Pseudo Time Research Articles

Addressing geometrical attributes and seismic imaging capability of fault systems in a world-class metal endowed region: Abitibi Greenstone Belt, Canada

With an excess of 75 Moz of gold produced, the Timmins-Porcupine mining camp in the Abitibi green stone belt is the largest gold producing camp in Canada. Most of the endowment is associated with two major, east-west striking, crustal fault zones, the Porcupine Destor fault and the Pipestone fault. The seismic survey presented in this study crosses these prolific fault systems in the Matheson area, which represents the eastern extension of the Timmins-Porcupine mining camp. The survey includes two receiver lines, the east and west profiles, with unequal receiver spacing among them and seismic shots only acquired along the east profile. After careful considerations, we treated the survey as a parallel geometry and designated the east and the west processing profiles to potentially image subsurface architecture in crystalline rock environment. We considered three scenarios to process the data: the entire survey, the east, and the west profiles individually considered. A full pseudo pre-stack time migrated 3D cube of the Archean crust documents the usefulness in imaging the extent of the geological features. By integrating the 2D and 3D seismic images with the local magnetic susceptibility isosurface model, we interpreted the Porcupine Destor fault to dip shallowly to the south and the Pipestone fault as steeply-dipping/subvertical. The techniques used here can be further applied to other areas with similar dense infrastructure requiring imperfect acquisition survey geometry, including crooked lines, to produce fully 3D or pseudo seismic cubes in such areas of great exploration interest.

Numerical Assessment of Dipole Interaction with the Single-Phase Nanofluid Flow in an Enclosure: A Pseudo-Transient Approach.

Nanofluids substantially enhance the physical and thermal characteristics of the base or conducting fluids specifically when interacting with the magnetic field. Several engineering processes like geothermal energy extraction, metal casting, nuclear reactor coolers, nuclear fusion, magnetohydrodynamics flow meters, petrochemicals, and pumps incorporate magnetic field interaction with the nanofluids. On the other hand, an enhancement in heat transfer due to nanofluids is essentially required in various thermal systems. The goal of this study is to figure out that how much a magnetic field affects nanofluid flow in an enclosure because of a dipole. The nanofluid is characterized using a single-phase model, and the governing partial differential equations are computed numerically. A Pseudo time based numerical algorithm is developed to numerically solve the problem. It can be deduced that the Reynolds number and the magnetic parameter have a low effect on the Nusselt number and skin friction. The Nusselt number rises near the dipole location because of an increase in the magnetic parameter and the Reynolds number . The imposed magnetic field alters the region of high temperature nearby the dipole, while newly generated vortices rotate in alternate directions. Furthermore, nanoparticle volume fraction causes a slight change in the skin friction while it marginally reduces the Nusselt number.

Increased Expression of Tim-3 Is Associated With Depletion of NKT Cells In SARS-CoV-2 Infection.

In the ongoing coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), natural killer T (NKT) cells act as primary initiators of immune responses. However, a decrease of circulating NKT cells has been observed in COVID-19 different stages, of which the underlying mechanism remains to be elucidated. Here, by performing single-cell RNA sequencing analysis in three large cohorts of COVID-19 patients, we found that increased expression of Tim-3 promotes depletion of NKT cells during the progression stage of COVID-19, which is associated with disease severity and outcome of patients with COVID-19. Tim-3+ NKT cells also expressed high levels of CD147 and CD26, which are potential SARS-CoV-2 spike binding receptors. In the study, Tim-3+ NKT cells showed high enrichment of apoptosis, higher expression levels of mitochondrial genes and caspase genes, with a larger pseudo time value. In addition, Tim-3+ NKT cells in COVID-19 presented a stronger capacity to secrete IFN-γ, IL-4 and IL-10 compared with healthy individuals, they also demonstrated high expression of co-inhibitory receptors such as PD-1, CTLA-4, and LAG-3. Moreover, we found that IL-12 secreted by dendritic cells (DCs) was positively correlated with up-regulated expression of Tim-3 in NKT cells in COVID-19 patients. Overall, this study describes a novel mechanism by which up-regulated Tim-3 expression induced the depletion and dysfunction of NKT cells in COVID-19 patients. These findings not only have possible implications for the prediction of severity and prognosis in COVID-19 but also provide a link between NKT cells and future new therapeutic strategies in SARS-CoV-2 infection.

