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The injected fluids in secondary processes supplement the natural energy present in the reservoir to displace oil. The recovery efficiency mainly depends on the mechanism of pressure maintenance. However, the injected fluids in tertiary or enhanced oil recovery (EOR) processes interact with the reservoir rock/oil system. Thus, EOR techniques are receiving substantial attention worldwide as the available oil resources are declining. However, some challenges, such as low sweep efficiency, high costs and potential formation damage, still hinder the further application of these EOR technologies. Current studies on nanoparticles are seen as potential solutions to most of the challenges associated with these traditional EOR techniques. This paper provides an overview of the latest studies about the use of nanoparticles to enhance oil recovery and paves the way for researchers who are interested in the integration of these progresses. The first part of this paper addresses studies about the major EOR mechanisms of nanoparticles used in the forms of nanofluids, nanoemulsions and nanocatalysts, including disjoining pressure, viscosity increase of injection fluids, preventing asphaltene precipitation, wettability alteration and interfacial tension reduction. This part is followed by a review of the most important research regarding various novel nano-assisted EOR methods where nanoparticles are used to target various existing thermal, chemical and gas methods. Finally, this review identifies the challenges and opportunities for future study regarding application of nanoparticles in EOR processes.

Physiological and molecular processes initiated during implantation for pregnancy success are complex but highly organized. This review primarily highlights adverse ripple effects arising from defects during the peri-implantation period that perpetuate throughout pregnancy. These defects are reflected in aberrations in embryo spacing, decidualization, placentation and intrauterine embryonic growth, manifesting in preeclampsia, miscarriages and/or preterm birth. Understanding molecular signaling networks that coordinate strategies for successful implantation and decidualization may lead to approaches to improve the outcome of natural pregnancy and pregnancy conceived from in vitro fertilization.

Mass spectrometry imaging (MSI) enables label-free spatial mapping of hundreds of biomolecules in tissue sections. This capability provides valuable information on tissue heterogeneity that is difficult to obtain using population-averaged assays. Despite substantial developments in both instrumentation and methodology, MSI of tissue samples at single-cell resolution remains challenging. Herein, we describe a protocol for robust imaging of tissue sections with a high (better than 10-μm) spatial resolution using nanospray desorption electrospray ionization (nano-DESI) mass spectrometry, an ambient ionization technique that does not require sample pretreatment before analysis. In this protocol, mouse uterine tissue is used as a model system to illustrate both the workflow and data obtained in these experiments. We provide a detailed description of the nano-DESI MSI platform, fabrication of the nano-DESI and shear force probes, shear force microscopy experiments, spectral acquisition, and data processing. A properly trained researcher (e.g., technician, graduate student, or postdoc) can complete all the steps from probe fabrication to data acquisition and processing within a single day. We also describe a new strategy for acquiring both positive- and negative-mode imaging data in the same experiment. This is achieved by alternating between positive and negative data acquisition modes during consecutive line scans. Using our imaging approach, hundreds of high-quality ion images were obtained from a single uterine section. This protocol enables sensitive and quantitative imaging of lipids and metabolites in heterogeneous tissue sections with high spatial resolution, which is critical to understanding biochemical processes occurring in biological tissues. This protocol describes how to achieve high spatial resolution imaging of biological tissues using nanospray desorption electrospray ionization mass spectrometry

Impairment in gut epithelial integrity and barrier function is associated with many diseases. The homeostasis of intestinal barrier is based on a delicate regulation of epithelial proliferation and differentiation. AMP-activated protein kinase (AMPK) is a master regulator of energy metabolism, and cellular metabolites are intrinsically involved in epigenetic modifications governing cell differentiation. We aimed to evaluate the regulatory role of AMPK on intestinal epithelial development and barrier function. In this study, AMPK activator (AICAR) improved the barrier function of Caco-2 cells as indicated by increased transepithelial electrical resistance and reduced paracellular FITC-dextran permeability; consistently, AICAR enhanced epithelial differentiation and tight junction formation. Transfection of Caco-2 cells with AMPK WT plasmid, which enhances AMPK activity, improved epithelial barrier function and epithelial differentiation, while K45R (AMPK dominant negative mutant) impaired; these changes were correlated with the expression of caudal type homeobox 2 (CDX2), the key transcription factor committing cells to intestinal epithelial lineage. CDX2 deficiency abolished intestinal differentiation promoted by AMPK activation. Mechanistically, AMPK inactivation was associated with polycomb repressive complex 2 regulated enrichment of H3K27me3, the inhibitory histone modification, and lysine-specific histone demethylase-1-mediated reduction of H3K4me3, a permissive histone modification. Those histone modifications provide a mechanistic link between AMPK and CDX2 expression. Consistently, epithelial AMPK knockout in vivo reduced CDX2 expression, impaired intestinal barrier function, integrity and ultrastructure of tight junction, and epithelial cell migration, promoted intestinal proliferation and exaggerated dextran sulfate sodium-induced colitis. In summary, AMPK enhances intestinal barrier function and epithelial differentiation via promoting CDX2 expression, which is partially mediated by altered histone modifications in the Cdx2 promoter.

