Hyperbolic Adversarial Learning for Personalized Item Recommendation

Personalized recommendation systems are indispensable intelligent components for social media and e-commerce. Traditional personalized item recommendation models are vulnerable to adversarial perturbations, resulting in poor robustness. Although adversarial learning-based recommendation models are able to improve the robustness, they inherently model the interaction relationships between users and items in Euclidean space, where it is difficult for them to capture the hierarchical relationships among entities. To address the above issues, we propose a hyperbolic adversarial learning based personalized item recommendation model, called HALRec. Specifically, HALRec models the interactions in hyperbolic space and utilizes hyperbolic distances to measure the similarities among entities. Moreover, instead of in Euclidean space, HALRec exploits the adversarial learning technique in hyperbolic space, i.e., HAL-Rec maximizes the hyperbolic adversarial perturbations loss while minimizing the hyperbolic based Bayesian personalized ranking loss. Hence, HALRec inherits the advantages of hyperbolic representation learning in capturing hierarchical relationships and adversarial learning in enhancing the robustness of the recommendation model. In addition, we utilize tangent space optimization to simplify the learning of model parameters. Experimental results on real-world datasets show that our proposed hyperbolic adversarial learning-based personalized item recommendation method outperforms the state-of-the-art personalized recommendation algorithms

Sensitive Determination of Trace 4-Nitrophenol in Ambient Environment Using a Glassy Carbon Electrode Modified with Formamide-Converted Nitrogen-Doped Carbon Materials

Sensing trace amounts of 4-nitrophenol (4-NP) as a harmful substance to organisms even in small quantities is of great importance. The present study includes a sensitive and selective electrochemical sensor for detecting 4-NP in natural water samples using formamide-converted nitrogen-carbon materials (shortened to f-NC) as a new material for electrode modification. The structure and morphology of the f-NC were set apart by SEM, TEM, XRD, XPS, FTIR, Raman, and the electrochemical performance of the f-NC were set apart by CV, EIS and CC. We studied the electrochemical behaviour of 4-NP on the glassy carbon electrode modified with f-NC before and after pyrolysis treatment (denoted as f-NC1/GCE and f-NC2/GCE). In 0.2 M of H2SO4 solution, the f-NC2/GCE has an apparent electrocatalytic activity to reduce 4-NP. Under the optimal conditions, the reduction peak current of 4-NP varies linearly, with its concentration in the range of 0.2 to 100 mM, and the detection limit obtained as 0.02 mM (S/N = 3). In addition, the electrochemical sensor has high selectivity, and the stability is quite good. The preparation and application of the sensor to detect 4-NP in water samples produced satisfactory results, which provides a new method for the simple, sensitive and quantitative detection of 4-NP

Complete Genome Sequence Reveals Evolutionary and Comparative Genomic Features of Xanthomonas albilineans Causing Sugarcane Leaf Scald

International audienceLeaf scald (caused by Xanthomonas albilineans) is an important bacterial disease affecting sugarcane in most sugarcane growing countries, including China. High genetic diversity exists among strains of X. albilineans from diverse geographic regions. To highlight the genomic features associated with X. albilineans from China, we sequenced the complete genome of a representative strain (Xa-FJ1) of this pathogen using the PacBio and Illumina platforms. The complete genome of strain Xa-FJ1 consists of a circular chromosome of 3,724,581 bp and a plasmid of 31,536 bp. Average nucleotide identity analysis revealed that Xa-FJ1 was closest to five strains from the French West Indies and the USA, particularly to the strain GPE PC73 from Guadeloupe. Comparative genomic analysis between Xa-FJ1 and GPE PC73 revealed prophage integration, homologous recombination, transposable elements, and a clustered regulatory interspaced short palindromic repeats (CRISPR) system that were linked with 16 insertions/deletions (InDels). Ten and 82 specific genes were found in Xa-FJ1 and GPE PC73, respectively, and some of these genes were subjected to phage-related proteins, zona occludens toxin, and DNA methyltransferases. Our findings highlight intra-species genetic variability of the leaf scald pathogen and provide additional genomic resources to investigate its fitness and virulence

Transcriptional Dysregulations of Seven Non-Differentially Expressed Genes as Biomarkers of Metastatic Colon Cancer

Background: Colon cancer (CC) is common, and the mortality rate greatly increases as the disease progresses to the metastatic stage. Early detection of metastatic colon cancer (mCC) is crucial for reducing the mortality rate. Most previous studies have focused on the top-ranked differentially expressed transcriptomic biomarkers between mCC and primary CC while ignoring non-differentially expressed genes. Results: This study proposed that the complicated inter-feature correlations could be quantitatively formulated as a complementary transcriptomic view. We used a regression model to formulate the correlation between the expression levels of a messenger RNA (mRNA) and its regulatory transcription factors (TFs). The change between the predicted and real expression levels of a query mRNA was defined as the mqTrans value in the given sample, reflecting transcription regulatory changes compared with the model-training samples. A dark biomarker in mCC is defined as an mRNA gene that is non-differentially expressed in mCC but demonstrates mqTrans values significantly associated with mCC. This study detected seven dark biomarkers using 805 samples from three independent datasets. Evidence from the literature supports the role of some of these dark biomarkers. Conclusions: This study presented a complementary high-dimensional analysis procedure for transcriptome-based biomarker investigations with a case study on mCC

A New Biocontrol Agent <i>Bacillus velezensis</i> SF334 against Rubber Tree Fungal Leaf Anthracnose and Its Genome Analysis of Versatile Plant Probiotic Traits

Natural rubber is an important national strategic and industrial raw material. The leaf anthracnose of rubber trees caused by the Colletotrichum species is one of the important factors restricting the yields of natural rubber. In this study, we isolated and identified strain Bacillus velezensis SF334, which exhibited significant antagonistic activity against both C. australisinense and C. siamense, the dominant species of Colletotrichum causing rubber tree leaf anthracnose in the Hainan province of China, from a pool of 223 bacterial strains. The cell suspensions of SF334 had a significant prevention effect for the leaf anthracnose of rubber trees, with an efficacy of 79.67% against C. siamense and 71.8% against C. australisinense. We demonstrated that SF334 can lead to the lysis of C. australisinense and C. siamense mycelia by causing mycelial expansion, resulting in mycelial rupture and subsequent death. B. velezensis SF334 also harbors some plant probiotic traits, such as secreting siderophore, protease, cellulase, pectinase, and the auxin of indole-3-acetic acid (IAA), and it has broad-spectrum antifungal activity against some important plant pathogenic fungi. The genome combined with comparative genomic analyses indicated that SF334 possesses most genes of the central metabolic and gene clusters of secondary metabolites in B. velezensis strains. To our knowledge, this is the first time a Bacillus velezensis strain has been reported as a promising biocontrol agent against the leaf anthracnose of rubber trees caused by C. siamense and C. australisinense. The results suggest that B. velezensis could be a potential candidate agent for the leaf anthracnose of rubber trees

Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality

Abstract Background Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts. Methods We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients. Results Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values. Conclusions Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php

Additional file 1 of Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality

Supplementary Material