Flow Patterns and Pore Structure Effects on Residual Oil during Water and CO₂ Flooding: In Situ CT Scanning

Carbon dioxide (CO2) enhanced oil recovery (EOR) is an important technology to achieve carbon neutrality by sequestering CO2 underground while simultaneously recovering crude oil. Reservoir pore structure is a key factor influencing CO2 EOR. In this study, we utilized advanced online in situ CT scanning and digital rock techniques to obtain, for the first time, evolution profiles of the finger area during water flooding and CO2 flooding processes, quantitatively assessing the differences in fluid patterns. Additionally, we first introduced an innovative approach using advanced machine learning techniques, especially XGBoost and SHAP, to construct a predictive model of the relative change of oil phase occupancy (RCPOC) based on pore structure parameters and evaluated the importance of each pore structure parameter. Importantly, our results revealed that CO2 can significantly increase the sweep efficiency area while substantially reducing residual oil saturation, in stark contrast to the relatively uniform water front observed during water flooding. Furthermore, we elucidated the critical role of capillary forces, demonstrating that water flooding primarily extracts trapped oil from small pores, while CO2 flooding effectively extracts oil from larger pores. During CO2 flooding, there is a positive correlation between coordination number, mean throat radius (MeanTR), and mean throat length (MeanTL) and the change in oil occupancy, whereas their influence during water flooding is limited. In summary, this study contributes to the understanding of flow patterns and pore structure effects on residual oil during water flooding and the CO2 flooding processes. It also provides a novel approach based on pore structure parameters to predict RCPOC and assess the importance of influencing factors, thereby expanding our research perspective on this issue

Детство последней вьюгой улетело

Детство последней вьюгой улетело, / И весной, как-то майским днем, / Юность на меня мундир одела, / Обхватила талию ремнем

MCAD mRNA in C, R, H, R+H groups of the three geno-type mice.

<p>* significantly different from the control group, p<0.05; ** p<0.01. # significantly different from the same group in WT, p<0.01; ## p<0.01.</p

Image7_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p

Long-Term-Stable Near-Infrared Polymer Dots with Ultrasmall Size and Narrow-Band Emission for Imaging Tumor Vasculature <i>in Vivo</i>

Fluorescent nanoprobes have become one of the most promising classes of materials for cancer imaging. However, there remain many unresolved issues with respect to the understanding of their long-term colloidal stability and photostability in both biological systems and the environment. In this study, we report long-term-stable near-infrared (NIR) polymer dots for <i>in vivo</i> tumor vasculature imaging. NIR-emitting polymer dots were prepared by encapsulating an NIR dye, silicon 2,3-naphthalo­cyanine bis­(trihexyl­silyloxide) (NIR775), into a matrix of polymer dots, poly­[2-methoxy-5-(2-ethyl­hexyloxy)-1,4-phenylene­vinylene] (MEH-PPV), using a nanoscale precipitation method. The prepared NIR polymer dots were sub-5 nm in diameter, exhibited narrow-band NIR emission at 778 nm with a full width at half-maximum of 20 nm, and displayed a large Stokes shift (>300 nm) between the excitation and emission maxima. In addition, no significant uptake of the prepared NIR polymer dots by either human glioblastoma U87MG cells or human non-small cell lung carcinoma H1299 cells was detected. Moreover, these NIR polymer dots showed long-term colloidal stability and photostability in water at 4 °C for at least 9 months, and were able to image vasculature of xeno­grafted U87MG tumors in living mice after intra­venous injection. These results thus open new opportunities for the development of whole-body imaging of mice based on NIR polymer dots as fluorescent nanoprobes

Image4_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p

Image1_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p

Image5_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p

Indigenous species barcode database improves the identification of zooplankton

<div><p>Incompleteness and inaccuracy of DNA barcode databases is considered an important hindrance to the use of metabarcoding in biodiversity analysis of zooplankton at the species-level. Species barcoding by Sanger sequencing is inefficient for organisms with small body sizes, such as zooplankton. Here mitochondrial <i>cytochrome c oxidase I</i> (<i>COI</i>) fragment barcodes from 910 freshwater zooplankton specimens (87 morphospecies) were recovered by a high-throughput sequencing platform, Ion Torrent PGM. Intraspecific divergence of most zooplanktons was < 5%, except <i>Branchionus leydign</i> (Rotifer, 14.3%), <i>Trichocerca elongate</i> (Rotifer, 11.5%), <i>Lecane bulla</i> (Rotifer, 15.9%), <i>Synchaeta oblonga</i> (Rotifer, 5.95%) and <i>Schmackeria forbesi</i> (Copepod, 6.5%). Metabarcoding data of 28 environmental samples from Lake Tai were annotated by both an indigenous database and NCBI Genbank database. The indigenous database improved the taxonomic assignment of metabarcoding of zooplankton. Most zooplankton (81%) with barcode sequences in the indigenous database were identified by metabarcoding monitoring. Furthermore, the frequency and distribution of zooplankton were also consistent between metabarcoding and morphology identification. Overall, the indigenous database improved the taxonomic assignment of zooplankton.</p></div