An implicit lattice Boltzmann flux solver for simulation of compressible flows

The lattice Boltzmann flux solver (LBFS) is a novel Boltzmann-typed solver under the finite volume framework, which has many advantages over traditional lattice Boltzmann equation (LBE) solvers. However, the existing versions of LBFS are explicit in temporal discretization. Therefore, they are of low computational efficiency and are mostly applied to the simulation of flows around simple geometries. In this paper, an implicit lattice Boltzmann flux solver for compressible flows is proposed, which can efficiently march in time with a large CFL number, and therefore facilitates the simulation of flows around complex geometries in practical engineering applications. Aiming to obtain the time-accurate results of unsteady flows, the implicit LBFS is further combined with the dual time-stepping technique to efficiently simulate unsteady flows, in which a sub-iteration is applied on the pseudo time level. In the sub-iteration, the Jacobian matrix is preconditioned by the lower-upper symmetric Gauss-Seidel (LUSGS) method and solved by the generalized minimal residual method (GMRES). The accuracy and efficiency of the proposed solver are demonstrated by various simulations in a wide Mach number regime. To further demonstrate its performance in practical engineering applications, the proposed solver is also used to simulate the flow over complex aircraft models.

Cardiovascular Risk After SARS-CoV-2 Infection Is Mediated by IL18/IL18R1/HIF-1 Signaling Pathway Axis.

ObjectivesCurrently, cardiovascular risk associated with COVID-19 has been brought to people’s attention, but the mechanism is not clear. The aim of this study is to elucidate the mechanisms based on multiple omics data.MethodologyWeighted gene co-expression network analysis (WGCNA) was used to identify key pathways. Combination analysis with aneurysm and atherosclerosis related pathways, hypoxia induced factor-1 (HIF-1) signaling were identified as key pathways of the increased cardiovascular risk associated with COVID-19. ScMLnet algorithm based on scRNA-seq was used to explore the regulation of HIF-1 pathway by intercellular communication. Proteomic analysis was used to detect the regulatory mechanisms between IL18 and HIF-1 signaling pathway. Pseudo time locus analysis was used to study the regulation of HIF1 signaling pathway in macrophages and vascular smooth muscle cells (VSMC) phenotypic transformation. The Virtual Inference of protein-activity by Enriched Regulon (VIPER) analysis was used to study the activity of regulatory proteins. Epigenetic analysis based on methylation revealed epigenetic changes in PBMC after SARS-CoV-2 infection. Potential therapeutic compounds were explored by using Cmap algorithm.ResultsHIF-1 signaling pathway is a common key pathway for aneurysms, atherosclerosis and SARS-CoV-2 infection. Intercellular communication analysis showed that macrophage-derived interleukin-18 (IL-18) activates the HIF-1 signaling pathway through IL18R1. Proteomic analysis showed that IL18/IL18R1 promote NF-κB entry into the nucleus, and activated the HIF-1 signaling pathway. Macrophage-derived IL18 promoted the M1 polarization of macrophages and the syntactic phenotype transformation of VSMCs. MAP2K1 mediates the functional regulation of HIF-1 signaling pathway in various cell types. Epigenetic changes in PBMC after COVID-19 infection are characterized by activation of the type I interferon pathway. MEK inhibitors are the promising compounds for the treatment of HIF-1 overactivation.ConclusionsThe IL18/IL18R1/HIF1A axis is expected to be an therapeutic target for cardiovascular protection after SARS-CoV-2 infection. MEK inhibitors may be an choice for cardiovascular protection after SARS-COV-2 infection

DESIGN OF ADVANCED PSEUDO RANDOM DISCRETE TIME EVEN SYSTEM FOR FAULT DETECTION

: In this paper, the design of advanced pseudo random discrete time even system for fault detection is implemented to increase the speed of the system. Here sufficient conditions are given to implement the estimator. Pseudo random multiple input signals are given to DES. The main intent of this pseudo random discrete time even system is to increase the speed and produce efficient result in terms of delay and area. The discrete time even system will take multiple signals and perform its operation. The adaptive MISO (multiple input and single output) controller will controls the speed of the system. Self-tuning regulator will tune the multiple inputs and give the single output. Hence, from pseudo random single output block, the output is obtained. Compared to the existed system, the pseudo random discrete time even system gives effective output.

