The impact of spatio-temporal travel distance on epidemics using an interpretable attention-based sequence-to-sequence model

Amidst the COVID-19 pandemic, travel restrictions have emerged as crucial interventions for mitigating the spread of the virus. In this study, we enhance the predictive capabilities of our model, Sequence-to-Sequence Epidemic Attention Network (S2SEA-Net), by incorporating an attention module, allowing us to assess the impact of distinct classes of travel distances on epidemic dynamics. Furthermore, our model provides forecasts for new confirmed cases and deaths. To achieve this, we leverage daily data on population movement across various travel distance categories, coupled with county-level epidemic data in the United States. Our findings illuminate a compelling relationship between the volume of travelers at different distance ranges and the trajectories of COVID-19. Notably, a discernible spatial pattern emerges with respect to these travel distance categories on a national scale. We unveil the geographical variations in the influence of population movement at different travel distances on the dynamics of epidemic spread. This will contribute to the formulation of strategies for future epidemic prevention and public health policies.Comment: 18 pages, 7 figure

Production of Glycopeptide Derivatives for Exploring Substrate Specificity of Human OGA Toward Sugar Moiety.

O-GlcNAcase (OGA) is the only enzyme responsible for removing N-acetyl glucosamine (GlcNAc) attached to serine and threonine residues on proteins. This enzyme plays a key role in O-GlcNAc metabolism. However, the structural features of the sugar moiety recognized by human OGA (hOGA) remain unclear. In this study, a set of glycopeptides with modifications on the GlcNAc residue, were prepared in a recombinant full-length human OGT-catalyzed reaction, using chemoenzymatically synthesized UDP-GlcNAc derivatives. The resulting glycopeptides were used to evaluate the substrate specificity of hOGA toward the sugar moiety. This study will provide insights into the exploration of probes for O-GlcNAc modification, as well as a better understanding of the roles of O-GlcNAc in cellular physiology

The impact of digital finance on water use intensity in China and mechanisms

[Objective] Digital finance based on information technology provides a new opportunity for the construction of a water-saving society. Effectively release the dividend of water saving and emission reduction brought by the development of digital finance is an important issue for promoting the development of the Chinese-style modernization. [Methods] Based on panel data from 30 provinces in China from 2011 to 2020, this study empirically tested the relationship between digital finance development and water resource utilization using methods such as fixed effect and mediation effect models. [Results] (1) The development of digital finance has an inhibitory effect on water use intensity, which is mainly achieved by improving the coverage and depth of use of digital finance, and the result is still valid after robustness tests such as substitution of variables, instrumental variable method, and limited information maximum likelihood method. (2) From the perspective of transmission pathways, technological innovation and industrial structure adjustment have played a significant mediating role between digital finance and water use intensity, but the mediating role of industrial structure upgrading is not significant. (3) From the perspective of heterogeneity, digital finance has a positive impact on water use efficiency in the eastern region; Meanwhile, digital finance has a significant inhibitory effect on the water use intensity of the primary and secondary industries, but the impact on the water use intensity of the tertiary industry is not significant; Regions with favorable water endowments are more likely to reap the benefits of digital finance. [Conclusion] Therefore, it is necessary to strengthen the construction of digital finance, actively innovate financial services, and give full play to the enabling role of digital finance in resource utilization according to local conditions and production policies

Genetic Polymorphism and mRNA Expression Studies Reveal IL6R and LEPR Gene Associations with Reproductive Traits in Chinese Holsteins

Genetic selection of milk yield traits alters the energy distribution of high producing cows, resulting in gene-induced negative energy balance, and consequently, poor body condition scores and reduced reproductive performances. Here, we investigated two metabolic-syndrome pathway genes, IL6R (Interleukin 6 receptor) and LEPR (Leptin receptor), for their polymorphism effects on reproductive performance in dairy cows, by applying polymorphism association analyses in 1588 Chinese Holstein cows (at population level) and gene expression analyses in granulosa cells isolated from eight cows (at cell level). Among the six single nucleotide polymorphisms we examined (two SNPs for IL6R and four SNPs for LEPR), five were significantly associated with at least one reproductive trait, including female fertility traits covering both the ability to recycle after calving and the ability to conceive and keep pregnancy when inseminated properly, as well as calving traits. Notably, the identified variant SNP g.80143337A/C in LEPR is a missense variant. The role of IL6R and LEPR in cattle reproduction were further confirmed by observed differences in relative gene expression levels amongst granulosa cells with different developmental stages. Collectively, the functional validation of IL6R and LEPR performed in this study improved our understanding of cattle reproduction while providing important molecular markers for genetic selection of reproductive traits in high-yielding dairy cattle

A data-driven method for total organic carbon prediction based on random forests

The total organic carbon (TOC) is an important parameter for shale gas reservoir exploration. Currently, predicting TOC using seismic elastic properties is challenging and of great uncertainty. The inverse relationship, which acts as a bridge between TOC and elastic properties, is required to be established correctly. Machine learning especially for Random Forests (RF) provides a new potential. The RF-based supervised method is limited in the prediction of TOC because it requires large amounts of feature variables and is very onerous and experience-dependent to derive effective feature variables from real seismic data. To address this issue, we propose to use the extended elastic impedance to automatically generate 222 extended elastic properties as the feature variables for RF predictor training. In addition, the synthetic minority oversampling technique is used to overcome the problem of RF training with imbalanced samples. With the help of variable importance measures, the feature variables that are important for TOC prediction can be preferentially selected and the redundancy of the input data can be reduced. The RF predictor is finally trained well for TOC prediction. The method is applied to a real dataset acquired over a shale gas study area located in southwest China. Examples illustrate the role of extended variables on improving TOC prediction and increasing the generalization of RF in prediction of other petrophysical properties