Pseudo Time Research Articles

Single-Cell RNA Sequencing Uncovers Pathological Processes and Crucial Targets for Vascular Endothelial Injury in Diabetic Hearts.

Cardiovascular disease remains the leading cause of high mortality in individuals with diabetes mellitus. Endothelial injury is a major contributing factor for vascular dysfunction in diabetes. However, the precise mechanisms underlying endothelial cell injury and their heterogeneity in diabetes remains elusive. In this study, single-cell sequencing is performed in heart tissues from leptin receptor knock-out (db/db) diabetic mice at various pathological stages. Through cell cluster identification, differential gene analysis, intercellular communication analysis, pseudo time analysis, and transcription factor analysis, a novel mechanism of cardiac vascular endothelial damage in diabetes is identified. Specifically, a single-cell transcription map of cardiac vascular endothelial cells is presented in db/db mice. Diverse cellular clusters are found to play vital roles under diabetes-induced damage, highlighting crucial transcription factors involved in their regulation. In addition, the essential transcription factor Ets1 is found to protect against vascular endothelial injury in db/db mice. In summary, the work provides a comprehensive understanding of the development of diabetic cardiac vascular endothelial damage and the heterogeneity of the cells involved. These findings offer valuable insights into potential treatments and assessments of diabetic cardiovascular endothelial damage.

Application of dynamic material balance to evaluate oil wells in Buzurgan oil field

The Material Balance Equation is a crucial tool utilized in reservoir studies to evaluate fluids and rock properties at static pressures. The Flowing and Dynamic Material Balance methods offer a significant advantage by avoiding the requirement to shut down wells, as they use flowing pressure instead of static pressure under constant or variable flow rates. The concept of "Dynamic Material Balance" involves converting the bottom hole flowing pressure at any point at any given time to the average reservoir pressure at that point. This allows for the use of classical material balance calculations and the development of classical material balance plots. In this study, the Dynamic Material Balance and Agrawal Type Curve techniques were used to estimate average reservoir pressures, initial hydrocarbon in place, and ultimate oil recovery for a well in the Mishrif reservoir, the main reservoir in the Buzurgan oil field. Many wells in this field experience problems such as high-pressure decline or continuous water production, necessitating ongoing evaluation. While the Dynamic Material Balance method focuses on boundary-dominated flow data, the Agrawal-type curve technique analyzes data from both transient and boundary flow periods. Agarwal decline curves were constructed using relationships of pseudo pressure normalized production, material balance pseudo time, and dimensionless variables in well-test analysis. The results from both methods showed comparable results with an absolute percentage error of (0.738) %, (3.07) %, and 5.7% for oil-in-place, drainage area, and average reservoir pressure, respectively. This strong correlation between the Dynamic Material Balance and Type Curve results indicates their accuracy and reliability.

Clustering Pseudo Time Series: Exploring Trajectories in the Ageing Process.

Investigating the natural ageing process typically involves the use of extensive longitudinal datasets that can capture changes associated with the progression of ageing. However, they are often resource-intensive and time-consuming to conduct. Cross-sectional data, on the other hand, provides a snapshot of a population at many different ages and can capture many disease processes but do not incorporate the time dimension. Pseudo time series can be reconstructed from cross sectional data, with the aim to explore dynamic processes (such as the ageing process). In this paper we focus on employing pseudo time series analysis on cross-sectional population data that we constrain using age information to create realistic trajectories of people with different degrees of cardiovascular disease. We then use clustering methods to construct and label trajectory-based phenotypes, aiming to enhance our understanding of ageing and disease progression.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

Single-cell transcriptomics in MI identify Slc25a4 as a new modulator of mitochondrial malfunction and apoptosis-associated cardiomyocyte subcluster