Production effect evaluation of shale gas fractured horizontal well under variable production and variable pressure

Most shale gas production prediction models are established on the ideal production conditions (the constant pressure and constant production condition), deviating from the production performance characteristics of shale gas wells with variable production and pressure and leading to large error in unknown model parameter inversion of the actual shale gas wells, and obviously showing great limitations in characterizing the dynamic production performance and predicting EUR of shale gas wells for variable production-pressure. Furthermore, the material balance equation is more common in solving the variable production and pressure of shale gas wells issues, but it is only suitable for characterizing boundary flow. In this paper, a semi-analytical productivity prediction model of shale gas well for constant pressure was established and validated by the practical production data. Then the material balance pseudo time and linear superposition pseudo time were introduced respectively to modify the true pseudo time in the proposed productivity prediction model to demonstrate that the linear superposition pseudo time was proper to obtain the unknown fracture inversion parameters compared to material balance pseudo time during the variable pressure-production of actual shale gas wells. Finally, the Monte Carlo method in the IHS numerical software was adopted to analyze the impact of the fracture half-length uncertainty on the probability distribution of EUR, which showed that the P50 value is closer to the deterministic EUR value predicted by the analytical production prediction model. Research indicates that it is of certain theoretical reference to reduce the risk of production prediction during the production of shale gas wells and guide the optimization of development plans.

Lymphoblasts from Minimal Residual Disease in B-ALL Have a Transcriptional and Chromatin Accessibility Profile Shifted Towards Earlier Hematopoietic Progenitor Biology

Background: Prognosis for children and adults with B-cell acute lymphoblastic leukemia (B-ALL), the most common pediatric malignancy, is heavily dependent on minimal residual disease (MRD) levels at the end of induction. Rates of detectable MRD are dependent on patient age and genomic subgroup. For patients with detectable MRD, the primary therapeutic adjustment is intensification of therapy if possible. Despite the incredible advances in MRD detection and clear associations with prognosis, uncovering the underlying biology of MRD could provide critical insight into mechanisms of therapy resistance and suggest research avenues for precise therapeutic approaches.Methods: We collected samples from paired diagnostic and MRD time points from four patients with B-ALL, with MRD ranging from 0.15-0.84%. Based upon clinical MRD immunophenotype, we performed FACS sorting of lymphoblasts from each sample in order to isolate the rare MRD cells, and performed single-cell RNA sequencing (scRNA-seq) on all four pairs and single-cell ATAC sequencing (scATAC-seq) on two of these pairs. Data were compared across time points and integrated with publicly available datasets studying normal hematopoietic development. Raw scRNA-seq and scATAC-seq data were preprocessed using the Cell Ranger pipeline (10x Genomics, v.3.1.0). Seurat V4 (Hao et al., Cell 2021), Signac (Stuart et al., bioRxiv 2020), and MetaCell (Baran et al., Genome Biology 2019) were used to perform quality control (QC), normalize, identify highly variable features, perform dimension reduction, identify both discretized cell clusters and transient meta cells, and ultimately, visualize using uniform manifold approximation and projection (UMAP).Results: Post scRNA-seq QC, the median number of cells per datasets was 2772, the median number of genes per dataset was 13855 and the median number of UMIs per cell was 6518. Post scATAC-seq QC, the median number of cells per datasets was 1157, the median number of peaks per dataset was 76190 and the median number of peaks per cell was 26642. We compared the diagnosis to MRD time points within each of the four cases. In each case, the transcriptionalprofile of the lymphoblasts changed from diagnosis to MRD timepoints (Fig. 1a), with specific transcriptional clusters enriched in the sorted MRD samples (Fig. 1b). To understand this difference, we compared the time points to publicly available datasets of normal hematopoietic development. First, we compared our data to published bulk RNA-seq data from sorted, well-defined hematopoietic development states (Corces et al., Nature Genetics 2016). We performed principal component analysis (PCA) on this data, and projected our generated scRNA-seq data onto the same principal component (PC) space. While the first PC captures the systematic artifact between the bulk and single-cell data, the second PC captures the developmental pseudo time. In each case, cells from sorted MRD show transcriptional profiles shifterd towards earlier progenitor cells on development pseudo time (Fig. 1c). We performed a similar analysis using scATAC-seq from a paired diagnosis and MRD sample, showing that the chromatin accessibility profile of MRD lymphoblasts reveals the same directional change towards patterns seen in earlier hematopoietic progenitor cells. We repeated a similar data integration on a published scRNA-seq dataset from well annotated normal hematopoietic developmental states (Granja et al. Nature Biotechnology 2019), which again revealed that the transcriptional patterns of MRD lymphoblasts align more closely with common lymphoid progenitors than pre-B cells. (Fig. 1d).Conclusions: Using FACS sorting to isolated rare populations of MRD and subsequent single-cell analyses, we show that therapeutic pressure results in residual disease with an earlier progenitor transcriptional profile, likely determined by altered chromatin accessibility profile. Moving forward, we will explore the drivers of these changing transcriptomic and epigenomic profiles through expanding our number of cases to confirm initial findings, optimizing ATAC sequencing approaches to study the rare cell populations present in low-level MRD, and exploring specific transcription factor motifs driving these differences. Our findings have the potential to inform research into rational therapeutic approaches directed toward the specific biology of low level MRD in B-ALL. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Estimate Gas Initially in Place of Tight Gas Reservoirs Based on Developed Methodology of Dynamic Material Balance Technique

With growing global demand for hydrocarbons and decreasing conventional reserves, the gas industry is shifting its focus in the direction of unconventional reservoirs. Tight gas reservoirs have typically been deemed uneconomical due to their low permeability which is understood to be below 0.1mD, requiring advanced drilling techniques and stimulation to enhance hydrocarbons. However, the first step in determining the economic viability of the reservoir is to see how much gas is initially in place. Numerical simulation has been regarded across the industry as the most accurate form of gas estimation, however, is extremely costly and time consuming. The aim of this study is to provide a framework for a simple analytical method to estimate gas. Usually during production three variables are readily accessible: production rate, production time, and pressure-volume-temperature properties. This paper develops an analytical approach derived from the dynamic material balance proposing a new methodology to calculate pseudo time, with an interactive technique. This model encompasses pseudo functions accounting for pressure dependent fluid and rock variables. With the dynamic material balance yielding weak results in the linear flow regimes, an additional methodology derived from the volumetric tank model has been taken into consideration whereby equivalent drainage area is linked to total reservoir area. It has been shown even with short production data this volumetric approach yields accurate results. This proposed methodology has been validated against previous literature and additional cases considered to determine the sensitivity of each of it to reservoir parameters. Finally, it is shown that this method works for both fractured and unfractured wells in tight gas reservoirs, however, it is sensitive to the quantity of data based within the pseudo steady state flow period.

Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data.

Background: Parkinson’s disease (PD), Alzheimer’s disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potential for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applied in biology and shows excellent results. In this article, we use AI to model PD, AD, MCI data and analyze the possible connections between them. Method: Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed artificial neural network (ANN) classifier models based on these features to distinguish samples, and evaluated the model’s performance with classification accuracy and Area Under Curve (AUC). Second, we used Ingenuity Pathway Analysis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators. Result: We used different classifier to construct the model and finally found that ANN model would outperform the traditional machine learning model. In summary, three different classifiers were constructed to be used in different application scenarios, First, we incorporated 6 indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Secondly, incorporated 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Then, 15 indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. All classifier models have an auc value greater than 0.95. Then, we verified that the constructed feature engineering filtered out fewer important features but contained more information, which helped to build a better model. In addition, by classifying the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that are both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies. Conclusion: The feature engineering we build allows better extraction of information while reducing the number of features, which may help in subsequent applications. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Overall, it is important for us to study neurodegenerative diseases from both diagnostic and interventional aspects.

Stability and accuracy of Engquist–Majda absorbing boundary condition for pseudo spectral time domain method

AbstractLagrange polynomials based Chebyshev Pseudo Spectral Time Domain (L‐CPSTD) method is an accurate time domain solver for electromagnetic problems. It utilizes the Lagrange interpolation polynomials to expand electromagnetic fields. This global interpolation, which uses all field values on all of the grid points to calculate the value at a single point, provides spectral accuracy. However, absorbing boundary conditions (ABCs) must be applied for open space problems. Engquist–Majda ABC is an important one due to its simplicity. Characteristic variables (CVs) can be used to implement the ABCs. In this article, for the first time, stability and accuracy of the Engquist–Majda ABC are proved by using the CVs in the L‐CPSTD solution of Maxwell's equations. The theoretical findings are verified by using the matrix eigenvalue method and the reflection coefficient in one and two dimensional open space examples. The numerical result is also validated by an analytical and an FDTD solution of a parallel‐plate waveguide problem. The efficiency of the proposed ABC is clearly shown.

THE LID-DRIVEN HYDROMAGNETIC FLOW OF NEWTONIAN AND NON-NEWTONIAN LIQUIDS IN A SQUARE CAVITY

In this paper, we formulate the steady hydromagnetic lid-driven cavity problem in a stream function-vorticity form for weakly electrically conducting Newtonian and non-Newtonian liquids. Then we solve them by using the concept of pseudo time derivative. The classical benchmark results of the Newtonian liquid are recovered as a limiting case and the inhibiting influence of the magnetic field on the Newtonian and non-Newtonian liquids’ flow field is clearly depicted through graphs. We also show certain aspects of the flow for the first time in tables.

Second-order monopile wave loads at linear cost

A method to compute the second-order free surface elevation, depth integrated force and mud line moment for a slender circular vertical cylinder is presented. The method is valid for unidirectional irregular waves and includes inertia loads and viscous loads.We first derive the linear transfer functions for free surface elevation, depth-integrated force and moment from the complex Fourier amplitudes of the velocity potential. Next, the second-order contributions are expressed through closed form quadratic transfer functions, which are further diagonalized through eigen decomposition. Hereby the second-order contributions can be computed as products of pseudo time series calculated by FFT, with the eigenvectors acting as transfer functions on the linear Fourier amplitudes.For a sample 3-hour sea state, we find that eight eigen vectors are sufficient to achieve an accuracy of 1% for the maximum peak value of force and moment and 1.3% for free surface elevation, relative to the standard deviation of each signal. These results are obtained 2500 faster than with the conventional approach and we demonstrate that the computational effort of the new method scales like O(NlogN), similar to linear wave loads, where N is the number of frequencies. For the eight mode approximation, the error bound of 1% for loads and 4% for free surface elevation are found to hold across various values of the normalized peak wave number from shallow to deep water. The accuracy is adjustable through the number of modes and is found to be independent of the time series length. The methods potential in practical design is discussed.

Let's do the time warp again: non‐linear time series matching as a tool for sequentially structured data in ecology

AbstractEcological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contain information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic time warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but also any sequentially structured data set, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small‐scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological data sets. In this study, we use an example data set of high‐resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith data sets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered “pseudo time series” data such as classification of species by shape.

Dissecting the Transcriptional and Chromatin Accessibility Heterogeneity of Proliferating Cone Precursors in Human Retinoblastoma Tumors by Single Cell Sequencing-Opening Pathways to New Therapeutic Strategies?

