Sterile inflammation of peritoneal membrane caused by peritoneal dialysis: focus on the communication between immune cells and peritoneal stroma.

Peritoneal dialysis is a widely used method for treating kidney failure. However, over time, the peritoneal structure and function can deteriorate, leading to the failure of this therapy. This deterioration is primarily caused by infectious and sterile inflammation. Sterile inflammation, which is inflammation without infection, is particularly concerning as it can be subtle and often goes unnoticed. The onset of sterile inflammation involves various pathological processes. Peritoneal cells detect signals that promote inflammation and release substances that attract immune cells from the bloodstream. These immune cells contribute to the initiation and escalation of the inflammatory response. The existing literature extensively covers the involvement of different cell types in the sterile inflammation, including mesothelial cells, fibroblasts, endothelial cells, and adipocytes, as well as immune cells such as macrophages, lymphocytes, and mast cells. These cells work together to promote the occurrence and progression of sterile inflammation, although the exact mechanisms are not fully understood. This review aims to provide a comprehensive overview of the signals from both stromal cells and components of immune system, as well as the reciprocal interactions between cellular components, during the initiation of sterile inflammation. By understanding the cellular and molecular mechanisms underlying sterile inflammation, we may potentially develop therapeutic interventions to counteract peritoneal membrane damage and restore normal function.

Signal separation of simultaneous dual-tracer PET imaging based on global spatial information and channel attention.

BACKGROUND: Simultaneous dual-tracer positron emission tomography (PET) imaging efficiently provides more complete information for disease diagnosis. The signal separation has long been a challenge of dual-tracer PET imaging. To predict the single-tracer images, we proposed a separation network based on global spatial information and channel attention, and connected it to FBP-Net to form the FBPnet-Sep model. RESULTS: Experiments using simulated dynamic PET data were conducted to: (1) compare the proposed FBPnet-Sep model to Sep-FBPnet model and currently existing Multi-task CNN, (2) verify the effectiveness of modules incorporated in FBPnet-Sep model, (3) investigate the generalization of FBPnet-Sep model to low-dose data, and (4) investigate the application of FBPnet-Sep model to multiple tracer combinations with decay corrections. Compared to the Sep-FBPnet model and Multi-task CNN, the FBPnet-Sep model reconstructed single-tracer images with higher structural similarity, peak signal-to-noise ratio and lower mean squared error, and reconstructed time-activity curves with lower bias and variation in most regions. Excluding the Inception or channel attention module resulted in degraded image qualities. The FBPnet-Sep model showed acceptable performance when applied to low-dose data. Additionally, it could deal with multiple tracer combinations. The qualities of predicted images, as well as the accuracy of derived time-activity curves and macro-parameters were slightly improved by incorporating a decay correction module. CONCLUSIONS: The proposed FBPnet-Sep model was considered a potential method for the reconstruction and signal separation of simultaneous dual-tracer PET imaging.

Transformer-CNN hybrid network for improving PET time of flight prediction.

Objective.In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-to-noise ratio of images. Precision in TOF is measured by full width at half maximum (FWHM) and the offset from ground truth, referred to as coincidence time resolution (CTR) and bias.Approach.This study proposes a network combining transformer and convolutional neural network (CNN) to utilize TOF information from detector waveforms, using event waveform pairs as inputs. This approach integrates the global self-attention mechanism of Transformer, which focuses on temporal relationships, with the local receptive field of CNN. The combination of global and local information allows the network to assign greater weight to the rising edges of waveforms, thereby extracting valuable temporal information for precise TOF predictions. Experiments were conducted using lutetium yttrium oxyorthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors. The network was trained and tested using the waveform datasets after cropping.Main results.Compared to the constant fraction discriminator (CFD), CNN, CNN with attention, long short-term memory (LSTM) and Transformer, our network achieved an average CTR of 189 ps, reducing it by 82 ps (more than 30%), 13 ps (6.4%), 12 ps (6.0%), 16 ps (7.8%) and 9 ps (4.6%), respectively. Additionally, a reduction of 10.3, 8.7, 6.7 and 4 ps in average bias was achieved compared to CNN, CNN with attention, LSTM and Transformer.Significance.This work demonstrates the potential of applying the Transformer for PET TOF estimation using real experimental data. Through the integration of both CNN and Transformer with local and global attention, it achieves optimal performance, thereby presenting a novel direction for future research in this field.

