Metabolic profile, bioavailability and toxicokinetics of zearalenone-14-glucoside in rats after oral and intravenous administration by liquid chromatography high-resolution mass spectrometry and tandem mass spectrometry

Zearalenone-14-glucoside (ZEN-14G), a key modified mycotoxin, has attracted a great deal of attention due to the possible conversion to its free form of zearalenone (ZEN) exerting toxicity. In this study, the toxicokinetics of ZEN-14G were investigated in rats after oral and intravenous administration. The plasma concentrations of ZEN-14G and its major five metabolites were quantified using a validated liquid chromatography tandem mass spectrometry (LC-MS/MS) method. The data were analyzed via non-compartmental analysis using software WinNonlin 6.3. The results indicated that ZEN-14G was rapidly hydrolyzed into ZEN in vivo. In addition, the major parameters of ZEN-14G following intravenous administration were: area under the plasma concentration-time curve (AUC), 1.80 h.ng/mL; the apparent volume of distribution (V-Z), 7.25 L/kg; and total body clearance (CL), 5.02 mL/h/kg, respectively. After oral administration, the typical parameters were: AUC, 0.16 h.ng/mL; V-Z, 6.24 mL/kg; and CL, 4.50 mL/h/kg, respectively. The absolute oral bioavailability of ZEN-14G in rats was about 9%, since low levels of ZEN-14G were detected in plasma, which might be attributed to its extensive metabolism. Therefore, liquid chromatography high-resolution mass spectrometry (LC-HRMS) was adopted to clarify the metabolic profile of ZEN-14G in rats' plasma. As a result, eight metabolites were identified in which ZEN-14-glucuronic acid (ZEN-14GlcA) had a large yield from the first time-point and continued accumulating after oral administration, indicating that ZEN-14-glucuronic acid could serve a potential biomarker of ZEN-14G. The obtained outcomes would prompt the accurate safety evaluation of ZEN-14G

B7-H4 Polymorphism Influences the Prevalence of Diabetes Mellitus and Pro-Atherogenic Dyslipidemia in Patients with Psoriasis.

BACKGROUND The co-inhibitory molecule B7-H4 is located in the genomic regions associated with type 1 diabetes (T1D) susceptibility. However, the correlation of B7-H4 with glycometabolism and dyslipidemia has never been studied. OBJECTIVE To explore the influence of B7-H4 polymorphism on the prevalence of diabetes mellitus (DM) and dyslipidemia in psoriasis. METHODS In this single-center cross-sectional study, we recruited 265 psoriatic patients receiving methotrexate (MTX) treatment. Thirteen single-nucleotide polymorphisms (SNPs) in B7-H4 were genotyped. Serum levels of total cholesterol (TC), triglycerides (TG), lipoprotein (a) (LP(a)), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB) were measured at baseline and week 12. RESULTS The GG genotype carriers of rs12025144 in B7-H4 had a higher prevalence of DM (57.14% vs. 17.71% vs. 18.67%, p = 0.0018), and had a poorer response to MTX in diabetic patients (p < 0.05), compared with AA or AG genotype carriers. The AG genotype of rs2066398 was associated with higher levels of pro-atherogenic lipids. MTX significantly downregulated the level of anti-atherogenic lipid ApoA1 in AA genotype carriers of rs2066398. CONCLUSIONS The genotypes rs12025144 and rs2066398 in B7-H4 were correlated with a higher prevalence of DM and dyslipidemia in psoriasis, respectively

Fully-Connected Spatial-Temporal Graph for Multivariate Time Series Data

Multivariate Time-Series (MTS) data is crucial in various application fields. With its sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits Spatial-Temporal (ST) dependencies, involving temporal correlations between timestamps and spatial correlations between sensors in each timestamp. To effectively leverage this information, Graph Neural Network-based methods (GNNs) have been widely adopted. However, existing approaches separately capture spatial dependency and temporal dependency and fail to capture the correlations between Different sEnsors at Different Timestamps (DEDT). Overlooking such correlations hinders the comprehensive modelling of ST dependencies within MTS data, thus restricting existing GNNs from learning effective representations. To address this limitation, we propose a novel method called Fully-Connected Spatial-Temporal Graph Neural Network (FC-STGNN), including two key components namely FC graph construction and FC graph convolution. For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT. Further, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations. Extensive experiments show the effectiveness of FC-STGNN on multiple MTS datasets compared to SOTA methods.Comment: 9 pages, 8 figure

How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions

While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition? This study seeks to explore this question through the lens of syntax, a crucial component of sentence comprehension. Adopting a natural language question-answering (Q&A) scheme, we craft questions targeting nine syntactic knowledge points that are most closely related to sentence comprehension. Experiments conducted on 24 LLMs suggest that most have a limited grasp of syntactic knowledge, exhibiting notable discrepancies across different syntactic knowledge points. In particular, questions involving prepositional phrase attachment pose the greatest challenge, whereas those concerning adjectival modifier and indirect object are relatively easier for LLMs to handle. Furthermore, a case study on the training dynamics of the LLMs reveals that the majority of syntactic knowledge is learned during the initial stages of training, hinting that simply increasing the number of training tokens may not be the `silver bullet' for improving the comprehension ability of LLMs.Comment: 20 pages, 6 figure

Graph Contextual Contrasting for Multivariate Time Series Classification

Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views of unlabeled samples and then learns effective representations for these samples. Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques, aiming to preserve temporal patterns against perturbations for MTS data. However, they overlook spatial consistency that requires the stability of individual sensors and their correlations. As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data. Thus, we propose Graph Contextual Contrasting (GCC) for spatial consistency across MTS data. Specifically, we propose graph augmentations including node and edge augmentations to preserve the stability of sensors and their correlations, followed by graph contrasting with both node- and graph-level contrasting to extract robust sensor- and global-level features. We further introduce multi-window temporal contrasting to ensure temporal consistency in the data for each sensor. Extensive experiments demonstrate that our proposed GCC achieves state-of-the-art performance on various MTS classification tasks.Comment: 9 pages, 5 figure

SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between a labeled source domain and an unlabeled target domain. However, these methods face challenges when dealing with Multivariate Time-Series (MTS) data. MTS data typically consist of multiple sensors, each with its own unique distribution. This characteristic makes it hard to adapt existing UDA methods, which mainly focus on aligning global features while overlooking the distribution discrepancies at the sensor level, to reduce domain discrepancies for MTS data. To address this issue, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both the local and global sensor levels. At the local sensor level, we design endo-feature alignment, which aligns sensor features and their correlations across domains. To reduce domain discrepancy at the global sensor level, we design exo-feature alignment that enforces restrictions on global sensor features. We further extend SEA to SEA++ by enhancing the endo-feature alignment. Particularly, we incorporate multi-graph-based high-order alignment for both sensor features and their correlations. Extensive empirical results have demonstrated the state-of-the-art performance of our SEA and SEA++ on public MTS datasets for MTS-UDA

Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation

To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models. Despite improvements made in model robustness, these VUDA methods require access to both source data and source model parameters for adaptation, raising serious data privacy and model portability issues. To cope with the above concerns, this paper firstly formulates Black-box Video Domain Adaptation (BVDA) as a more realistic yet challenging scenario where the source video model is provided only as a black-box predictor. While a few methods for Black-box Domain Adaptation (BDA) are proposed in image domain, these methods cannot apply to video domain since video modality has more complicated temporal features that are harder to align. To address BVDA, we propose a novel Endo and eXo-TEmporal Regularized Network (EXTERN) by applying mask-to-mix strategies and video-tailored regularizations: endo-temporal regularization and exo-temporal regularization, performed across both clip and temporal features, while distilling knowledge from the predictions obtained from the black-box predictor. Empirical results demonstrate the state-of-the-art performance of EXTERN across various cross-domain closed-set and partial-set action recognition benchmarks, which even surpassed most existing video domain adaptation methods with source data accessibility.Comment: 9 pages, 4 figures, and 4 table

JWA Deficiency Suppresses Dimethylbenz[a]Anthracene-Phorbol Ester Induced Skin Papillomas via Inactivation of MAPK Pathway in Mice

Our previous studies indicated that JWA plays an important role in DNA damage repair, cell migration, and regulation of MAPKs. In this study, we investigated the role of JWA in chemical carcinogenesis using conditional JWA knockout (JWAΔ2/Δ2) mice and two-stage model of skin carcinogenesis. Our results indicated that JWAΔ2/Δ2 mice were resistant to the development of skin papillomas initiated by 7, 12-dimethylbenz(a)anthracene (DMBA) followed by promotion with 12-O-tetradecanoylphorbol-13-acetate (TPA). In JWAΔ2/Δ2 mice, the induction of papilloma was delayed, and the tumor number and size were reduced. In primary keratinocytes from JWAΔ2/Δ2 mice, DMBA exposure induced more intensive DNA damage, while TPA-promoted cell proliferation was reduced. The further mechanistic studies showed that JWA deficiency blocked TPA-induced activation of MAPKs and its downstream transcription factor Elk1 both in vitro and in vivo. JWAΔ2/Δ2 mice are resistance to tumorigenesis induced by DMBA/TPA probably through inhibition of transcription factor Elk1 via MAPKs. These results highlight the importance of JWA in skin homeostasis and in the process of skin tumor development