Myocardial infarction (MI) is the leading cause of premature death. The death of cardiomyocytes (CMs) and the dysfunction of the remaining viable CMs are the main pathological factors contributing to heart failure (HF) following MI. This study aims to determine the transcriptional profile of CMs and investigate the heterogeneity among CMs under hypoxic conditions. Single-cell atlases of the heart in both the sham and MI groups were developed using single-cell data (GSE214611) downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The heterogeneity among CMs was explored through various analyses including enrichment, pseudo time, and intercellular communication analysis. The marker gene of C5 was identified using differential expression analysis (DEA). Real-time polymerase chain reaction (RT-PCR), bulk RNA-sequencing dataset analysis, western blotting, immunohistochemical and immunofluorescence staining, Mito-Tracker staining, TUNEL staining, and flow cytometry analysis were conducted to validate the impact of the marker gene on mitochondrial function and cell apoptosis of CMs under hypoxic conditions. We identified a cell subcluster named C5 that exhibited a close association with mitochondrial malfunction and cellular apoptosis characteristics, and identified Slc25a4 as a significant biomarker of C5. Furthermore, our findings indicated that the expression of Slc25a4 was increased in failing hearts, and the downregulation of Slc25a4 improved mitochondrial function and reduced cell apoptosis. Our study significantly identified a distinct subcluster of CMs that exhibited strong associations with ventricular remodeling following MI. Slc25a4 served as the hub gene for C5, highlighting its significant potential as a novel therapeutic target for MI.

Single Cell Isolation from Human Diabetic Fibrovascular Membranes for Single-Cell RNA Sequencing.

Single-cell transcriptomic analyses have emerged as very powerful tools to query the gene expression changes at the single-cell level in physiological and pathological conditions. The quality of the analysis is heavily dependent on tissue digestion protocols, with the goal of preserving thousands of single live cells to submit to the subsequent processing steps and analysis. Multiple digestion protocols that use different enzymes to digest the tissues have been described. Harsh digestion can damage certain cell types, but this might be required to digest especially fibrotic tissue as in our experimental condition. In this paper, we summarize a collagenase type I digestion protocol for preparing the single-cell suspension from fibrovascular tissues surgically removed from patients with proliferative diabetic retinopathy (PDR) for single-cell RNA sequencing (scRNA-Seq) analyses. We also provide a detailed description of the data analysis that we implemented in a previously published study. Key features • Single-cell suspension from fibrovascular membranes isolated from PDR patients. • Single-cell RNA sequencing analyses performed using Seurat package in RStudio. • Trajectory analyses or pseudotime analyses to study the trajectory over (pseudo)time of specific cell types. • This protocol requires Illumina HiSEQ4000 instrument and knowledge of R and RStudio language for the analyses.

Integrative Analysis of Machine Learning and Molecule Docking Simulations for Ischemic Stroke Diagnosis and Therapy

Due to the narrow therapeutic window and high mortality of ischemic stroke, it is of great significance to investigate its diagnosis and therapy. We employed weighted gene coexpression network analysis (WGCNA) to ascertain gene modules related to stroke and used the maSigPro R package to seek the time-dependent genes in the progression of stroke. Three machine learning algorithms were further employed to identify the feature genes of stroke. A nomogram model was built and applied to evaluate the stroke patients. We analyzed single-cell RNA sequencing (scRNA-seq) data to discern microglia subclusters in ischemic stroke. The RNA velocity, pseudo time, and gene set enrichment analysis (GSEA) were performed to investigate the relationship of microglia subclusters. Connectivity map (CMap) analysis and molecule docking were used to screen a therapeutic agent for stroke. A nomogram model based on the feature genes showed a clinical net benefit and enabled an accurate evaluation of stroke patients. The RNA velocity and pseudo time analysis showed that microglia subcluster 0 would develop toward subcluster 2 within 24 h from stroke onset. The GSEA showed that the function of microglia subcluster 0 was opposite to that of subcluster 2. AZ_628, which screened from CMap analysis, was found to have lower binding energy with Mmp12, Lgals3, Fam20c, Capg, Pkm2, Sdc4, and Itga5 in microglia subcluster 2 and maybe a therapeutic agent for the poor development of microglia subcluster 2 after stroke. Our study presents a nomogram model for stroke diagnosis and provides a potential molecule agent for stroke therapy.