Three-dimensional spatio-angular fluorescence microscopy with a polarized dual-view inverted selective-plane illumination microscope (pol-diSPIM).

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the three-dimensional orientations and diffraction-limited positions of ensembles of fluorescent dipoles that label biological structures, and we share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model our samples, their excitation, and their detection using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labelled giant unilamellar vesicles, fast-scarlet-labelled cellulose in xylem cells, and phalloidin-labelled actin in U2OS cells. Additionally, we observe phalloidin-labelled actin in mouse fibroblasts grown on grids of labelled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

Research Progress of Drug Delivery Systems Targeting the Kidneys.

Chronic kidney disease (CKD) affects more than 10% of the global population, and its incidence is increasing, partially due to an increase in the prevalence of disease risk factors. Acute kidney injury (AKI) is an independent risk factor for CKD and end-stage renal disease (ESRD). The pathogenic mechanisms of CKD provide several potential targets for its treatment. However, due to off-target effects, conventional drugs for CKD typically require high doses to achieve adequate therapeutic effects, leading to long-term organ toxicity. Therefore, ideal treatments that completely cure the different types of kidney disease are rarely available. Several approaches for the drug targeting of the kidneys have been explored in drug delivery system research. Nanotechnology-based drug delivery systems have multiple merits, including good biocompatibility, suitable degradability, the ability to target lesion sites, and fewer non-specific systemic effects. In this review, the development, potential, and limitations of low-molecular-weight protein-lysozymes, polymer nanomaterials, and lipid-based nanocarriers as drug delivery platforms for treating AKI and CKD are summarized.

Dipeptidyl peptidase 4 inhibitors vs. metformin for new-onset dementia: a propensity score-matched cohort study.

BACKGROUND: Hypoglycemic pharmacotherapy interventions for alleviating the risk of dementia remains controversial, particularly about dipeptidyl peptidase 4 (DPP4) inhibitors versus metformin. Our objective was to investigate whether the initiation of DPP4 inhibitors, as opposed to metformin, was linked to a reduced risk of dementia. METHODS: We included individuals with type 2 diabetes over 40 years old who were new users of DPP4 inhibitors or metformin in the Chinese Renal Disease Data System (CRDS) database between 2009 and 2020. The study employed Kaplan-Meier and Cox regression for survival analysis and the Fine and Gray model for the competing risk of death. RESULTS: Following a 1:1 propensity score matching, the analysis included 3626 DPP4 inhibitor new users and an equal number of metformin new users. After adjusting for potential confounders, the utilization of DPP4 inhibitors was associated with a decreased risk of all-cause dementia compared to metformin (hazard ratio (HR) 0.63, 95% confidence interval (CI) 0.45-0.89). Subgroup analysis revealed that the utilization of DPP4 inhibitors was associated with a reduced incidence of dementia in individuals who initiated drug therapy at the age of 60 years or older (HR 0.69, 95% CI 0.48-0.98), those without baseline macrovascular complications (HR 0.62, 95% CI 0.41-0.96), and those without baseline microvascular complications (HR 0.67, 95% CI 0.47-0.98). CONCLUSION: In this real-world study, we found that DPP4 inhibitors presented an association with a lower risk of dementia in individuals with type 2 diabetes than metformin, particularly in older people and those without diabetes-related comorbidities.

Frequency-Enhanced Geometric-Constrained Reconstruction for Localizing Myocardial Infarction in 12-Lead Electrocardiograms.

