Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model

Background Single cell sequencing technologies have advanced our understanding of kidney biology and disease but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth. Methods We optimized and validated a female bilateral ischemia reperfusion injury model. Using the 10X Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. Results An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded non-negative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we find that injured proximal tubule cells are characterized by increasing macrophage and lymphocyte interactions even at 6 weeks after injury, potentially reflecting the AKI to CKD transition. Conclusions In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created a data visualization web application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/; login: humphreyslab_visium password: irivisium).

Analysis of the Repair Time of Finishing Works Using a Probabilistic Approach for Efficient Residential Buildings Maintenance Strategies

In general, the long-term maintenance planning of residential buildings is performed based on uniform repair times. However, in fact, various factors, such as the quality and user patterns, affect the performance of residential building components in the Operation and Maintenance (O&M) phase. Hence, various residential building components are repaired at uncertain times, acting as a risk for the residential building maintenance plan. Therefore, an efficient maintenance plan should be established considering maintenance uncertainty. In this regard, this study aims to analyze the uncertainty of repair times for various finishing works in residential buildings based on a probabilistic methodology and outline the implications for the establishment of efficient maintenance strategies in these buildings. Hence, 47,344 repair data for 63 buildings in 12 public residential building complexes completed between 1991 and 2001 in the Republic of Korea were used for analysis. Before the analysis, a repair time matrix was constructed by classifying the finishing works in 25 types and setting service life times to 6–26 years. The repair time distribution for each finishing work was then derived. Results confirmed that basic repair time setting can be performed and various information for reasonable maintenance decision making regarding each finishing work can be provided through a probabilistic approach. The probabilistic approach can be used as a critical decision-making method because there is uncertainty associated with the repair time of each finishing work owing to the performance degradations of various finishing works due to complex causes. Although this study focused on repair time owing to data collection limitations, maintenance strategies with strategic flexibility can be established by developing probabilistic methods that simultaneously consider frequency and cost by securing additional high-quality cost data.

Topological Analysis of γH2AX and MRE11 Clusters Detected by Localization Microscopy during X-ray-Induced DNA Double-Strand Break Repair

DNA double-strand breaks (DSBs), known as the most severe damage in chromatin, were induced in breast cancer cells and normal skin fibroblasts by 2 Gy ionizing photon radiation. In response to DSB induction, phosphorylation of the histone variant H2AX to γH2AX was observed in the form of foci visualized by specific antibodies. By means of super-resolution single-molecule localization microscopy (SMLM), it has been recently shown in a first article about these data that these foci can be separated into clusters of about the same size (diameter ~400 nm). The number of clusters increased with the dose applied and decreased with the repair time. It has also been shown that during the repair period, antibody-labeled MRE11 clusters of about half of the γH2AX cluster diameter were formed inside several γH2AX clusters. MRE11 is part of the MRE11–RAD50–NBS1 (MRN) complex, which is known as a DNA strand resection and broken-end bridging component in homologous recombination repair (HRR) and alternative non-homologous end joining (a-NHEJ). This article is a follow-up of the former ones applying novel procedures of mathematics (topology) and similarity measurements on the data set: to obtain a measure for cluster shape and shape similarities, topological quantifications employing persistent homology were calculated and compared. In addition, based on our findings that γH2AX clusters associated with heterochromatin show a high degree of similarity independently of dose and repair time, these earlier published topological analyses and similarity calculations comparing repair foci within individual cells were extended by topological data averaging (2nd-generation heatmaps) over all cells analyzed at a given repair time point; thereby, the two dimensions (0 and 1) expressed by components and holes were studied separately. Finally, these mean value heatmaps were averaged, in addition. For γH2AX clusters, in both normal fibroblast and MCF-7 cancer cell lines, an increased similarity was found at early time points (up to 60 min) after irradiation for both components and holes of clusters. In contrast, for MRE11, the peak in similarity was found at later time points (2 h up to 48 h) after irradiation. In general, the normal fibroblasts showed quicker phosphorylation of H2AX and recruitment of MRE11 to γH2AX clusters compared to breast cancer cells and a shorter time interval of increased similarity for γH2AX clusters. γH2AX foci and randomly distributed MRE11 molecules naturally occurring in non-irradiated control cells did not show any significant topological similarity.