PurposeRetinoblastoma (Rb) is a malignant neoplasm arising during retinal development from mutations in the RB1 gene. Loss or inactivation of both copies of RB1 results in initiation of retinoblastoma tumors; however, additional genetic changes are needed for the continued growth and spread of the tumor. Ex vivo research has shown that in humans, retinoblastoma may initiate from RB1-depleted cone precursors. Notwithstanding, it has not been possible to assess the full spectrum of clonal types within the tumor itself in vivo and the molecular changes occurring at the cells of origin, enabling their malignant conversion. To overcome these challenges, we have performed the first single cell (sc) RNA- and ATAC-Seq analyses of primary tumor tissues, enabling us to dissect the transcriptional and chromatin accessibility heterogeneity of proliferating cone precursors in human Rb tumors.MethodsTwo Rb tumors each characterized by two pathogenic RB1 mutations were dissociated to single cells and subjected to scRNA-Seq and scATAC-Seq using the 10× Genomics platform. In addition, nine human embryonic and fetal retina samples were dissociated to single cells and subjected to scRNA- and ATAC-Seq analyses. The scRNA- and ATAC-Seq data were embedded using Uniform Manifold Approximation and Projection and clustered with Seurat graph–based clustering. Integrated scATAC-Seq analysis of Rb tumors and human embryonic/fetal retina samples was performed to identify Rb cone enriched subclusters. Pseudo time analysis of proliferating cones in the Rb samples was performed with Monocle. Ingenuity Pathway Analysis was used to identify the signaling pathway and upstream regulators in the Rb cone–enriched subclusters.ResultsOur single cell analyses revealed the predominant presence of cone precursors at different stages of the cell cycle in the Rb tumors and among those identified the G2/M subset as the cell type of origin. scATAC-Seq analysis identified two Rb enriched cone subclusters, each characterized by activation of different upstream regulators and signaling pathways, enabling proliferating cone precursors to escape cell cycle arrest and/or apoptosis.ConclusionsOur study provides evidence of Rb tumor heterogeneity and defines molecular pathways that can be targeted to define new treatment strategies.

The probable destructive mechanisms behind COVID-19 on male reproduction system and fertility.

PurposeThe present study aims to summarize the current understanding of probable mechanisms and claims of adverse effects of SARS-CoV-2 on male fertility potential.MethodsOur search was including original articles, reviews, guidelines, letters to the editor, comments on guidelines, and editorials, regarding the male reproductive system. We used the words SARS-CoV-2, coronavirus, severe acute respiratory syndrome coronavirus 2, “2019 ncov,” testis, sperm, male factor infertility, fertility treatment, semen, assisted reproductive technology (ART), sexual transmission, and ACE2.ResultsData showed coronavirus affects men more than women because of more expression of 2019 nCoV receptors (ACE2 and TMPRSS2) in testicular cells. Also, “Bioinformatics Analysis” suggests that sperm production may be damaged, since “Pseudo Time Analysis” has shown disruption in spermatogenesis. “Gene Ontology” (GO) showed an increase in viral reproduction and a decrease in sperm production-related terms. Recently, SARS-COV-2 mRNA and protein were detected in the semen of patients that had recovered from SARS-CoV-2 infection. Therefore, the probable disruption of blood-testis barrier (BTB) in febrile diseases is suspected in the acute phase of the disease enabling viral entry into the testes. Not only is spermatogenesis disturbed, but also disturbs gonadotropin, androgens, and testosterone secretion during SARS-CoV-2 infection. No sexual transmission has been reported yet; however, detection of the virus in semen still makes the sexual transmission an open question.ConclusionThere is a concern that male fertility may be disturbed after the SARS-CoV-2 infection. Therefore, follow-up of the reproductive functions and male fertility may be necessary in recovered cases, especially in aged men.