Determining the location of myocardial infarction is crucial for clinical management and therapeutic stratagem. However, existing diagnostic tools either sacrifice ease of use or are limited by their spatial resolution. Addressing this, we aim to refine myocardial infarction localization via surface potential reconstruction of the ventricles in 12-lead electrocardiograms (ECG). A notable obstacle is the ill-posed nature of such reconstructions. To overcome this, we introduce the frequency-enhanced geometric-constrained iterative network (FGIN). FGIN begins by mining the latent features from ECG data across both time and frequency domains. Subsequently, it increases the data dimensionality of ECG and captures intricate features using convolutional layers. Finally, FGIN incorporates ventricular geometry as a constraint on surface potential distribution. It allocates variable weights to distinct edges. Experimental validation of FGIN confirms its efficacy over synthetic and clinical datasets. On the synthetic dataset, FGIN outperforms seven existing reconstruction methods, attaining the highest Pearson Correlation Coefficient of 0.8624, the lowest Root Mean Square Error of 0.1548, and the highest Structural Similarity Index Measure of 0.7988. On the clinical public dataset (2007 PhysioNet/Computers in Cardiology Challenge), FGIN achieves better localization results than other approaches, according to the clinical standard 17-segment model, achieving an average Segment Overlap of 87.2%. Clinical trials on 50 patients demonstrate FGIN's effectiveness, showing an average accuracy of 91.6% and an average Segment Overlap of 88.2%.

Micro-Electro-Mechanical Systems Microphones: A Brief Review Emphasizing Recent Advances in Audible Spectrum Applications.

The MEMS microphone is a representative device among the MEMS family, which has attracted substantial research interest, and those tailored for human voice have earned distinct success in commercialization. Although sustained development persists, challenges such as residual stress, environmental noise, and structural innovation are posed. To collect and summarize the recent advances in this subject, this paper presents a concise review concerning the transduction mechanism, diverse mechanical structure topologies, and effective methods of noise reduction for high-performance MEMS microphones with a dynamic range akin to the audible spectrum, aiming to provide a comprehensive and adequate analysis of this scope.

Mitochondrial metabolism and targeted treatment strategies in ischemic-induced acute kidney injury.

Ischemia-reperfusion injury (IRI) is a common cause of acute kidney injury (AKI). The kidney is susceptible to IRI under several clinical conditions, including hypotension, sepsis, and surgical procedures, such as partial nephrectomy and kidney transplantation. Extensive research has been conducted on the mechanism and intervention strategies of renal IRI in past decades; however, the complex pathophysiology of IRI-induced AKI (IRI-AKI) is not fully understood, and there remains a lack of effective treatments for AKI. Renal IRI involves several processes, including reactive oxygen species (ROS) production, inflammation, and apoptosis. Mitochondria, the centers of energy metabolism, are increasingly recognized as substantial contributors to the early phases of IRI. Multiple mitochondrial lesions have been observed in the renal tubular epithelial cells (TECs) of IRI-AKI mice, and damaged or dysfunctional mitochondria are toxic to the cells because they produce ROS and release cell death factors, resulting in TEC apoptosis. In this review, we summarize the recent advances in the mitochondrial pathology in ischemic AKI and highlight promising therapeutic approaches targeting mitochondrial dysfunction to prevent or treat human ischemic AKI.

Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging.

Objective. The primary aim of our study is to advance our understanding and diagnosis of cardiac diseases. We focus on the reconstruction of myocardial transmembrane potential (TMP) from body surface potential mapping.Approach. We introduce a novel methodology for the reconstruction of the dynamic distribution of TMP. This is achieved through the integration of convolutional neural networks with conventional optimization algorithms. Specifically, we utilize the subject-specific transfer matrix to describe the dynamic changes in TMP distribution and ECG observations at the body surface. To estimate the TMP distribution, we employ LNFISTA-Net, a learnable non-local regularized iterative shrinkage-thresholding network. The coupled estimation processes are iteratively repeated until convergence.Main results. Our experiments demonstrate the capabilities and benefits of this strategy. The results highlight the effectiveness of our approach in accurately estimating the TMP distribution, thereby providing a reliable method for the diagnosis of cardiac diseases.Significance. Our approach demonstrates promising results, highlighting its potential utility for a range of applications in the medical field. By providing a more accurate and dynamic reconstruction of TMP, our methodology could significantly improve the diagnosis and treatment of cardiac diseases, thereby contributing to advancements in healthcare.

Joint spatio-temporal features constrained self-supervised electrocardiogram representation learning.

