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The online social media ecosystem is becoming more and more confused because of more and more fake information and the social media of malicious users' fake content; at the same time, unspeakable pain has been brought to mankind. Social robot detection uses supervised classification based on artificial feature extraction. However, user privacy is also involved in using these methods, and the hidden feature information is also ignored, such as semi-supervised algorithms with low utilization rates and graph features. In this work, we symmetrically combine BERT and GCN (Graph Convolutional Network, GCN) and propose a novel model that combines large scale pretraining and transductive learning for social robot detection, BGSRD. BGSRD constructs a heterogeneous graph over the dataset and represents Twitter as nodes using BERT representations. Corpus learning via text graph convolution network is a single text graph, which is mainly built for corpus-based on word co-occurrence and document word relationship. BERT and GCN modules can be jointly trained in BGSRD to achieve the best of merit, training data and unlabeled test data can spread label influence through graph convolution and can be carried out in the large-scale pre-training of massive raw data and the transduction learning of joint learning representation. The experiment shows that a better performance can also be achieved by BGSRD on a wide range of social robot detection datasets. [ABSTRACT FROM AUTHOR]

In the present study, a low-cost and facile method is adopted to fabricate graphitic carbon nitride doped polystyrene thin film by casting method. GCN was prepared by the thermal condensation method. The particle size analyzer for GCN depicted that the particle is in nanoscale regime. Different concentration of GCN (1, 2, 3, 4, 5%) was embedded in PS thin film. Thermal studies revealed that composite thin film is stable up to 400 C. The optical properties were investigated in detail. 5% doped GCN in PS exhibited higher absorbance covering UV and visible light and further it exhibited a low bandgap. Higher PL quenching with non-radiative emission is noticed for the higher concentration of GCN in PS. A lower PL intensity is attributed to low recombination rate for electron-hole pairs. Therefore, 5GPS can be employed as a potential candidate for optoelectronic application very specifically for solar cell application. Due to the low water absorption property, this nanocomposite thin film can also be used as a superhydrophobic material. [ABSTRACT FROM AUTHOR]

Recombinant human antibody production represents a major growing class of biopharmaceuticals based on the technological progress within the last decades especially in CHO cells. The HIV neutralizing human monoclonal antibody 2F5 was developed as hybridoma from human lymphocyte preparations. In order to estimate the potential of recombinant 2F5-expressing CHO cells, we generated different recombinant CHO cell lines by varying regulatory sequences, the codon usage, the signal peptides, and the transfection technique. These 2F5-expressing cell lines were developed by selection of the best producer, clone homogeneity, and clone stability. The gene copy number of the clones differed significantly due to methotrexate amplification. In one cell line, we identified only one copy of heavy chain and two copies of light chain. Neither the gene copy number nor the promoter was found to influence the amount of transcript exclusively emphasizing the positioning effect of the transgene. Messenger RNA levels were highest in 2F5/CO and may have resulted from a combination of the promoter and codon-optimized sequences, but unexpectedly, the amount of secreted product was not elevated in this configuration. In our example, translational and post-translational limitations are responsible for decreased antibody secretion. [ABSTRACT FROM AUTHOR]

This paper presents a novel use case of Graph Convolutional Network (GCN) learning representations for predictive data mining, specifically from user/task data in the domain of high-performance computing (HPC). It outlines an approach based on a coalesced data set: logs from the Slurm workload manager, joined with user experience survey data from computational cluster users. We introduce a new method of constructing a heterogeneous unweighted HPC graph consisting of multiple typed nodes after revealing the manifold relations between the nodes. The GCN structure used here supports two tasks: i) determining whether a job will complete or fail and ii) predicting memory and CPU requirements by training the GCN semi-supervised classification model and regression models on the generated graph. The graph is partitioned into partitions using graph clustering. We conducted classification and regression experiments using the proposed framework on our HPC log dataset and evaluated predictions by our trained models against baselines using test_score, F1-score, precision, recall for classification, and R1 score for regression, showing that our framework achieves significant improvements.

