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Objective: Individual differences in pain sensitivity play a role in the development and maintenance of chronic pain. Previous studies have shown more interpretation and memory biases toward pain-related information in chronic pain patients than in healthy controls. Yet, it remains unclear whether interpretation and/or memory biases in healthy individuals vary with the level of pain sensitivity. Methods: Two groups of healthy participants with high or low pain sensitivity (HPS and LPS) were recruited based on their scores on a pain-sensitivity questionnaire, which categorization was verified by an experimental pain-sensitivity test. Interpretation bias was indirectly assessed by an incidental learning task that measured the tendency to interpret ambiguous expressions as either painful or happy, and memory bias was assessed by an image-recognition task that used images depicting painful or neutral situations. Results: Participants in the HPS group tended to interpret ambiguous expressions as painful, indicative of interpretation bias toward pain, which was not observed in the LPS group. Participants overall generally recognized pain-related images more accurately and quickly than neutral images, indicative of memory bias toward pain, which did not differ between the two groups. Conclusion: Individual variation in pain sensitivity is related to an interpretation bias toward pain but not to a memory bias. The observed link between pain-sensitivity profiles and interpretation bias in healthy individuals suggests the potential role of interpretation bias in chronic pain development and maintenance. Intervention to reduce interpretation bias toward pain might be helpful in the prevention and treatment of chronic pain., Author(s): Zekun Guo [sup.1] , Wenyun Zhang [sup.1] , Xiaohan Lyu [sup.1] , Qian Ye [sup.1] , Weiwei Peng [sup.1] Author Affiliations: (1) https://ror.org/01vy4gh70, grid.263488.3, 0000 0001 0472 9649, School [...]

The gene dosage at the imprinted Dlk1-Dio3 locus is critical for cell growth and development. A relatively high gene expression within the Dlk1-Dio3 region, especially the active expression of Gtl2, has been identified as the only reliable marker for cell pluripotency. The DNA methylation state of the IG-DNA methylated regions (DMR), which is located upstream of the Gtl2 gene, dominantly contributes to the control of gene expression in the Dlk1-Dio3 locus. However, the precise mechanism underlying the regulation of DNA methylation in the IG-DMR remains largely unknown. Here, we use the F9 embryonal carcinoma cell line, a low pluripotent cell model, to identify the mechanism responsible for DNA methylation in the IG-DMR, and find that the interaction of PGC7 with UHRF1 is involved in maintaining DNA methylation and inducing DNA hypermethylation in the IG-DMR region. PGC7 and UHRF1 cooperatively bind in the IG-DMR to regulate the methylation of DNA and histones in this imprinted region. PGC7 promotes the recruitment of DNMT1 by UHRF1 to maintain DNA methylation in the IG-DMR locus. The interaction between PGC7 and UHRF1 strengthens their binding to H3K9me3 and leads to further enrichment of H3K9me3 in the IG-DMR by recruiting the specific histone methyltransferase SETDB1. Consequently, the abundance of H3K9me3 promotes DNMT3A to bind to the IG-DMR and increases DNA methylation level in this region. In summary, we propose a new mechanism of DNA methylation regulation in the IG-DMR locus and provide further insight into the understanding of the difference in Gtl2 expression levels between high and low pluripotent cells.

With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for "sweet spots" prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R2 values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment. [ABSTRACT FROM AUTHOR]