The electrocardiogram (ECG) measurements with clinical diagnostic labels are intrinsically limited. We propose a generative learning based self-supervised method for general ECG representations applicable to various downstream tasks, thus achieving the goal of reducing the dependence on labeled data. However, existing self-supervised methods either fail to provide satisfactory ECG representations or require too much effort to curate a large amount of expert-annotated datasets. We propose a spatio-temporal joint detection based self-supervised method with little or no human supervision to label massive datasets. Considering the spatio-temporal characteristics of ECG signals, we dynamically randomly mask the original signal (temporal detection) and disrupt the order of leads (spatial detection) to complete the learning through reconstructing the original signal and predicting the lead numbers. To validate the effectiveness of the proposed method, we use several publicly available ECG databases as well as a private ECG data of ventricular tachycardia to pre-train our model. We use diagnostic classification of 27 arrhythmia types and localization of ventricular tachycardia origin sites as two downstream tasks, respectively. The results show that learning ECG representations with this method is effective. This effort demonstrates the feasibility of learning representations from ECG data by self-supervised learning. Our self-supervised method uses only 60% of the labeled data used by the supervised method to achieve the same performance. Using the same amount of data, our self-supervised approach shows 1.3% and 8.6% improvement in classification and localization accuracy compared to the model with random initialization on two types of downstream tasks, respectively.

From the regulatory mechanism of TFEB to its therapeutic implications.

Transcription factor EB (TFEB), known as a major transcriptional regulator of the autophagy-lysosomal pathway, regulates target gene expression by binding to coordinated lysosomal expression and regulation (CLEAR) elements. TFEB are regulated by multiple links, such as transcriptional regulation, post-transcriptional regulation, translational-level regulation, post-translational modification (PTM), and nuclear competitive regulation. Targeted regulation of TFEB has been victoriously used as a treatment strategy in several disease models such as ischemic injury, lysosomal storage disorders (LSDs), cancer, metabolic disorders, neurodegenerative diseases, and inflammation. In this review, we aimed to elucidate the regulatory mechanism of TFEB and its applications in several disease models by targeting the regulation of TFEB as a treatment strategy.

Anatomically Guided PET Image Reconstruction Using Conditional Weakly-Supervised Multi-Task Learning Integrating Self-Attention.

To address the lack of high-quality training labels in positron emission tomography (PET) imaging, weakly-supervised reconstruction methods that generate network-based mappings between prior images and noisy targets have been developed. However, the learned model has an intrinsic variance proportional to the average variance of the target image. To suppress noise and improve the accuracy and generalizability of the learned model, we propose a conditional weakly-supervised multi-task learning (MTL) strategy, in which an auxiliary task is introduced serving as an anatomical regularizer for the PET reconstruction main task. In the proposed MTL approach, we devise a novel multi-channel self-attention (MCSA) module that helps learn an optimal combination of shared and task-specific features by capturing both local and global channel-spatial dependencies. The proposed reconstruction method was evaluated on NEMA phantom PET datasets acquired at different positions in a PET/CT scanner and 26 clinical whole-body PET datasets. The phantom results demonstrate that our method outperforms state-of-the-art learning-free and weakly-supervised approaches obtaining the best noise/contrast tradeoff with a significant noise reduction of approximately 50.0% relative to the maximum likelihood (ML) reconstruction. The patient study results demonstrate that our method achieves the largest noise reductions of 67.3% and 35.5% in the liver and lung, respectively, as well as consistently small biases in 8 tumors with various volumes and intensities. In addition, network visualization reveals that adding the auxiliary task introduces more anatomical information into PET reconstruction than adding only the anatomical loss, and the developed MCSA can abstract features and retain PET image details.

Functional traits and health implications of the global household drinking-water microbiome retrieved using an integrative genome-centric approach.