Detecting defects on photovoltaic panels using electroluminescence images can significantly enhance the production quality of these panels. Nonetheless, in the process of defect detection, there often arise instances of missed detections and false alarms due to the close resemblance between embedded defect features and the intricate background information. To tackle this challenge, we propose an Adaptive Complementary Fusion (ACF) module designed to intelligently integrate spatial and channel information. This module is seamlessly integrated into YOLOv5 for detecting defects on photovoltaic panels, aiming primarily to enhance model detection performance, achieve model lightweighting, and accelerate detection speed. In order to validate the efficacy of the proposed module, we conducted experiments using a dataset comprising 4500 electroluminescence images of photovoltaic panels. Compared to the cutting-edge detection capability of YOLOv8, our YOLO-ACF method exhibits enhancements of 5.2, 0.8, and 2.3 percentage points in R, mAP50, and mAP50-95, respectively. In contrast to the lightest and fastest YOLOv5, YOLO-ACF achieves reductions of 12.9%, 12.4%, and 4.2% in parameters, weight, and time, respectively, while simultaneously boosting FPS by 5%. Through qualitative and quantitative comparisons with various alternative methods, we demonstrate that our YOLO-ACF strikes a good balance between detection performance, model complexity, and detection speed for defect detection on photovoltaic panels. Moreover, it demonstrates remarkable versatility across a spectrum of defect types.

Background Anxiety and depressive symptoms are common among patients with heart failure (HF). Physical limitations, lifestyle changes, and uncertainties related to HF can result in the development or exacerbating of anxiety and depressive symptoms. However, the central and bridge symptoms of anxiety and depressive symptoms network among patients with HF remain unclear. Network analysis is a statistical method that can discover and visualize complex relationships between multiple variables. This study aimed to establish a network of anxiety and depressive symptoms and identify the central and bridge symptoms in this network among patients with HF. Methods This study employed a cross-sectional study design and convenience sampling to recruit patients with HF. This study followed the Helsinki Declaration and was approved by the Research Ethics Committee of Hospital. The Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire (PHQ-9) were administered to evaluate anxiety and depressive symptoms among patients with HF, respectively. Network analysis of anxiety and depressive symptoms was performed using R. Results In the anxiety and depressive symptoms network, PHQ2 (feeling down, depressed, or hopeless), PHQ7 (inability to concentrate), and GAD4 (difficulty relaxing) were the most central symptoms. Anxiety and depressive symptoms were linked by PHQ2 (feeling down, depressed, or hopeless), GAD6 (becoming easily annoyed or impatient), GAD5 (unable to sit still because of anxiety), GAD7 (feeling afraid that something terrible is about to happen), and PHQ6 (feeling bad or like a failure, or disappointing oneself or family). Conclusions This study identified the central and bridge symptoms in a network of anxiety and depressive symptoms. Targeting these symptoms can contribute to interventions for patients with HF at risk of—or suffering from—anxiety and depressive symptoms, which can be effective in reducing the comorbidity of anxiety and depression.

Brain degeneration is commonly caused by some chronic diseases, such as Alzheimer's disease (AD) and diabetes mellitus (DM). The risk prediction of brain degeneration aims to forecast the situation of disease progression of patients in the near future based on their historical health records. It is beneficial for patients to make an accurate clinical diagnosis and early prevention of disease. Current risk predictions of brain degeneration mainly rely on single-modality medical data, such as Electronic Health Records (EHR) or magnetic resonance imaging (MRI). However, only leveraging EHR or MRI data for the pertinent and accurate prediction is insufficient because of single-modality information (e.g., pixel or volume information of image data or clinical context information of non-image data). Several deep learning-based methods have used multimodal data to predict the risks of specified diseases. However, most of them simply integrate different modalities in an early, intermediate, or late fusion structure and do not care about the intra-modal and intermodal dependencies. A lack of these dependencies would lead to sub-optimal prediction performance. Thus, we propose an encoder-decoder framework for better risk prediction of brain degeneration by using MRI and EHR. An encoder module is one of the key components and mainly focuses on feature extraction of input data. Specifically, we introduce an encoder module, which integrates intra-modal and inter-modal dependencies with the spatial-temporal attention and cross-attention mechanism. The corresponding decoder module is another key component and mainly parses the features from the encoder. In the decoder module, a disease-oriented module is used to extract the most relevant disease representation features. We take advantage of a multi-head attention module followed by a fully connected layer to produce the predicted results. As different types of AD and DM influence the nature and severity of brain degeneration, we evaluate the proposed method for three-class prediction of AD and three-class prediction of DM. Our results show that the proposed method with integrated MRI and EHR data achieves an accuracy of 0.859 and 0.899 for the risk prediction of AD and DM, respectively. The prediction performance is significantly better than the benchmarks, including MRI-only, EHR-only, and state-of-the-art multimodal fusion methods.

