The Blood AFB1-DNA Adduct Acting as a Biomarker for Predicting the Risk and Prognosis of Primary Hepatocellular Carcinoma

Aflatoxin B1 (AFB1) is an important carcinogen for primary hepatocellular carcinoma (PHCC). However, the values of blood AFB1-DNA adducts predicting HCC risk and prognosis have not still been clear. We conducted a hospital-based case-control study, consisting of 380 patients with pathologically diagnosed PHCC and 588 controls without any evidence of liver diseases, to elucidate the associations between the amount of AFB1-DNA adducts in the peripheral blood and the risk and outcome of HCC. All subjects had not the history of hepatitis B and C virus infection. AFB1-DNA adducts were tested using enzyme-linked immunosorbent assay. Cases with PHCC featured an increasing blood amount of AFB1-DNA adducts compared with controls (2.01 ± 0.71 vs. 0.98 ± 0.63 μmol/DNA). Increasing adduct amount significantly grew the risk of PHCC [risk values, 1.82 (1.34–2.48) and 3.82 (2.71–5.40) for medium and high adduct level, respectively]. Furthermore, compared with patients with low adduct level, these with medium or high adduct level faced a higher death and tumor-recurrence risk. These results suggest that the blood AFB1-DNA adducts may act as a potential biomarker for predicting the risk and prognosis of PHCC

High-efficient optical frequency mixing in all-dielectric metasurface empowered by multiple bound states in the continuum

We present nonlinear optical four-wave mixing in a silicon nanodisk dimer metasurface. Under the oblique incident plane waves, the designed metasurface exhibits a multi-resonant feature with simultaneous excitations of three quasi-bound states in the continuum (BIC). Through employing these quasi-BIC with maximizing electric field energy at the input bump wavelengths, significant enhancements of third-order nonlinear processes including third-harmonic generation, degenerate and non-degenerate four-wave mixing are demonstrated, giving rise to ten new frequencies in the visible wavelengths. This work may lead to a new frontier of ultracompact optical mixer for applications in optical circuitry, ultrasensitive sensing, and quantum nanophotonics

Relation-aware Graph Attention Model With Adaptive Self-adversarial Training

This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipeline, namely heterogeneous graph representation learning and negative sampling. Existing message passing-based graph neural networks use edges either for graph traversal and/or selection of message encoding functions. Ignoring the edge semantics could have severe repercussions on the quality of embeddings, especially when dealing with two nodes having multiple relations. Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training. Although existing hard negative sampling techniques can identify challenging negative relationships for optimization, new techniques are required to control false negatives during training as false negatives could corrupt the learning process. To address these issues, first, we propose RelGNN -- a message passing-based heterogeneous graph attention model. In particular, RelGNN generates the states of different relations and leverages them along with the node states to weigh the messages. RelGNN also adopts a self-attention mechanism to balance the importance of attribute features and topological features for generating the final entity embeddings. Second, we introduce a parameter-free negative sampling technique -- adaptive self-adversarial (ASA) negative sampling. ASA reduces the false-negative rate by leveraging positive relationships to effectively guide the identification of true negative samples. Our experimental evaluation demonstrates that RelGNN optimized by ASA for relationship prediction improves state-of-the-art performance across established benchmarks as well as on a real industrial dataset

A Diffusion model for POI recommendation

Next Point-of-Interest (POI) recommendation is a critical task in location-based services that aim to provide personalized suggestions for the user's next destination. Previous works on POI recommendation have laid focused on modeling the user's spatial preference. However, existing works that leverage spatial information are only based on the aggregation of users' previous visited positions, which discourages the model from recommending POIs in novel areas. This trait of position-based methods will harm the model's performance in many situations. Additionally, incorporating sequential information into the user's spatial preference remains a challenge. In this paper, we propose Diff-POI: a Diffusion-based model that samples the user's spatial preference for the next POI recommendation. Inspired by the wide application of diffusion algorithm in sampling from distributions, Diff-POI encodes the user's visiting sequence and spatial character with two tailor-designed graph encoding modules, followed by a diffusion-based sampling strategy to explore the user's spatial visiting trends. We leverage the diffusion process and its reversed form to sample from the posterior distribution and optimized the corresponding score function. We design a joint training and inference framework to optimize and evaluate the proposed Diff-POI. Extensive experiments on four real-world POI recommendation datasets demonstrate the superiority of our Diff-POI over state-of-the-art baseline methods. Further ablation and parameter studies on Diff-POI reveal the functionality and effectiveness of the proposed diffusion-based sampling strategy for addressing the limitations of existing methods

Longitudinal survey of microbiome associated with particulate matter in a megacity.

BackgroundWhile the physical and chemical properties of airborne particulate matter (PM) have been extensively studied, their associated microbiome remains largely unexplored. Here, we performed a longitudinal metagenomic survey of 106 samples of airborne PM2.5 and PM10 in Beijing over a period of 6 months in 2012 and 2013, including those from several historically severe smog events.ResultsWe observed that the microbiome composition and functional potential were conserved between PM2.5 and PM10, although considerable temporal variations existed. Among the airborne microorganisms, Propionibacterium acnes, Escherichia coli, Acinetobacter lwoffii, Lactobacillus amylovorus, and Lactobacillus reuteri dominated, along with several viral species. We further identified an extensive repertoire of genes involved in antibiotic resistance and detoxification, including transporters, transpeptidases, and thioredoxins. Sample stratification based on Air Quality Index (AQI) demonstrated that many microbial species, including those associated with human, dog, and mouse feces, exhibit AQI-dependent incidence dynamics. The phylogenetic and functional diversity of air microbiome is comparable to those of soil and water environments, as its composition likely derives from a wide variety of sources.ConclusionsAirborne particulate matter accommodates rich and dynamic microbial communities, including a range of microbial elements that are associated with potential health consequences

2-tert-Butyl-6-[(4-chloro-2-nitro­phen­yl)diazen­yl]-4-methylphenol

In the title compound, C17H18ClN3O3, the dihedral angle between the planes of the two benzene rings is 1.03 (7)°. The overall conformation of the mol­ecule is influenced, in part, by electron delocalization and by an intra­molecular bifurcated O—H⋯(O,N) hydrogen bonds. The O atoms of the nitro group, one of which serves as an H bond acceptor, are disordered over two sets of sites with refined occupancies of 0.56 (3) and 0.44 (3)

Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods