Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper)

Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level traffic risk prediction, with a focus on uncertainty quantification. Our case study, conducted in the Lambeth borough of London, UK, demonstrates the superior performance of our approach in comparison to existing methods. Although the negative binomial distribution may not be the most suitable choice for handling real, non-binary risk levels, our work lays a solid foundation for future research exploring alternative distribution models or techniques. Ultimately, the STZINB-GNN contributes to enhanced transportation safety and data-driven decision-making in urban planning by providing a more accurate and reliable framework for road-level traffic risk prediction and uncertainty quantification

Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets predominantly feature zero values, indicating no incidents, with sporadic high-risk values for severe incidents. Notably, a majority of current models, especially deep learning methods, focus solely on estimating risk values, overlooking the uncertainties arising from the inherently unpredictable nature of incidents. To tackle this challenge, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks (STZITD-GNNs). Our model merges the reliability of traditional statistical models with the flexibility of graph neural networks, aiming to precisely quantify uncertainties associated with road-level traffic incident risks. This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity. Furthermore, a zero-inflated component helps to identify the non-incident risk scenarios. As a result, the STZITD-GNNs effectively capture the dataset's skewed distribution, placing emphasis on infrequent but impactful severe incidents. Empirical tests using real-world traffic data from London, UK, demonstrate that our model excels beyond current benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also in its adeptness at curtailing uncertainties, delivering robust predictions over short (7 days) and extended (14 days) timeframes

Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatialtemporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional ’zero-inflated’ model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using realworld datasets highlight STTD’s superiority in providing accurate predictions and precise confidence intervals, particularly in highresolution scenarios. GitHub code is available online

The complete chloroplast genome sequence of Gentiana triflora and comparative analysis with its congeneric species

Gentiana triflora is an important medicinal plant in China with economic and medicinal value. Here, we report the complete chloroplastsequences of G. trifloral. The cp genome of G. triflora of 149, 125 bp contains 130 unique genes, including 85 protein-coding genes,8 rRNA genes, and 37 tRNA genes. The analysis of repeat showed that palindromic had the highest frequency. Besides, a total numberof 45 SSR were identified, most of which were mononucleotide adenine-thymine. Comparative genome analysis of Gentiana speciesrevealed that the pair of the inverted repeat was more conserved than the single-copy region. This analysis resulted in identification of 8 hypervariable regions (trnH-GUG, trnG-UCC-intron, atpI, trnD-GUC, trnL-UAA, rpl32-trnL-UAG, petA and ycf1). Phylogenetic analysis revealed that G. triflora was most closely related to Gentiana manshurica. In conclusion, this study enriched the genomic resources of the Gentiana genus and provided a basis for evolution and phylogeny analyses

Identification of DHX36 as a tumour suppressor through modulating the activities of the stress-associated proteins and cyclin-dependent kinases in breast cancer

The nucleic acid guanine-quadruplex structures (G4s) are involved in many aspects of cancer progression. The DEAH-box polypeptide 36 (DHX36) has been identified as a dominant nucleic acid helicase which targets and disrupts DNA and RNA G4s in an ATP-dependent manner. However, the actual role of DHX36 in breast cancer remains unknown. In this study, we observed that the gene expression of DHX36 was positively associated with patient survival in breast cancer. The abundance of DHX36 is also linked with pathologic conditions and the stage of breast cancer. By using the xenograft mouse model, we demonstrated that the stable knockdown of DHX36 via lentivirus in breast cancer cells significantly promoted tumour growth. We also found that, after the DHX36 knockdown (KD), the invasion of triple-negative breast cancer cells was enhanced. In addition, we found a significant increase in the number of cells in the S-phase and a reduction of apoptosis with the response to cisplatin. DHX36 KD also desensitized the cytotoxic cellular response to paclitaxel and cisplatin. Transcriptomic profiling analysis by RNA sequencing indicated that DHX36 altered gene expression profile through the upstream activation of TNF, IFNγ, NFκb and TGFβ1. High throughput signalling analysis showed that one cluster of stress-associated kinase proteins including p53, ROCK1 and JNK were suppressed, while the mitotic checkpoint protein-serine kinases CDK1 and CDK2 were activated, as a consequence of the DHX36 knockdown. Our study reveals that DHX36 functions as a tumour suppressor and may be considered as a potential therapeutic target in breast cancer

Identification of DHX36 as a tumour suppressor through modulating the activities of the stress-associated proteins and cyclin-dependent kinases in breast cancer

The nucleic acid guanine-quadruplex structures (G4s) are involved in many aspects of cancer progression. The DEAH-box polypeptide 36 (DHX36) has been identified as a dominant nucleic acid helicase which targets and disrupts DNA and RNA G4s in an ATP-dependent manner. However, the actual role of DHX36 in breast cancer remains unknown. In this study, we observed that the gene expression of DHX36 was positively associated with patient survival in breast cancer. The abundance of DHX36 is also linked with pathologic conditions and the stage of breast cancer. By using the xenograft mouse model, we demonstrated that the stable knockdown of DHX36 via lentivirus in breast cancer cells significantly promoted tumour growth. We also found that, after the DHX36 knockdown (KD), the invasion of triple-negative breast cancer cells was enhanced. In addition, we found a significant increase in the number of cells in the S-phase and a reduction of apoptosis with the response to cisplatin. DHX36 KD also desensitized the cytotoxic cellular response to paclitaxel and cisplatin. Transcriptomic profiling analysis by RNA sequencing indicated that DHX36 altered gene expression profile through the upstream activation of TNF, IFNγ, NFκb and TGFβ1. High throughput signalling analysis showed that one cluster of stress-associated kinase proteins including p53, ROCK1 and JNK were suppressed, while the mitotic checkpoint protein-serine kinases CDK1 and CDK2 were activated, as a consequence of the DHX36 knockdown. Our study reveals that DHX36 functions as a tumour suppressor and may be considered as a potential therapeutic target in breast cancer

Vital Role of Synthesis Temperature in Co–Cu Layered Hydroxides and Their Fenton-like Activity for RhB Degradation

Cu and Co have shown superior catalytic performance to other transitional elements, and layered double hydroxides (LDHs) have presented advantages over other heterogeneous Fenton catalysts. However, there have been few studies about Co–Cu LDHs as catalysts for organic degradation via the Fenton reaction. Here, we prepared a series of Co–Cu LDH catalysts by a co-precipitation method under different synthesis temperatures and set Rhodamine B (RhB) as the target compound. The structure-performance relationship and the influence of reaction parameters were explored. A study of the Fenton-like reaction was conducted over Co–Cu layered hydroxide catalysts, and the variation of synthesis temperature greatly influenced their Fenton-like catalytic performance. The Co–Cut=65°C catalyst with the strongest LDH structure showed the highest RhB removal efficiency (99.3% within 30 min). The change of synthesis temperature induced bulk-phase transformation, structural distortion, and metal–oxygen (M–O) modification. An appropriate temperature improved LDH formation with defect sites and lengthened M–O bonds. Co–Cu LDH catalysts with a higher concentration of defect sites promoted surface hydroxide formation for H2O2 adsorption. These oxygen vacancies (Ovs) promoted electron transfer and H2O2 dissociation. Thus, the Co–Cu LDH catalyst is an attractive alternative organic pollutants treatment

Vital Role of Synthesis Temperature in Co–Cu Layered Hydroxides and Their Fenton-like Activity for RhB Degradation

Cu and Co have shown superior catalytic performance to other transitional elements, and layered double hydroxides (LDHs) have presented advantages over other heterogeneous Fenton catalysts. However, there have been few studies about Co–Cu LDHs as catalysts for organic degradation via the Fenton reaction. Here, we prepared a series of Co–Cu LDH catalysts by a co-precipitation method under different synthesis temperatures and set Rhodamine B (RhB) as the target compound. The structure-performance relationship and the influence of reaction parameters were explored. A study of the Fenton-like reaction was conducted over Co–Cu layered hydroxide catalysts, and the variation of synthesis temperature greatly influenced their Fenton-like catalytic performance. The Co–Cut=65°C catalyst with the strongest LDH structure showed the highest RhB removal efficiency (99.3% within 30 min). The change of synthesis temperature induced bulk-phase transformation, structural distortion, and metal–oxygen (M–O) modification. An appropriate temperature improved LDH formation with defect sites and lengthened M–O bonds. Co–Cu LDH catalysts with a higher concentration of defect sites promoted surface hydroxide formation for H2O2 adsorption. These oxygen vacancies (Ovs) promoted electron transfer and H2O2 dissociation. Thus, the Co–Cu LDH catalyst is an attractive alternative organic pollutants treatment