A level set reliability-based topology optimization (LS-RBTO) method considering sensitivity mapping and multi-source interval uncertainties

With the diversification of engineering structure performance requirements and the continuous development of structural design refinement, structural design methods are facing more and more factors to be considered. It is necessary to develop advanced design technology. In this paper, a sensitivity mapping technique is proposed to improve the effect of topology optimization based on a gradient optimization algorithm. The applicability of this technology is analyzed. In addition, considering the influence of multi-source uncertainties in the whole life cycle of the structure, a reliability-based topology optimization strategy based on the level set method is proposed. The sensitivity of displacement constraint and reliability constraint with pseudo time is derived. Numerical examples and Monte Carlo verification results fully illustrate the applicability, effectiveness, and efficiency of the proposed method. This method has been successfully applied to the topology optimization design of the rocket skid structure.

Parameterized Domain Mapping for Order Tracking of Rotating Machinery

Because of the periodicity of vibration signals from the cyclic motion of rotating machinery, many spectrum-based methods are widely applied. However, frequency distortion often occurs under non-stationary conditions, which weakens the effectiveness of spectrum-based methods. Order tracking can overcome the frequency distortion problem, but the instantaneous angular speed is hard to detect, especially under strong noise or with close-spaced frequencies. In this paper, we map the signal into a new domain called the pseudo time domain to eliminate the non-stationarity based on the mechanism of rotating machinery. A parameterized domain mapping function (PDMF) is used to represent the relationship between the time and pseudo time domains. Instead of conducting order tracking after detecting the instantaneous angular speed, we directly optimize the parameter set of the PDMF to construct an appropriate pseudo time domain. A new spectrum concentration indicator considering the continuity of the spectrum is built as the optimization objective. The Legendre polynomial, which shows good approximation property, is applied as the general kernel function of PDMF. Numerical and experimental verification shows good anti-noise performance and the ability to deal with complex and close-spaced frequencies under speed variation or speed fluctuation conditions.

P-796 Single-cell transcriptomics unveils a role for leptin receptor in human endometrial mesenchymal stromal/stem cells

Abstract Study question What are the characteristics of leptin receptor (LepR) expressing cells within the endometrial mesenchymal stromal/stem cells (eMSCs) population? Summary answer LepR+ eMSCs display better stem-cell capacities as well as distinctively quiescent properties compared to eMSCs and LepR- eMSCs. What is known already Single-cell RNA sequencing (scRNA-seq) reveals that cultured eMSCs are heterogeneous. LepR is highly expressed in bone marrow MSCs, and LepR+ cells contribute towards the formation of fibroblast colony-forming units (CFU-F). Mouse LepR+ MSCs are quiescent and activate upon injuries to form osteoblasts and adipocytes in the bone marrow. In this study, we hypothesized that LepR+ cells play a critical role in the stem cell population in human endometrium. Study design, size, duration We performed scRNA-seq on magnetic bead selected eMSCs (CD140b+CD146+ cells). Functional stem cell assays such as clonogenicity and serial subcloning were performed on the different stromal subsets (eMSC, LepR- eMSC, and LepR+ eMSC) isolated by fluorescence-activated cell sorting (FACS) cultured endometrial stromal cells. The cell cycle analysis was evaluated by multicolor flow cytometry on freshly isolated endometrial stromal cells. Participants/materials, setting, methods The endometrial samples were collected from women aged 37–53 years with regular menstrual cycles undergoing hysterectomy. The expression and localization of LepR in human endometrium were determined by flow cytometry and immunohistochemistry, respectively. The in vitro colony-forming and self-renewal abilities were assessed on FACS sorted endometrial stromal populations. The cell cycle status of the stromal subsets was determined by flow cytometry and the quiescent property was determined by qPCR (RB1, RBL2, CDKN1A, CDKN1B, and E2F4). Main results and the role of chance Clustering analysis of the scRNA-seq data revealed 5 subpopulations (SP0-4) of cultured eMSCs. A high proportion (>90%) of the cells in SP3 and SP4 were at the G1 phase. The majority of the SP3 cells (92%) were located at the root of the cell trajectory and pseudo time inference analysis. The expression of LepR was highly specific in SP3 suggesting LepR eMSCs are potentially the origin progenitors.The expression of LepR in endometrial stromal cells was 2.61 ± 1.43%, and in eMSCs was 25.59 ± 19.04%. In vitro experiments demonstrated that LepR+ eMSCs exhibit better stem-cell properties, including clonogenic and self-renewal ability, than its negative counterparts. QPCR validation of cell-quiescence associated genes revealed higher expression of RB1, RBL2, and CDKN1B in the LepR+ eMSCs than eMSCs and LepR- eMSCs. In addition, multicolor flow analysis on freshly isolated endometrial cells revealed significantly higher proportion of LepR+ eMSCs are in the G0 phase of the cell cycle when compared to eMSCs and LepR- eMSCs. The above results indicate in vivo LepR+ eMSCs are relatively more quiescent. Limitations, reasons for caution The mechanism involved in maintaining or activating quiescent LepR+ eMSCs remains unknown. The role of LepR+ is likely to be different in human compared to mouse. We also lack an in vivo model for studying LepR+ eMSCs during endometrial repair. Wider implications of the findings Our findings extend the understanding of heterogeneous human endometrial mesenchymal stem cells and unveil a new role for LepR in endometrial regeneration. Trial registration number not applicatble