The threonine deaminase gene ( ILV1) of Saccharomyces cerevisiae has been designated 'multifunctional' since Bollon (1974) indicated its involvement both in the catalysis of the first step in isoleucine biosynthesis and in the regulation of the isoleucine-valine pathway. Its role in regulation is characterized by a decrease in the activity of the five isoleucine-valine enzymes when cells are grown in the presence of the three branched-chain amino acids, isoleucine, valine and leucine (multivalent repression). We have demonstrated that the regulation of AHA reductoisomerase (encoded by ILV5) and branched-chain amino acid transaminase is unaffected by the deletion of ILV1, subsequently revealing that the two enzymes can be regulated in the absence of threonine deaminase. Both threonine deaminase activity and ILV1 mRNA levels increase in mutants ( gcd2 and gcd3) having constitutively derepressed levels of enzymes under the general control of amino acid biosynthesis, as well as in response to starvation for tryptophan and branched-chain amino acid imbalance. Thus, the ILV1 gene is under general amino acid control, as is the case for both the ILV5 and the transaminase gene. Multivalent repression of reductoisomerase and transaminase can be observed in mutants defective in general control ( gcn and gcd), whereas this is not the case for threonine deaminase. Our analysis suggests that repression effected by general control is not complete in minimal medium. Amino acid dependent regulation of threonine deaminase is only through general control, while the branched-chain amino acid repression of AHA reducto isomerase and the transaminase is caused both by general control and an amino acid-specific regulation. [ABSTRACT FROM AUTHOR]

• An unsupervised deep hashing framework that consists of node representation learning stage and hash function learning stage is proposed. • In the first stage, we utilize graph convolution network to integrate the relationships between samples into node representations. • In the second stage, we use above node representations to fast achieve an end-to-end hash model to generate semantic hash codes. • Extensive experiments show NRDH can achieve higher performance and outperform the state-of-the-art unsupervised deep hashing methods. Supervised graph convolution network (GCN) based hashing algorithms have achieved good results by recognizing images according to the relationships between objects, but they are hard to be applied to label-free scenarios. Besides, most existing unsupervised deep hashing algorithms neglect the relationships between different samples and thus fail to achieve high precision. To address this problem, we propose NRDH, an unsupervised D eep H ashing method with N ode R epresentation for image retrieval, which adopts unsupervised GCN to integrate the relationships between samples into image visual features. NRDH consists of node representation learning stage and hash function learning stage. In the first stage, we treat each image as a node of a graph and design GCN-based AutoEncoder, which can integrate the relationships between samples into node representation. In the second stage, we use above node representations to guide the network and help learn the hash function to fast achieve an end-to-end hash model to generate semantic hash codes. Extensive experiments on CIFAR-10, MS-COCO and FLICKR25K show NRDH can achieve higher performance and outperform the state-of-the-art unsupervised deep hashing methods. [ABSTRACT FROM AUTHOR]

The structure of a sentence conveys rich linguistic knowledge and has proven useful for natural language understanding. In this paper, we aim to incorporate syntactical constraints and long-range word dependencies into the sentence encoding procedure using the widely applied Graph Convolutional Network (GCN) and word dependency trees. Existing syntax-aware GCN methods construct the adjacency matrix by referring to whether two words are connected in the dependency tree. But they fail to model the word dependency type, which reflects how the words are linked in dependency trees. They cannot distinguish the different contributions of different word dependency paths. To avoid introducing redundant word dependencies that harm language understanding, we propose a GCN version that is extended by a novel Word Dependency Gate mechanism. Word Dependency Gate can adaptively maintain the balance between the inclusion and exclusion of specific word dependency paths based on the word dependency type and its word context. Experiments show that our approach can effectively incorporate the relevant syntactical dependency in BERT and achieve a state-of-the-art performance in the End-to-End Aspect-Based Sentiment Analysis and Relation Triple Extraction tasks. • We propose exploiting syntactical structures through a GCN. • Design Word Dependency Gate that encode the edges and labels in the dependency trees. • Our model can effectively incorporate the syntactical dependency in BERT. • Achieve a state-of-the-art performance in End-to-End Aspect-Based Sentiment Analysis. [ABSTRACT FROM AUTHOR]