A high-order implicit least square-based finite difference-finite volume method for incompressible flows on unstructured grids

In this study, a high-order implicit least squares-based finite difference-finite volume (ILSFD-FV) method with a lattice Boltzmann flux solver is presented for the simulation of two-dimensional incompressible flows on unstructured grids. In this method, a high-order polynomial based on Taylor series expansion is applied within each control cell, where the unknown spatial derivatives at each cell center are approximated by the least squares-based finite difference scheme. The volume integral of the high-order polynomial over the control cell results in a pre-multiplied coefficient matrix in the time-dependent term. This makes the high-order method be implicit in nature. With this feature, a high-order implicit Runge–Kutta time integration scheme, namely, the explicit first-stage singly diagonally implicit Runge–Kutta (ESDIRK) scheme, is applied to obtain the time-accurate solutions for flow problems. The non-linear system of equations arising from each ESDIRK stage except for the first explicit stage is solved by a dual time stepping approach. A matrix-free lower-upper symmetric Gauss–Seidel solver is then used to efficiently march the solution in the pseudo time. The present high-order ILSFD-FV method is verified and validated by both steady and unsteady 2D incompressible flow problems. Numerical results indicate that the developed implicit method outperforms its explicit counterpart in terms of the convergence property and computational efficiency. The speedup ratio of the computational effort is about 3–22.

Bootstrap based inference for sparse high-dimensional time series models

Fitting sparse models to high-dimensional time series is an important area of statistical inference. In this paper, we consider sparse vector autoregressive models and develop appropriate bootstrap methods to infer properties of such processes. Our bootstrap methodology generates pseudo time series using a model-based bootstrap procedure which involves an estimated, sparsified version of the underlying vector autoregressive model. Inference is performed using so-called de-sparsified or de-biased estimators of the autoregressive model parameters. We derive the asymptotic distribution of such estimators in the time series context and establish asymptotic validity of the bootstrap procedure proposed for estimation and, appropriately modified, for testing purposes. In particular, we focus on testing that large groups of autoregressive coefficients equal zero. Our theoretical results are complemented by simulations which investigate the finite sample performance of the bootstrap methodology proposed. A real-life data application is also presented.

The balanced dispatching problem in passengers transport services on demand

In the passengers’ transport services on demand, such as taxi services, a dispatching solution determines the service quality level and the incomes received by both drivers and the transport company. In the last years, related work has been mainly focused on improving the service quality. However, the drivers’ incomes balancing is still a challenge, especially for emerging economies. To address this situation, we introduce the balanced dispatching problem in passengers transport services (BDP-PTS) on demand, which seeks a dispatching solution that aims to minimize the variance of the incomes per unit of working time among the drivers. We propose five easy-to-implement online dispatching algorithms, which rely on dispatching rules obtained from the variance analysis and explore the performance measures for a dispatching solution provided by them. Those algorithms consider the BDP-PTS under an offline scenario, where all the information is revealed beforehand, and the maximum number of solicited transport services is performed. We are focused on a specific set of instances, called complete, which admit at least a feasible solution where all requested transport services are performed. Consequently, we prove the NP-completeness in the strong sense of the BDP-PTS under an offline scenario for these instances, and formulate a mixed integer quadratic programming (MIQP) model to solve it. This complexity computation status implies that no polynomial or pseudo time algorithms exist for solving it, unless P = NP, involving an important quantity of running time and memory resources to model and to resolve it in an empirical computation. Finally, computational experiments are carried out to compare the proposed online dispatching algorithms and the MIQP model on datasets of real complete instances from a Chilean transport company. The obtained results show that the proposed online dispatching algorithm based on the dispatching rule, called SRV, is able to reduce more efficiently the income dispersion among drivers within reduced running times, assigning over a 98% of total solicited transport service and allowing a practical implementation into an automated dispatching system on a basic hardware infrastructure, as it is the case of the transport companies in developing countries.

RedPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer

The recent advancement of single-cell RNA sequencing (scRNA-seq) technologies facilitates the study of cell lineages in developmental processes and cancer. In this study, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets, as well as other single-cell transcriptome data. In particular, we identified a stem cell-like subpopulation in malignant glioma cells. These cells express known proliferative markers, such as GFAP, ATP1A2, IGFBPL1, and ALDOC, and remain silenced for quiescent markers such as ID3. Furthermore, we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time. redPATH is available at https://github.com/tinglabs/redPATH.