HMG domain. Due to their crucial role in determining cell fate, the dysregulation of SOX family members is closely associated with tumorigenesis, including tumor invasion, metastasis, proliferation, apoptosis, epithelial-mesenchymal transition, stemness and drug resistance. Despite considerable research to investigate the mechanisms and functions of the SOX family, confusion remains regarding aspects such as the role of the SOX family in tumor immune microenvironment (TIME) and contradictory impacts the SOX family exerts on tumors. This review summarizes the physiological function of the SOX family and their multiple roles in tumors, with a focus on the relationship between the SOX family and TIME, aiming to propose their potential role in cancer and promising methods for treatment

Transcription Factor BACH1 in Cancer: Roles, Mechanisms, and Prospects for Targeted Therapy

Transcription factor BTB domain and CNC homology 1 (BACH1) belongs to the Cap \u27n\u27 Collar and basic region Leucine Zipper (CNC-bZIP) family. BACH1 is widely expressed in mammalian tissues, where it regulates epigenetic modifications, heme homeostasis, and oxidative stress. Additionally, it is involved in immune system development. More importantly, BACH1 is highly expressed in and plays a key role in numerous malignant tumors, affecting cellular metabolism, tumor invasion and metastasis, proliferation, different cell death pathways, drug resistance, and the tumor microenvironment. However, few articles systematically summarized the roles of BACH1 in cancer. This review aims to highlight the research status of BACH1 in malignant tumor behaviors, and summarize its role in immune regulation in cancer. Moreover, this review focuses on the potential of BACH1 as a novel therapeutic target and prognostic biomarker. Notably, the mechanisms underlying the roles of BACH1 in ferroptosis, oxidative stress and tumor microenvironment remain to be explored. BACH1 has a dual impact on cancer, which affects the accuracy and efficiency of targeted drug delivery. Finally, the promising directions of future BACH1 research are prospected. A systematical and clear understanding of BACH1 would undoubtedly take us one step closer to facilitating its translation from basic research into the clinic

Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data

Purpose: Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs. Methods: We present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients were divided into three subtypes, namely, estrogen receptor (ER)-positive, ER-negative, and triple-negative groups. The associations between TIL scores and gene expression and somatic mutation were examined separately in three breast cancer subtypes. Both univariate and multivariate survival analyses were performed on 43 TIL image features to examine the prognostic value of TIL spatial patterns in different breast cancer subtypes. Results: The TIL score was in strong association with immune response pathway and genes (eg, programmed death-1 and CLTA4). Different breast cancer subtypes showed TIL score in association with mutations from different genes suggesting that different genetic alterations may lead to similar phenotypes. Spatial TIL features that represent density and distribution of TIL clusters were important indicators of the patient outcomes. Conclusion: Our pipeline can facilitate computational pathology-based discovery in cancer immunology and research on immunotherapy. Our analysis results are available for the research community to generate new hypotheses and insights on breast cancer immunology and development

Using hollow dodecahedral NiCo-LDH with multi-active sites to modify BiVO4 photoanode facilitates the photoelectrochemical water splitting performance

Photoelectrochemical (PEC) water splitting presents a promising approach for harnessing solar energy and converting it into hydrogen energy. However, the limited water oxidation activity of semiconductor photoanodes has severely hampered the overall conversion efficiency. In this study, a hollow dodecahedral structure of NiCo-LDH (HD-NiCo-LDH) was designed using the metal-organic framework ZIF-67 as a precursor. HD-NiCo-LDH was employed to modify the BiVO4 photoanode, serving as an oxygen evolution cocatalyst. HD-NiCo-LDH can enhance light absorption, accelerate photogenic hole extraction, promote photogenic charge separation and improve the kinetics of water oxidation reaction. Significantly, the unique hollow dodecahedral structure of HD-NiCo-LDH possesses a larger specific surface areas, which provides additional active sites for the water oxidation reaction and facilitates the adsorption of water molecules. The photocurrent density of the optimized HD-NiCo-LDH/BiVO4 photoanode reaches 4.54 mA/cm2 at 1.23 V vs. RHE, which is 3.3 times greater than the bare BiVO4 photoanode. This presented work introduces an innovative design concept for photoanodes supported by oxygen evolution cocatalysts with multi-active sites

Glycyrrhiza uralensis polysaccharides ameliorates cecal ligation and puncture-induced sepsis by inhibiting the cGAS-STING signaling pathway

Ethnopharmacological relevance:G. uralensis Fisch. (Glycyrrhiza uralensis) is an ancient and widely used traditional Chinese medicine with good efficacy in clearing heat and detoxifying action. Studies suggest that Glycyrrhiza Uralensis Polysaccharides (GUP), one of the major components of G. uralensis, has anti-inflammatory, anti-cancer and hepatoprotective effects., but its exact molecular mechanism has not been explored in depth.Aim of the study: Objectives of our research are about exploring the anti-inflammatory role of GUP and the mechanisms of its action.Materials and methods: ELISA kits, Western blotting, immunofluorescence, quantitative real-time PCR, immunoprecipitation and DMXAA-mediated STING activation mice models were performed to investigate the role of GUP on the cGAS-STING pathway. To determine the anti-inflammatory effects of GUP, cecal ligation and puncture (CLP) sepsis models were employed.Results: GUP could effectively inhibit the activation of the cGAS-STING signaling pathway accompany by a decrease the expression of type I interferon-related genes and inflammatory factors in BMDMs, THP-1, and human PBMCs. Mechanistically, GUP does not affect the oligomerization of STING, but affects the interaction of STING with TBK1 and TBK1 with IRF3. Significantly, GUP had great therapeutic effects on DMXAA-induced agonist experiments in vivo as well as CLP sepsis in mice.Conclusion: Our studies suggest that GUP is an effective inhibitor of the cGAS-STING pathway, which may be a potential medicine for the treatment of inflammatory diseases mediated by the cGAS-STING pathway

Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD

Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer

Background: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. Methods: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. Results: Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features. Conclusion: This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets