Stability of a stochastic brucellosis model with semi-Markovian switching and diffusion.

To explore the influence of state changes on brucellosis, a stochastic brucellosis model with semi-Markovian switchings and diffusion is proposed in this paper. When there is no switching, we introduce a critical value R s and obtain the exponential stability in mean square when R s < 1 by using the stochastic Lyapunov function method. Sudden climate changes can drive changes in transmission rate of brucellosis, which can be modelled by a semi-Markov process. We study the influence of stationary distribution of semi-Markov process on extinction of brucellosis in switching environment including both stable states, during which brucellosis dies out, and unstable states, during which brucellosis persists. The results show that increasing the frequencies and average dwell times in stable states to certain extent can ensure the extinction of brucellosis. Finally, numerical simulations are given to illustrate the analytical results. We also suggest that herdsmen should reduce the densities of animal habitation to decrease the contact rate, increase slaughter rate of animals and apply disinfection measures to kill brucella.

USP26 as a hepatitis B virus-induced deubiquitinase primes hepatocellular carcinogenesis by epigenetic remodeling.

Despite recent advances in systemic therapy for hepatocellular carcinoma (HCC), the prognosis of hepatitis B virus (HBV)-induced HCC patients remains poor. By screening a sgRNA library targeting human deubiquitinases, we find that ubiquitin-specific peptidase 26 (USP26) deficiency impairs HBV-positive HCC cell proliferation. Genetically engineered murine models with Usp26 knockout confirm that Usp26 drives HCC tumorigenesis. Mechanistically, we find that the HBV-encoded protein HBx binds to the promoter and induces the production of USP26, which is an X-linked gene exclusively expressed in the testis. HBx consequently promotes the association of USP26 with SIRT1 to synergistically stabilize SIRT1 by deubiquitination, which promotes cell proliferation and impedes cell apoptosis to accelerate HCC tumorigenesis. In patients with HBV-positive HCC, USP26 is robustly induced, and its levels correlate with SIRT1 levels and poor prognosis. Collectively, our study highlights a causative link between HBV infection, deubiquitinase induction and development of HCC, identifying a druggable target, USP26.

A program for real-time surveillance of SARS-CoV-2 genetics.

The COVID-19 pandemic brought forth an urgent need for widespread genomic surveillance for rapid detection and monitoring of emerging SARS-CoV-2 variants. It necessitated design, development, and deployment of a nationwide infrastructure designed for sequestration, consolidation, and characterization of patient samples that disseminates de-identified information to public authorities in tight turnaround times. Here, we describe our development of such an infrastructure, which sequenced 594,832 high coverage SARS-CoV-2 genomes from isolates we collected in the United States (U.S.) from March 13th 2020 to July 3rd 2023. Our sequencing protocol ('Virseq') utilizes wet and dry lab procedures to generate mutation-resistant sequencing of the entire SARS-CoV-2 genome, capturing all major lineages. We also characterize 379 clinically relevant SARS-CoV-2 multi-strain co-infections and ensure robust detection of emerging lineages via simulation. The modular infrastructure, sequencing, and analysis capabilities we describe support the U.S. Centers for Disease Control and Prevention national surveillance program and serve as a model for rapid response to emerging pandemics at a national scale.

Martingale solutions and asymptotic behaviors for a stochastic cross-diffusion three-species food chain model with prey-taxis.

The stochastic food chain model is an important model within the field of ecological research. Since existing models are difficult to describe the influence of cross-diffusion and random factors on the evolution of species populations, this work is concerned with a stochastic cross-diffusion three-species food chain model with prey-taxis, in which the direction of predators' movement is opposite to the gradient of prey, i.e., a higher density of prey. The existence and uniqueness of martingale solutions are established in a Hilbert space by using the stochastic Galerkin approximation method, the tightness criterion, Jakubowski's generalization of the Skorokhod theorem, and the Vitali convergence theorem. Furthermore, asymptotic behaviors around the steady states of the stochastic cross-diffusion three-species food chain model in the time mean sense are investigated. Finally, numerical simulations are carried out to illustrate the results of our analysis.

Accurate assembly of multiple RNA-seq samples with Aletsch.

MOTIVATION: High-throughput RNA sequencing has become indispensable for decoding gene activities, yet the challenge of reconstructing full-length transcripts persists. Traditional single-sample assemblers frequently produce fragmented transcripts, especially in single-cell RNA-seq data. While algorithms designed for assembling multiple samples exist, they encounter various limitations. RESULTS: We present Aletsch, a new assembler for multiple bulk or single-cell RNA-seq samples. Aletsch incorporates several algorithmic innovations, including a "bridging" system that can effectively integrate multiple samples to restore missed junctions in individual samples, and a new graph-decomposition algorithm that leverages "supporting" information across multiple samples to guide the decomposition of complex vertices. A standout feature of Aletsch is its application of a random forest model with 50 well-designed features for scoring transcripts. We demonstrate its robust adaptability across different chromosomes, datasets, and species. Our experiments, conducted on RNA-seq data from several protocols, firmly demonstrate Aletsch's significant outperformance over existing meta-assemblers. As an example, when measured with the partial area under the precision-recall curve (pAUC, constrained by precision), Aletsch surpasses the leading assemblers TransMeta by 22.9%-62.1% and PsiCLASS by 23.0%-175.5% on human datasets. AVAILABILITY AND IMPLEMENTATION: Aletsch is freely available at https://github.com/Shao-Group/aletsch. Scripts that reproduce the experimental results of this manuscript is available at https://github.com/Shao-Group/aletsch-test.

