Predicting antimicrobial resistance in E. coli with discriminative position fused deep learning classifier.

Escherichia coli (E. coli) has become a particular concern due to the increasing incidence of antimicrobial resistance (AMR) observed worldwide. Using machine learning (ML) to predict E. coli AMR is a more efficient method than traditional laboratory testing. However, further improvement in the predictive performance of existing models remains challenging. In this study, we collected 1937 high-quality whole genome sequencing (WGS) data from public databases with an antimicrobial resistance phenotype and modified the existing workflow by adding an attention mechanism to enable the modified workflow to focus more on core single nucleotide polymorphisms (SNPs) that may significantly lead to the development of AMR in E. coli. While comparing the model performance before and after adding the attention mechanism, we also performed a cross-comparison among the published models using random forest (RF), support vector machine (SVM), logistic regression (LR), and convolutional neural network (CNN). Our study demonstrates that the discriminative positional colors of Chaos Game Representation (CGR) images can selectively influence and highlight genome regions without prior knowledge, enhancing prediction accuracy. Furthermore, we developed an online tool (https://github.com/tjiaa/E.coli-ML/tree/main) for assisting clinicians in the rapid prediction of the AMR phenotype of E. coli and accelerating clinical decision-making.

Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement.

Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.

Natural Occurrence of Regulated and Emerging Mycotoxins in Wheat Grains and Assessment of the Risks from Dietary Mycotoxins Exposure in China.

Wheat grains are susceptible to contamination with various natural mycotoxins including regulated and emerging mycotoxins. This study surveyed the natural presence of regulated mycotoxins, such as deoxynivalenol (DON) and zearalenone (ZEN), and emerging mycotoxins such as beauvericin (BEA), enniatins (ENNs such as ENA, ENA1, ENB, ENB1) and Alternaria mycotoxins (i.e., alternariol monomethyl ether (AME), alternariol (AOH), tenuazonic acid (TeA), tentoxin (TEN), and altenuene (ALT)) in wheat grains randomly collected from eight provinces across China in 2021. The results revealed that each wheat grain sample was detected with at least one type of mycotoxin. The detection rates of these mycotoxins ranged from 7.1% to 100%, with the average occurrence level ranging from 1.11 to 921.8 µg/kg. DON and TeA were the predominant mycotoxins with respect to both prevalence and concentration. Approximately 99.7% of samples were found to contain more than one toxin, and the co-occurrence of ten toxins (DON + ZEN + ENA + ENA1 + ENB + ENB1 + AME + AOH + TeA + TEN) was the most frequently detected combination. The dietary exposure to different mycotoxins among Chinese consumers aged 4-70 years was as follows: 0.592-0.992 µg/kg b.w./day for DON, 0.007-0.012 µg/kg b.w./day for ZEN, 0.0003-0.007 µg/kg b.w./day for BEA and ENNs, 0.223-0.373 µg/kg b.w./day for TeA, and 0.025-0.041 µg/kg b.w./day for TEN, which were lower than the health-based guidance values for each mycotoxin, with the corresponding hazard quotient (HQ) being far lower than 1, implying a tolerable health risk for Chinese consumers. However, the estimated dietary exposure to AME and AOH was in the range of 0.003-0.007 µg/kg b.w./day, exceeding the Threshold of Toxicological Concern (TTC) value of 0.0025 µg/kg b.w./day, demonstrating potential dietary risks for Chinese consumers. Therefore, developing practical control and management strategies is essential for controlling mycotoxins contamination in the agricultural systems, thereby ensuring public health.

A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations.

In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students' latent knowledge and cognitive level is difficult, (2) the content of quizzes can be totally different but the knowledge points of these quizzes may be inherently related, and (3) ranking models based on supervised, semisupervised, or reinforcement learning focus on the current assignment without considering past performance. In this work, we propose KFRank, a knowledge-fusion ranking model based on reinforcement learning, which considers both a student's assignment history and the relevance of quizzes with their knowledge points. First, we load students' assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student's knowledge cognitive and the question. Then, a similarity estimator is built to choose historical questions, and an attention neural network is used to calculate the attention value and update the current study state with knowledge fusion. Finally, a rank algorithm based on a Markov decision process is used to optimize the parameters. Extensive experiments were conducted on a real-life dataset spanning a year and we compared our model with the state-of-the-art ranking models (e.g., ListNET and LambdaMART) and reinforcement-learning methods (such as MDPRank). Based on top-k nDCG values, our model outperforms other methods for groups of average and weak students, whose study abilities are relatively poor and thus their behaviors are more difficult to predict.

Pharmaceutical drugs chatter on Online Social Networks.

The ubiquity of Online Social Networks (OSNs) is creating new sources for healthcare information, particularly in the context of pharmaceutical drugs. We aimed to examine the impact of a given OSN's characteristics on the content of pharmaceutical drug discussions from that OSN. We compared the effect of four distinguishing characteristics from ten different OSNs on the content of their pharmaceutical drug discussions: (1) General versus Health OSN; (2) OSN moderation; (3) OSN registration requirements; and (4) OSNs with a question and answer format. The effects of these characteristics were measured both quantitatively and qualitatively. Our results show that an OSN's characteristics indeed affect the content of its discussions. Based on their information needs, healthcare providers may use our findings to pick the right OSNs or to advise patients regarding their needs. Our results may also guide the creation of new and more effective domain-specific health OSNs. Further, future researchers of online healthcare content in OSNs may find our results informative while choosing OSNs as data sources. We reported several findings about the impact of OSN characteristics on the content of pharmaceutical drug discussion, and synthesized these findings into actionable items for both healthcare providers and future researchers of healthcare discussions on OSNs. Future research on the impact of OSN characteristics could include user demographics, quality and safety of information, and efficacy of OSN usage.