A Comprehensive Analysis of Risk Factors Associated With Inpatient Falls.

The primary objective of this study was to identify the relationship between rates of falls among hospitalized patients and the use of inpatient medications associated with falls. This is a retrospective study on patients older than 60 years, hospitalized between January 1, 2021, and December 31, 2021. Ventilated patients and patients with a length of stay or fall less than 48 hours after admission were excluded. Falls were determined by assessing documented post fall assessments in the medical record. Patients who fell were matched 3:1 with control patients based on demographic data (age, sex, length of stay up to the fall time, and Elixhauser Comorbidity score). For controls, a pseudo time to fall was assigned based on matching. Medication information was gathered from barcode administration data. Statistical analysis was conducted using R and RStudio. A total of 6363 fall patients and 19,089 controls met the inclusion and exclusion criteria. Seven drug classes were identified as statistically significant ( P < 0.001) in increasing an inpatient's rate of falling: angiotensin-converting enzyme inhibitors (unadjusted odds ratio [OR], 1.22), antipsychotics (OR, 1.93), benzodiazepines (OR, 1.57), serotonin modulators (OR, 1.2), selective serotonin-reuptake inhibitors (OR, 1.26), tricyclics and norepinephrine reuptake inhibitors (OR, 1.45), and miscellaneous antidepressants (OR, 1.54). Hospitalized patients older than 60 years are more likely to fall while taking angiotensin-converting enzyme inhibitors, antipsychotics, benzodiazepines, serotonin modulators, selective serotonin-reuptake inhibitors, tricyclics, norepinephrine reuptake inhibitors, or miscellaneous antidepressants. Patients on opiates and diuretics had a significant decrease in rate of falls.

A nonlinear stochastic finite element method for solving elastoplastic problems with uncertainties

AbstractThis article presents an efficient nonlinear stochastic finite element method to solve stochastic elastoplastic problems. Similar to deterministic elastoplastic problems, we describe history‐dependent stochastic elastoplastic behavior utilizing a series of (pseudo) time steps and go further to solve the corresponding stochastic solutions. For each time step, the original stochastic elastoplastic problem is considered as a time‐independent nonlinear stochastic problem with initial values given by stochastic displacements, stochastic strains, and internal variables of the previous time step. To solve the stochastic solution at each time step, the corresponding nonlinear stochastic problem is transformed into a set of linearized stochastic finite element equations by means of finite element discretization and a stochastic Newton linearization, while the stochastic solution at each time step is approximated by a sum of the products of random variables and deterministic vectors. Each couple of the random variable and the deterministic vector is also used to approximate the stochastic solution of the corresponding linearized stochastic finite element equation that can be solved via a weakly intrusive method. In this method, the deterministic vector is computed by solving deterministic linear finite element equations, and corresponding random variables are solved by a non‐intrusive method. Further, the proposed method avoids the curse of dimensionality successfully since its computational effort does not increase dramatically as the stochastic dimensionality increases. Four numerical cases are used to demonstrate the good performance of the proposed method.