Sign language is the most important way of communication for hearing-impaired people. Research on sign language recognition can help normal people understand sign language. We reviewed the classic methods of sign language recognition, and the recognition accuracy is not high enough because of redundant information, human finger occlusion, motion blurring, the diversified signing styles of different people, and so on. To overcome these shortcomings, we propose a multi-scale and dual sign language recognition Network (SLR-Net) based on a graph convolutional network (GCN). The original input data was RGB videos. We first extracted the skeleton data from them and then used the skeleton data for sign language recognition. SLR-Net is mainly composed of three sub-modules: multi-scale attention network (MSA), multi-scale spatiotemporal attention network (MSSTA) and attention enhanced temporal convolution network (ATCN). MSA allows the GCN to learn the dependencies between long-distance vertices; MSSTA can directly learn the spatiotemporal features; ATCN allows the GCN network to better learn the long temporal dependencies. The three different attention mechanisms, multi-scale attention mechanism, spatiotemporal attention mechanism, and temporal attention mechanism, are proposed to further improve the robustness and accuracy. Besides, a keyframe extraction algorithm is proposed, which can greatly improve efficiency by sacrificing a little accuracy. Experimental results showed that our method can reach 98.08% accuracy rate in the CSL-500 dataset with a 500-word vocabulary. Even on the challenging dataset DEVISIGN-L with a 2000-word vocabulary, it also reached a 64.57% accuracy rate, outperforming other state-of-the-art sign language recognition methods. [ABSTRACT FROM AUTHOR]

Background: Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for various clinical applications. However, no prior work has utilized DL to extract relations between bleeding events and relevant entities., Objective: In this study, we aimed to evaluate multiple DL systems on a novel EHR data set for bleeding event-related relation classification., Methods: We first expert annotated a new data set of 1046 deidentified EHR notes for bleeding events and their attributes. On this data set, we evaluated three state-of-the-art DL architectures for the bleeding event relation classification task, namely, convolutional neural network (CNN), attention-guided graph convolutional network (AGGCN), and Bidirectional Encoder Representations from Transformers (BERT). We used three BERT-based models, namely, BERT pretrained on biomedical data (BioBERT), BioBERT pretrained on clinical text (Bio+Clinical BERT), and BioBERT pretrained on EHR notes (EhrBERT)., Results: Our experiments showed that the BERT-based models significantly outperformed the CNN and AGGCN models. Specifically, BioBERT achieved a macro F1 score of 0.842, outperforming both the AGGCN (macro F1 score, 0.828) and CNN models (macro F1 score, 0.763) by 1.4% (P<.001) and 7.9% (P<.001), respectively., Conclusions: In this comprehensive study, we explored and compared different DL systems to classify relations between bleeding events and other medical concepts. On our corpus, BERT-based models outperformed other DL models for identifying the relations of bleeding-related entities. In addition to pretrained contextualized word representation, BERT-based models benefited from the use of target entity representation over traditional sequence representation., (©Avijit Mitra, Bhanu Pratap Singh Rawat, David D McManus, Hong Yu. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.07.2021.)

Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient's cancer state and prognosis is the key challenge to develop a patient's therapeutic schedule. In the recent years, a variety of machine learning techniques widely contributed to analyzing real-world gene expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene expression data analysis by supplying computational models to support decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, and the complexity of data makes it difficult to extract vital features. Besides these, the efficacy of standard machine learning pipelines is far from being satisfactory despite the fact that diverse feature selection strategy had been applied. To address these problems, we developed directed relation-graph convolutional network to provide an advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on relational graph convolutional network method. The high-dimensional features of each sample were regarded as an image pixel, and convolutional neural network was implemented to predict the risk of metastasis for each patient. Ten cross-validations on 1,779 cases from The Cancer Genome Atlas show that our model's performance (area under the curve, AUC = 0.837; area under precision recall curve, AUPRC = 0.717) outstands that of an existing network-based method (AUC = 0.707, AUPRC = 0.555)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Xu, Cui and Wang.)

Social media had a revolutionary impact because it provides an ideal platform for share information; however, it also leads to the publication and spreading of rumors. Existing rumor detection methods have relied on finding cues from only user-generated content, user profiles, or the structures of wide propagation. However, the previous works have ignored the organic combination of wide dispersion structures in rumor detection and text semantics. To this end, we propose KZWANG, a framework for rumor detection that provides sufficient domain knowledge to classify rumors accurately, and semantic information and a propagation heterogeneous graph are symmetry fused together. We utilize an attention mechanism to learn a semantic representation of text and introduce a GCN to capture the global and local relationships among all the source microblogs, reposts, and users. An organic combination of text semantics and propagating heterogeneous graphs is then used to train a rumor detection classifier. Experiments on Sina Weibo, Twitter15, and Twitter16 rumor detection datasets demonstrate the proposed model's superiority over baseline methods. We also conduct an ablation study to understand the relative contributions of the various aspects of the method we proposed. [ABSTRACT FROM AUTHOR]