The biological safety of drinking water plays a crucial role in public health protection. However, research on the drinking water microbiome remains in its infancy, especially little is known about the potentially pathogenic bacteria in and functional characteristics of the microbiome in household tap water that people are directly exposed to. In this study, we used a genomic-centric approach to construct a genetic catalogue of the drinking water microbiome by analysing 116 metagenomic datasets of household tap water worldwide, spanning nine countries/regions on five continents. We reconstructed 859 high-quality metagenome-assembled genomes (MAGs) spanning 27 bacterial and 2 archaeal phyla, and found that the core MAGs belonging to the phylum Proteobacteria encoded the highest metabolic functional diversity of the 33 key complete metabolic modules. In particular, we found that two core MAGs of Brevibacillus and Methylomona encoded genes for methane metabolism, which may support the growth of heterotrophic organisms observed in the oligotrophic ecosystem. Four MAGs of complete ammonia oxidation (comammox) Nitrospira were identified and functional metabolic analysis suggested these may enable mixotrophic growth and encode genes for reactive oxygen stress defence and arsenite reduction that could aid survival in the environment of oligotrophic drinking water systems. Four MAGs were annotated as potentially pathogenic bacteria (PPB) and thus represented a possible public health concern. They belonged to the genera Acinetobacter (n = 3) and Mycobacterium (n = 1), with a total relative abundance of 1.06 % in all samples. The genomes of PPB A. junii and A. ursingii were discovered to contain antibiotic resistance genes and mobile genetic elements that could contribute to antimicrobial dissemination in drinking water. Further network analysis suggested that symbiotic microbes which support the growth of pathogenic bacteria can be targets for future surveillance and removal.

ZmC2H2-149 negatively regulates drought tolerance by repressing ZmHSD1 in maize.

Drought is a major abiotic stress that limits maize production worldwide. Therefore, it is of great importance to improve drought tolerance in crop plants for sustainable agriculture. In this study, we examined the roles of Cys2 /His2 zinc-finger-proteins (C2H2-ZFPs) in maize's drought tolerance as C2H2-ZFPs have been implicated for plant stress tolerance. By subjecting 150 Ac/Ds mutant lines to drought stress, we successfully identified a Ds-insertion mutant, zmc2h2-149, which shows increased tolerance to drought stress. Overexpression of ZmC2H2-149 in maize led to a decrease in both drought tolerance and crop yield. DAP-Seq, RNA-Seq, Y1H and LUC assays additionally showed that ZmC2H2-149 directly suppresses the expression of a positive drought tolerance regulator, ZmHSD1 (hydroxysteroid dehydrogenase 1). Consistently, the zmhsd1 mutants exhibited decreased drought tolerance and grain yield under water deficit conditions compared to their respective wild-type plants. Our findings thus demonstrated that ZmC2H2-149 can regulate ZmHSD1 for drought stress tolerance in maize, offering valuable theoretical and genetic resources for maize breeding programmes that aim for improving drought tolerance.

Incidence, risk factors and outcome of postoperative acute kidney injury in China.

BACKGROUND: Postoperative acute kidney injury (AKI) is a common condition after surgery, however, the available data about nationwide epidemiology of postoperative AKI in China from large and high-quality studies are limited. This study aimed to determine the incidence, risk factors and outcomes of postoperative AKI among patients undergoing surgery in China. METHODS: This was a large, multicentre, retrospective study performed in 16 tertiary medical centres in China. Adult patients (≥18 years of age) who underwent surgical procedures from 1 January 2013 to 31 December 2019 were included. Postoperative AKI was defined by the Kidney Disease: Improving Global Outcomes creatinine criteria. The associations of AKI and in-hospital outcomes were investigated using logistic regression models adjusted for potential confounders. RESULTS: Among 520 707 patients included in our study, 25 830 (5.0%) patients developed postoperative AKI. The incidence of postoperative AKI varied by surgery type, which was highest in cardiac (34.6%), urologic (8.7%) and general (4.2%) surgeries. A total of 89.2% of postoperative AKI cases were detected in the first 2 postoperative days. However, only 584 (2.3%) patients with postoperative AKI were diagnosed with AKI on discharge. Risk factors for postoperative AKI included older age, male sex, lower baseline kidney function, pre-surgery hospital stay ≤3 days or >7 days, hypertension, diabetes mellitus and use of proton pump inhibitors or diuretics. The risk of in-hospital death increased with the stage of AKI. In addition, patients with postoperative AKI had longer lengths of hospital stay (12 versus 19 days) and were more likely to require intensive care unit care (13.1% versus 45.0%) and renal replacement therapy (0.4% versus 7.7%). CONCLUSIONS: Postoperative AKI was common across surgery type in China, particularly for patients undergoing cardiac surgery. Implementation and evaluation of an alarm system is important for the battle against postoperative AKI.