A multi-view co-training network for semi-supervised medical image-based prognostic prediction

Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied. In this paper, we propose a semi-supervised co-training deep neural network incorporating a support vector regression layer for survival time estimation (Co-DeepSVS) that improves the efficiency in utilizing censored data for prognostic prediction. First, we introduce a support vector regression layer in deep neural networks to deal with censored data and directly predict survival time, and more importantly to calculate the labeling confidence of each case. Then, we apply a semi-supervised multi-view co-training framework to achieve accurate prognostic prediction, where labeling confidence estimation with prior knowledge of pseudo time is conducted for each view. Experimental results demonstrate that the proposed Co-DeepSVS has a promising prognostic ability and surpasses most widely used methods on a multi-phase CT dataset. Besides, the introduction of SVR layer makes the model more robust in the presence of follow-up bias.

Transient electromagnetic pseudo wavefield imaging based on the sweep-time preconditioned precise integration algorithm

The solution of the transient electromagnetic pseudo wavefield is a seriously ill-posed problem, and conventional methods do not facilitate a stable and high-precision solution. The precise integration method is a powerful method for solving ill-conditioned linear equations, since it converts the solution of highly ill-conditioned linear equations into an integral process, thereby overcoming the key challenge of solving transient electromagnetic pseudo wavefields. In this study, various norm functions are employed to preprocess the coefficient matrix based on this method. The study findings reveal that, when compared to the results obtained without preprocessing, the result obtained with 1-norm preprocessing is optimal. Then, following preprocessing, the condition number of the coefficient matrix improves, and the optimal solution can be reached in fewer iterations. In this paper, the corresponding relationship between the diffusion field time range and pseudo time range is then further studied, as are the selection criteria of discrete points of diffusion field time and pseudo time, and the selection basis of the integral step of the preconditioned precise integration method. The resolution can be further improved by introducing the sweep-time strategy. After verifying the stability and practicality of the proposed method, the effectiveness of the proposed method is validated using a complex 3D mineral model. The results reveal that the pseudo wavefield reflects the interface information of abnormal bodies. Finally, the study demonstrates the great developmental potential of the proposed method in engineering exploration employing field data.

Characterization of the microenvironment of diabetic foot ulcers and potential drug identification based on scRNA-seq.

Diabetes foot ulcers (DFUs) are a type of foot infection, ulcer, and/or deep tissue destruction caused by neuropathy and vascular disease in the distal extremities of diabetic patients. Its pathogenesis and its microenvironment are not entirely understood. Initially, the GSE165816 data set from the GEO database was utilized for single cell analysis to reveal the microenvironment and functional status of DFUs. The GSE199939 RNA-seq data set was utilized for external validation. On the basis of the logistic regression machine learning algorithm (OCLR), pseudo time series analysis, dryness index analysis, and drug target gene analysis were then performed. By constructing drug-gene and gene-gene networks, we can locate the most recent DFUs treatments. Finally, immunofluorescence technology was used to detect the cell-related markers of the DFUs microenvironment, and qPCR was used to detect the expression of drug targets in DFUs. Firstly, we used the Cell Maker database to obtain information about human cells and related gene markers, and manually reviewed a total of 45 kinds of cells and maker information that may appear in the DFUs microenvironment, which were divided into 17 cell clusters after annotation. Subsequently, we counted the proportions of DM and DFUs in different types of cells, and the results showed that the proportions of macrophages, white blood cells, and monocytes were higher in patients with DFUs, while the proportions of pluripotent stem cells and stromal cells were higher in patients with DM. The Pseudo-time series analysis of cells in DFUs showed that the differentiation pathways of immune cells, mesenchymal cells and stem cells were similar in the three states, while the other cells were distributed in different stages. At the level of a single cell, the scores of both multipotential stem cells and hematopoietic stem cells were significantly lower in DFU healing and non-healing than in DM. Additionally, the highly expressed genes in DFU were chosen as drug targets. We identified seven potential target genes and discovered twenty drugs with high significance. Finally, the colocalization relationship between CD19, ITGAM, and HLA-DR expression in monocytes and macrophages of DFU skin tissue and healthy subjects was analyzed by laser confocal microscopy with the immunofluorescence triple labeling method. The results showed that the expressions of CD19, ITGAM, and HLA-DR in the skin of DFUs were significantly higher than those in the skin of healthy subjects, and the co-localization relationship was significant in DFUs. This study can serve as a resource for the treatment of DFUs.

Norges Bank Output Gap Estimates: Forecasting Properties, Reliability, Cyclical Sensitivity and Hysteresis

AbstractThis paper documents the suite of models (SoMs) used by Norges Bank to estimate the output gap. The models are estimated using data on GDP, unemployment, inflation, wages, investment, house prices and credit. We evaluate the estimated output gap series in terms of its forecasting properties, its reliability and its cyclical sensitivity to various measures of demand and supply shocks. A simple equally weighted average of estimates from different models features a better forecasting performance than each individual model. In addition, it helps predicting inflation in pseudo real time and exhibits limited variations when new data become available. The summary measure of potential output responds strongly and rapidly to permanent shocks and to narrative measures of technology shocks but, although to a more limited extent, also to demand shocks, thus partly capturing hysteresis effects.

Coupling simulation and accelerated degradation model for reliability estimation: Application to a voltage regulator

This paper demonstrates the use of numerical simulation data, depending on the behavior of a low voltage dropout regulator, for reliability performance estimation based on the accelerated degradation test data. The regulator was designed using Cadence Virtuoso software in 180 nm AMS CMOS technology, and simulated to evaluate its output voltage variations to both temperature and input voltage. The output voltage degradation data were generated according to environmental parameters (input voltage and temperature) constraints, which makes it possible to define failure thresholds under accelerated conditions, making use of the numerical simulation model along with the proposed degradation model. Degradation path model has been adopted to determine the pseudo failure time under the specified failure criterion (5 %). Acceleration law model has then been derived to estimate the reliability model parameters by performing maximum likelihood estimation method not only to analyse but also to predict lifetime data distribution of the regulator under different voltage and temperature stress conditions.

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital “Istituti Clinici Salvatore Maugeri” in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

Fast hyperbolic relaxation elliptic solver for numerical relativity: Conformally flat, binary puncture initial data

We introduce nrpyelliptic, an elliptic solver for numerical relativity (NR) built within the nrpy+ framework. As its first application, nrpyelliptic sets up conformally flat, binary black hole (BBH) puncture initial data (ID) on a single numerical domain, similar to the widely used twopunctures code. Unlike twopunctures, nrpyelliptic employs a hyperbolic relaxation scheme, whereby arbitrary elliptic partial differential equations (PDEs) are trivially transformed into a hyperbolic system of PDEs. As consumers of NR ID generally already possess expertise in solving hyperbolic PDEs, they will generally find nrpyelliptic easier to tweak and extend than other NR elliptic solvers. When evolved forward in (pseudo)time, the hyperbolic system exponentially reaches a steady state that solves the elliptic PDEs. Notably nrpyelliptic accelerates the relaxation waves, which makes it many orders of magnitude faster than the usual constant wave speed approach. While it is still $\ensuremath{\sim}12\mathrm{x}$ slower than twopunctures at setting up full-3D BBH ID, nrpyelliptic requires only $\ensuremath{\approx}0.3%$ of the runtime for a full BBH simulation in the einstein toolkit. Future work will focus on improving performance and generating other types of ID, such as binary neutron stars.

High-efficiency hybrid trim method for CFD simulation of rigid coaxial rotor

In order to solve the trim problem of computational fluid dynamics (CFD) simulation for rigid coaxial rotor, a hybrid trim model coupling CFD method and high-efficiency model (blade element theory) is established. In the trim process, the Jacobian matrix is solved by the high-efficiency model, while the CFD solver is only called for rotor performances modifying after each trimming step. The influences of pseudo time step, number of CFD revolution, and inflow model are investigated. Validation cases of AH-1G and Harrington-1 rotors are carried out, and good agreements are obtained. Results show that the trim efficiency can be significantly improved by saving the calculation of CFD for the Jacobian matrix. The trim accuracy is guaranteed by the correction operation with the CFD solver at each step. Moreover, as only time-averaged rotor performances are useful for trim, the efficiency can be further improved by adopting appropriately small pseudo time step and CFD revolution. The hybrid trim model has high robustness. The accuracy of inflow model for the coaxial rotor affects the convergence speed, but the final convergence can be achieved generally.