Reliability of the two-unit priority standby system revisited

This paper deals with reliability assessment of the repairable two-unit cold standby system when the first, main unit has the better performance level than the second one. Therefore, after its repair, the main unit is always switched into operation. The new Laplace transform representation for the system’s lifetime is obtained for arbitrary operation and repair time distributions of the units. For some particular cases, the Laplace transform of the system is shown to be rational, which enables the use of the matrix-exponential distributions for obtaining relevant reliability indices. The discrete setup of the model is also considered through the corresponding matrix-geometric distributions, which are the discrete analogs of the matrix-exponential distributions.

A Multi-Objective Mathematical Model for Level of Repair Analysis with Lead Times and Multi-Transportation Modes

In the event of failure of the product, level of repair analysis (LORA) is used to determine (1) whether the defective component should be discarded or repaired and (2) where this repair is made. In the literature, these repair operations are made with the aim of minimizing the total life cycle cost of the product. In this paper, we develop a multi-objective decision model that minimizes both the repair time (affected by lead times) and the repair costs. Our proposed model also considers the movement of the defective components to be performed by multiple transportation modes such as highway, railway, and airway. We use the epsilon constraint method to generate the Pareto frontier and analyze the trade-off between total repair costs and total repair time. We demonstrate the approach on an example problem.

Machine Learning to Predict Medical Devices Repair and Maintenance Needs

Products often experience different failure and repair needs during their lifespan. Prediction of the type of failure is crucial to the maintenance team for various reasons, such as realizing the device performance, creating standard strategies for repair, and analyzing the trade-off between cost and profit of repair. This study aims to apply machine learning tools to forecast failure types of medical devices and help the maintenance team properly decides on repair strategies based on a limited dataset. Two types of medical devices are used as the case study. The main challenge resides in using the limited attributes of the dataset to forecast product failure type. First, a multilayer perceptron (MLP) algorithm is used as a regression model to forecast three attributes, including the time of next failure, repair time, and repair time z-scores. Then, eight classification models, including Naïve Bayes with Bernoulli (NB-Bernoulli), Gaussian (NB-Gaussian), Multinomial (NB-Multinomial) model, Support Vector Machine with linear (SVM-Linear), polynomial (SVM-Poly), sigmoid (SVM-Sigmoid), and radical basis (SVM-RBF) function, and K-Nearest Neighbors (KNN) are used to forecast the failure type. Finally, Gaussian Mixture Model (GMM) is used to identify maintenance conditions for each product. The results reveal that the classification models could forecast failure type with similar performance, although the attributes of the dataset were limited.

Analysis and genetic algorithm optimization of a series system with K-out-of-(n + m): G mixed standby subsystems subject to imperfect switching and elapsed repair time

PurposeIn this paper, a new general system consisted of l subsystems connected in series is introduced. Each subsystem connected in K-out-of-(n + m): G mixed standby configuration.Design/methodology/approachThe lifetime of the system's units is assumed to be exponentially distributed and there is elapsed repair time with general distribution. The switch in each subsystem is assumed to be imperfect with the failure process follows an exponential distribution. A genetic algorithm is applied to the system to obtain the optimal solution of the system and solve the redundancy allocation problem.FindingsAnalysis of availability, reliability, mean time to failure and steady-state availability of the system is introduced. The measures of the system are discussed in special two cases when the elapsed repair time follows gamma and exponential distribution. An optimization problem with bi-objective functions is introduced to minimize the cost of the system and maximize the reliability function. A numeric application is introduced to show the implementation and effectiveness of the system and redundancy allocation problem.Originality/valueA new general K-out-of-(n + m): G mixed standby model with elapsed repair time and imperfect switching is introduced.

Intelligent restoration of ancient murals based on discrete differential algorithm

Many defects now appear in ancient murals due to both natural and man-made factors. To better repair the damaged ancient murals, this paper improves the existing intelligent restoration method. It identifies and marks the crack-producing area, categorizes the mural image into two types – texture area and flat area according to the local gradient features, decides the initial sample block and calculates the weight, analyzes the extracted pixel data and applies discrete differential algorithm to supplement image defects. Through experiments, the method is validated in meeting the needs of image continuity law and human vision. It can shorten the repair time and restore mural cracks in an intelligent way.