Landslides are among the most frequent and dangerous geological disasters worldwide, making accurate landslide susceptibility mapping (LSM) crucial for effective disaster prevention. This study introduces a novel LSM framework by integrating random forest (RF), support vector machine (SVM), and catastrophe theory (CT), and applies it to the contiguous impoverished areas of Liangshan, Sichuan. First, we selected 12 factors representing both internal environmental and external triggering conditions to assess landslide susceptibility. The frequency ratio method was used to assess the correlation between historical landslides and these factors. Second, CT was integrated into the RF- and SVM-based LSM models, resulting in two integrated models (RF-CT and SVM-CT) for generating LSM in the region. Finally, the receiver operating characteristic curve was used to evaluate and compare the accuracy of the methods. The results show that the RF-CT and SVM-CT frameworks performed well, with a 10% improvement in the success rate (0.899 for RF-CT and 0.873 for SVM-CT), and a 5% improvement in the prediction rate (0.783 for RF-CT and 0.775 for SVM-CT) compared with the individual RF and SVM models. These findings provide valuable insights for disaster prevention, poverty alleviation, and sustainable development in the study area.

Endometrial regeneration is essential for reproductive cycles and pregnancies, allowing the endometrium to undergo estrogen-driven repair, growth, and renewal after menstruation and parturition. Epithelial cells lining the uterine cavity undergo apoptosis during estrous cycles, and remnant cells can quickly restore this lining through a process known as re-epithelialization. It is presumed that adult stem/progenitor cells in the uterine stroma also contribute to re-epithelialization. However, the specific cell type(s) and the underlying mechanisms have not been determined. Herein, we use genetic lineage tracing assays in mice to identify Nestin + perivascular cells as active contributors to re-epithelialization. Notch signaling maintains Nestin + perivascular cells in a quiescent state, but these cells re-enter the cell cycle and differentiate into epithelial cells via estrogen-stimulated suppression of Notch signaling dependent on estrogen receptor alpha (ESR1). These findings demonstrate that perivascular cells support re-epithelialization and reveal a mechanism regulating the quiescence and activation of uterine perivascular cells. Uterine perivascular cells were identified as progenitors for endometrial re-epithelialization via mesenchymal-to-epithelial transition, whereby cellular quiescence and activation were mediated via Notch signaling and estrogen, respectively.

Epidemiological studies suggest that insulin resistance accelerates progression of age-based cognitive impairment, which neuroimaging has linked to brain glucose hypometabolism. As cellular inputs, ketones increase Gibbs free energy change for ATP by 27% compared to glucose. Here we test whether dietary changes are capable of modulating sustained functional communication between brain regions (network stability) by changing their predominant dietary fuel from glucose to ketones. We first established network stability as a biomarker for brain aging using two large-scale (n = 292, ages 20 to 85 y; n = 636, ages 18 to 88 y) 3 T functional MRI (fMRI) datasets. To determine whether diet can influence brain network stability, we additionally scanned 42 adults, age < 50 y, using ultrahigh-field (7 T) ultrafast (802 ms) fMRI optimized for single-participant-level detection sensitivity. One cohort was scanned under standard diet, overnight fasting, and ketogenic diet conditions. To isolate the impact of fuel type, an independent overnight fasted cohort was scanned before and after administration of a calorie-matched glucose and exogenous ketone ester (D-β-hydroxybutyrate) bolus. Across the life span, brain network destabilization correlated with decreased brain activity and cognitive acuity. Effects emerged at 47 y, with the most rapid degeneration occurring at 60 y. Networks were destabilized by glucose and stabilized by ketones, irrespective of whether ketosis was achieved with a ketogenic diet or exogenous ketone ester. Together, our results suggest that brain network destabilization may reflect early signs of hypometabolism, associated with dementia. Dietary interventions resulting in ketone utilization increase available energy and thus may show potential in protecting the aging brain.