Risk of Hospital-Acquired Acute Kidney Injury among Adult Opioid Analgesic Users: A Multicenter Real-World Data Analysis.

Introduction: Comprehensive data on the risk of hospital-acquired (HA) acute kidney injury (AKI) among adult users of opioid analgesics are lacking. This study aimed to systematically compare the risk of HA-AKI among the users of various opioid analgesics. Methods: This multicenter, retrospective real-world study analyzed 255,265 adult hospitalized patients who received at least one prescription of opioid analgesic during the first 30 days of hospitalization. The primary outcome was the time from the first opioid analgesic prescription to HA-AKI occurrence. 12 subtypes of opioid analgesics were analyzed, including 9 for treating moderate-to-severe pain and 3 for mild-to-moderate pain. We examined the association between the exposure to each subtype of opioid analgesic and the risk of HA-AKI using Cox proportional hazards models, using the most commonly used opioid analgesic as the reference group. Results: As compared to dezocine, the most commonly used opioid analgesic for treating moderate-to-severe pain, exposure to morphine, but not the other 7 types of opioid analgesics, was associated with a significantly increased risk of HA-AKI (adjusted hazard ratio: 1.56, 95% confidence interval: 1.40-1.78). The association was consistent in stratified analyses and in a propensity-matched cohort. There were no significant differences in the risk of HA-AKI among the opioid analgesic users with mild-to-moderate pain after adjusting for confounders. Conclusion: The use of morphine was associated with an increased risk of HA-AKI in adult patients with moderate-to-severe pain. Opioid analgesics other than morphine should be chosen preferentially in adult patients with high risk of HA-AKI when treating moderate-to-severe pain.

Prior information-guided reconstruction network for positron emission tomography images.

Background: Deep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction. Methods: We developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provided in the generator for prior information (PI) to guide the training phase. The PI can be reconstructed images obtained via conventional methods, nuclear medical images from other modalities, attenuation correction maps from time-of-flight-PET (TOF-PET) data, or any other physical parameters. For this study, the reconstructed images generated by filtered back projection (FBP) were chosen as the input of the additional channel. To improve the image quality, a 2-scale discriminator was adopted which can focus on both the coarse and fine field of the reconstruction images. Experiments were carried out on both a simulation dataset and a real Sprague Dawley (SD) rat dataset. Results: Two classic deep learning-based reconstruction networks, including U-Net and Deep-PET, were compared in our study. Compared with these two methods, our method could provide much higher quality PET image reconstruction in the study of the simulation dataset. The peak signal-to-noise ratio (PSNR) value reached 31.8498, and the structure similarity index measure (SSIM) value reached 0.9754. The real study on SD rats indicated that the proposed network also has strong generalization ability. Conclusions: The flexible PIGRN based on cGAN for PET images combines both raw data and PI. The results of comparison experiments and a generalization experiment based on simulation and SD rat datasets demonstrated that the proposed PIGRN has the ability to improve image quality and has strong generalization ability.

Laboratory Evaluation on the Water Flooding Characteristics in Bottom Water Reservoir Containing Interbeds.

Sandstone reservoirs with bottom water drive are widely distributed all over the world, which are characterized by the complex process of oil and water storage and transmission. At present, the research on the water flooding process and oil-water evolution characteristics in bottom water reservoirs containing interbeds needs to be strengthened. In this study, water flooding experiments with different placements of the interbeds were conducted using a two-dimensional (2D) vertical model. The results demonstrated that the interbeds make the bottom water flow upward more evenly, resulting in decreased incursion speed, increased displacement area, and better displacement effect. Moreover, compared with the tilted interbed model, the horizontal model has a 6% higher oil recovery rate, exhibiting a better oil displacement effect. The results presented herein will provide important guidance on water control in bottom-aquifer oil reservoirs containing interbeds and will promote unconventional petroleum resources recovery.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations to show that applying the trained 'de-aberration' networks outperforms alternative methods, and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.