Traffic prediction on the road, as a vital part of the Intelligent Transportation System (ITS) has attracted much attention recently. It is always one of the hot topics about how to implement an efficient, robust, and accurate vehicular traffic prediction system. With the help of Machine Learning-based (ML) methods, especially Deep Learning-based (DL) methods, the accuracy of the prediction model is increased. However, we also noticed that there are still many open challenges under ML-based vehicular traffic prediction model real-world implementation. Firstly, the time consumption for training DL model is relatively large when compared to parametric models, such as ARIMA, SARIMA. Second, it is still a hot topic for road traffic prediction that how to capture the spacial relationship between road detectors, which is affected by the geographic correlation, as well as the time change. The last but not the least, it is important for us to implement the prediction system into the real world; meanwhile, we should find a way to make use of the advanced technology applied in ITS to improve the prediction system itself. In this paper, we focus on improving the features of the prediction model, which can be helpful for implementing the model in the real world. We present a new hybrid deep learning model by using Graph Convolutional Network (GCN) and the deep aggregation structure (i.e., the sequence to sequence structure) of Gated Recurrent Unit (GRU). Meanwhile, in order to solve the real-world prediction problem, i.e., the online prediction task, we present a new online prediction strategy by using refinement learning. In order to further improve the model's accuracy and efficiency when applied to ITS, we make use of an efficient parallel training strategy while taking advantage of the vehicular cloud structure. [ABSTRACT FROM AUTHOR]

This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic is still spreading around the world and seriously imperils humankind's health. This swift spread has caused the public to panic and look to scientists for answers. Fortunately, these scientists already have a wealth of data-the Covid-19 reports that each country releases, reports with valuable spatial-temporal properties. These data point toward some key actions that humans can take in their fight against Covid-19. Technically, the Covid-19 records can be described as sequences, which represent spatial-temporal linkages among the data elements with graph structure. Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. Specifically, IT-GCN introduces ARIMA into GCN to model the data which originate on nodes in a graph, indicating the severity of the pandemic in different cities. Instead of regular spatial topology, we construct the graph nodes with the vectors via ARIMA parameterization to find out the interaction topology underlying in the pandemic data. Experimental results show that IT-GCN is able to capture the comprehensive interaction-temporal topology and achieve well-performed short-term prediction of the Covid-19 daily infected cases in the United States. Our framework outperforms state-of-art baselines in terms of MAE, RMSE and MAPE. We believe that IT-GCN is a valid and reasonable method to forecast the Covid-19 daily infected cases and other related time-series. Moreover, the prediction can assist in improving containment policies., (This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.)

Functional connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. A brain network, also named as connectome, could form a graph structure naturally, the nodes of which are brain regions and the edges are interregional connectivity. Thus, in this study, we proposed novel graph convolutional networks (GCNs) to extract efficient disease-related features from FC matrices. Considering the time-dependent nature of brain activity, we computed dynamic FC matrices with sliding windows and implemented a graph convolution-based LSTM (long short-term memory) layer to process dynamic graphs. Moreover, the demographics of patients were also used as additional outputs to guide the classification. In this paper, we proposed to utilize the demographic information as extra outputs and to share parameters among three networks predicting subject status, gender, and age, which serve as assistant tasks. We tested the performance of the proposed architecture in ADNI II dataset to classify Alzheimer's disease patients from normal controls. The classification accuracy, sensitivity, and specificity reach 90.0%, 91.7%, and 88.6%, respectively, on ADNI II dataset., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The LA hybrid lily 'Aladdin' has both excellent traits of Longiflorum hybrids and Asiatic hybrids—such as big and vivid flower, strong stem, high self-propagation coefficient, and shorter low temperature time required to release bulb dormancy in contrast to Oriental hybrids. A genome-wide transcriptional analysis using transcriptome RNA-Seq was performed in order to explore whether there is a gibberellin floral induction pathway in the LA hybrid lily. Subsequently, gene co-expression network analysis was used to analyze the possible interactions of key candidate genes screened from transcriptome data. At the same time, a series of physiological, biochemical, and cultivation tests were carried out. Results: The content of five endogenous hormones changed sharply in the shoot apex during the treatment of 200 mg/L exogenous gibberellin and the ratio of ABA/GA3 dropped and stayed at a lower level after 4 hours' treatment from the higher levels initially, reaching a dynamic balance. In addition, the metabolism of carbohydrates in the bulbs increase during exogenous gibberellin treatment. A total of 124,041 unigenes were obtained by RNA-seq. With the transcriptome analysis, 48,927 unigenes and 48,725 unigenes respectively aligned to the NR database and the Uniprot database. 114,138 unigenes, 25,369 unigenes, and 19,704 unigenes respectively aligned to the COG, GO, and KEGG databases. 2148 differentially expression genes (DEGs) were selected with the indicators RPKM ≥ 0, FDR ≤ 0.05 and |log2(ratio)| ≥ 2. The number of the upregulated unigenes was significantly more than the number of the downregulated unigenes. Some MADS-box genes related to flowering transformation—such as AGL20, SOC1, and CO—were found to be upregulated. A large number of gibberellin biosynthesis related genes such as GA2ox, GA3ox, GA20ox, Cytochrome P450, CYP81, and gibberellin signal transduction genes such as DELLA, GASA, and GID1 were significantly differentially expressed. The plant hormones related genes such as NCED3 and sugar metabolism related genes such as α-amylase, sucrose synthase hexokinase, and so on were also found expressing differentially. In addition, stress resistance related genes such as LEA1, LEA2, LEA4, serine/threonine protein kinase, LRR receptor-like serine/threonine protein kinase, P34 kinase, histidine kinase 3 and epigenetic related genes in DNA methylation, histone methylation, acetylation, ubiquitination of ribose were also found. Particularly, a large number of transcription factors responsive to the exogenous gibberellin signal including WRKY40, WRKY33, WRKY27, WRKY21, WRKY7, MYB, AP2/EREBP, bHLH, NAC1, NAC2, and NAC11 were found to be specially expressing. 30 gene sequences were selected from a large number of differentially expressed candidate genes for qRT-PCR expression verification (0, 2, 4, 8, and 16 h) and compared with the transcriptome expression levels. Conclusions: 200mg/L exogenous GA3 can successfully break the bulb's dormancy of the LA hybrid lily and significantly accelerated the flowering process, indicating that gibberellin floral induction pathway is present in the LA lily 'Aladdin'. With the GCNs analysis, two second messenger G protein-coupled receptor related genes that respond to gibberellin signals in the cell were discovered. The downstream transport proteins such as AMT, calcium transport ATPase, and plasma membrane ATPase were also discovered participating in GA signal transduction. Transcription factors including WRKY7, NAC2, NAC11, and CBF specially regulated phosphorylation and glycosylation during the ubiquitination degradation process of DELLA proteins. These transcription factors also activated in abscisic acid metabolism. A large number of transcription factors such as WRKY21, WRKY22, NAC1, AP2, EREB1, P450, and CYP81 that both regulate gibberellin signaling and low-temperature signals have also been found. Finally, the molecular mechanism of GA floral induction pathway in the LA hybrid lily 'Aladdin' was constructed. [ABSTRACT FROM AUTHOR]

Mining patterns of co-expressed genes across the subset of conditions help to narrow down the search space for the analysis of gene expression data. Identifying conditions specific key genes from the large-scale gene expression data is a challenging task. The conditions specific key gene signifies functional behavior of a group of co-expressed genes across the subset of conditions and can be act as biomarkers of the diseases. In this paper, we have propose a novel approach for identification of conditions specific key genes from Basal-Like Breast Cancer (BLBC) disease using biclustering algorithm and Gene Co-expression Network (GCN). The proposed approach is a two-stage approach. In the first stage, significant biclusters have been extracted with the help of 'runibic' biclustering algorithm. The second stage identifies conditions specific key genes from the extracted significant biclusters with the help of GCN. By using difference matrix and gene correlation matrix, we have constructed biologically meaningful and statistically strong GCN. Also, presented the proposed approach with the help of a process diagram and demonstrated the procedure with an example of bicluster number 93 (Bic93). From the experimental results, we observed that 95% and 85% of the extracted biclusters are found to be biologically significant at the p-values less than 0.05 and 0.01 respectively. We have compared proposed approach with the Weighted Gene Co-expression Network Analysis (WGCNA) based approach. From the comparison, our approach has performed effectively and extracted biologically significant biclusters. Also, identified conditions specific key genes which cannot be extracted using the WGCNA based approach. Some of the important identified known key genes are PIK3CA, SHC3, ERBB2, SHC4, PTOV1, STAG1, ZNF215 etc. These key genes can be used as a diagnostic and prognostic biomarker for the BLBC disease after the rigorous analysis. The identified conditions specific key genes can be helpful to reduce the analysis time and increase the accuracy of further research such as biomarker identification, drug target discovery etc., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Saccharomyces cerevisiae strains are the main responsible of most traditional alcohol fermentation processes performed around the world. The characteristics of the diverse traditional fermentations are very different according to their sugar composition, temperature, pH or nitrogen sources. During the adaptation of yeasts to these new environments provided by human activity, their different compositions likely imposed selective pressures that shaped the S. cerevisiae genome. In the present work we performed a comparative genomic hybridization analysis to explore the genome constitution of six S. cerevisiae strains isolated from different traditional fermentations (masato, mescal, cachaça, sake, wine, and sherry wine) and one natural strain. Our results indicate that gene copy numbers (GCN) are very variable among strains, and most of them were observed in subtelomeric and intrachromosomal gene families involved in metabolic functions related to cellular homeostasis, cell-to-cell interactions, and transport of solutes such as ions, sugars and metals. In many cases, these genes are not essential but they can play an important role in the adaptation to new environmental conditions. However, the most interesting result is the association observed between GCN changes in genes involved in the nitrogen metabolism and the availability of nitrogen sources in the different traditional fermentation processes. This is clearly illustrated by the differences in copy numbers not only in gene PUT1, the main player in the assimilation of proline as a nitrogen source, but also in CAR2, involved in arginine catabolism. Strains isolated from fermentations where proline is more abundant contain a higher number of PUT1 copies and are more efficient in assimilating this amino acid as a nitrogen source. A strain isolated from sugarcane juice fermentations, in which arginine is a rare amino acid, contains less copies of CAR2 and showed low efficiency in arginine assimilation. These results suggest that nitrogen metabolism has played an important role in the adaptive evolution of S. cerevisiae strains., (© 2013.)

Background & Aims: Indoleamine 2,3 dioxygenase-1 (IDO1) catabolizes tryptophan along the kynurenine pathway. Although IDO1 is expressed in inflamed and neoplastic epithelial cells of the colon, its role in colon tumorigenesis is not well understood. We used genetic and pharmacologic approaches to manipulate IDO1 activity in mice with colitis-associated cancer and human colon cancer cell lines., Methods: C57Bl6 wild-type (control), IDO1-/-, Rag1-/-, and Rag1/IDO1 double-knockout mice were exposed to azoxymethane and dextran sodium sulfate to induce colitis and tumorigenesis. Colitis severity was assessed by measurements of disease activity, cytokine levels, and histologic analysis. In vitro experiments were conducted using HCT 116 and HT-29 human colon cancer cells. 1-methyl tryptophan and small interfering RNA were used to inhibit IDO1. Kynurenine pathway metabolites were used to simulate IDO1 activity., Results: C57Bl6 mice given pharmacologic inhibitors of IDO1 and IDO1-/- mice had lower tumor burdens and reduced proliferation in the neoplastic epithelium after administration of dextran sodium sulfate and azoxymethane than control mice. These reductions also were observed in Rag1/IDO1 double-knockout mice compared with Rag1-/- mice (which lack mature adaptive immunity). In human colon cancer cells, blockade of IDO1 activity reduced nuclear and activated β-catenin, transcription of its target genes (cyclin D1 and Axin2), and, ultimately, proliferation. Exogenous administration of IDO1 pathway metabolites kynurenine and quinolinic acid led to activation of β-catenin and proliferation of human colon cancer cells, and increased tumor growth in mice., Conclusions: IDO1, which catabolizes tryptophan, promotes colitis-associated tumorigenesis in mice, independent of its ability to limit T-cell-mediated immune surveillance. The epithelial cell-autonomous survival advantage provided by IDO1 to colon epithelial cells indicate its potential as a therapeutic target., (Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.)