Education and training of acupuncture-moxibustion professionals from the perspective of medicine-engineering interdiscipline: case analysis of China's higher education reform and transformation under the background of "emerging medical education". / åŒ»å·¥ç»“åˆè§†è§’ä¸‹é’ˆç¸ä¸“ä¸šäººæ‰çš„æ•™è‚²ä¸ŽåŸ¹å ».

A sustainable training system for acupuncture-moxibustion and tuina professionals, integrating "medicine, industry, education and research" is established, under the main framework of the medicine-engineering interdiscipline, and with the consideration of the issues of medicine, the application of engineering technology, the thinking approaches of sciences, and the collaboration of business studies. It is the potential power to support the development of traditional medicine. Through analyzing the difficulties of the medicine-engineering interdiscipline of acupuncture specialty, and in association with the experiences of the early-stage development of the collaboration between medicine and engineering, the paper presents the cases of China's higher education reform and transformation under the background of "emerging medical education" so as to explore a replicable personnel training mode.

Enzymatically Bioactive Nucleus Pulposus Matrix Hydrogel Microspheres for Exogenous Stem Cells Therapy and Endogenous Repair Strategy to Achieve Disc Regeneration.

Exogenous stem cell therapy and endogenous repair has shown great potential in intervertebral disc regeneration. However, limited nutrients and accumulation of lactate largely impair the survival and regenerative capacity of implanted stem cells and endogenous nucleus pulposus cells (NPCs). Herein, an injectable hydrogel microsphere (LMGDNPs) have been developed by immersing lactate oxidase (LOX)-manganese dioxide (MnO2 ) nanozyme (LM) into glucose-enriched decellularized nucleus pulposus hydrogel microspheres (GDNPs) through a microfluidic system. LMGDNPs showed a delayed release profile of LOX and satisfactory enzymatic capacity in consuming lactate. Mesenchymal stem cells (MSCs) plated on LMGDNPs exhibited better cell viability than cells on GelMA and decellularized nucleus pulposus microspheres (DNP) and showed a obviously increased NPCs phenotype. LMGDNPs prevented MSCs and NPCs death and promoted extracellular matrix synthesis by exhausting lactate. It is determined that LMGDNPs promoted NPCs autophagy by activating transforming growth factor ß2 overlapping transcript 1 (TGFB2-OT1), relying on the nanozyme. MSCs-loaded LMGDNPs largely preserved disc hydration and alleviated matrix degradation in vivo. Summarily, LMGDNPs promoted cell survival and matrix regeneration by providing a nutrient supply, exhausting lactate, and activating autophagy via TGFB2-OT1 and its downstream pathway and may serve as an ideal delivery system for exogenous stem cell therapy and endogenous repair.

Transcript assembly and annotations: Bias and adjustment.

Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. We investigate the impact of annotations on transcript assembly. Surprisingly, we observe that opposite conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.

An In Silico Analysis of PCR-Based Monkeypox Virus Detection Assays: A Case Study for Ongoing Clinical Surveillance.

The 2022 global Mpox outbreak swiftly introduced unforeseen diversity in the monkeypox virus (MPXV) population, resulting in numerous Clade IIb sublineages. This propagation of new MPXV mutations warrants the thorough re-investigation of previously recommended or validated primers designed to target MPXV genomes. In this study, we explored 18 PCR primer sets and examined their binding specificity against 5210 MPXV genomes, representing all the established MPXV lineages. Our results indicated that only five primer sets resulted in almost all perfect matches against the targeted MPXV lineages, and the remaining primer sets all contained 1-2 mismatches against almost all the MPXV lineages. We further investigated the mismatched primer-genome pairs and discovered that some of the primers overlapped with poorly sequenced and assembled regions of the MPXV genomes, which are consistent across multiple lineages. However, we identified 173 99% genome-wide conserved regions across all 5210 MPXV genomes, representing 30 lineages/clades with at least 80% lineage-specific consensus for future primer development and primer binding evaluation. This exercise is crucial to ensure that the current detection schemes are robust and serve as a framework for primer evaluation in clinical testing development for other infectious diseases.

Finite-time contraction stability of a stochastic reaction-diffusion dengue model with impulse and Markov switching.

From the perspective of prevention and treatment of dengue, it is important to minimize the number of infections within a limited time frame. That is, the study of finite time contraction stability (FTCS) of dengue system is a meaningful topic. This article proposes a dengue epidemic model with reaction-diffusion, impulse and Markov switching. By constructing an equivalent system, the well-posedness of the positive solution is proved. The main result is that sufficient conditions to guarantee the finite time contraction stability of the dengue model are acquired based on the average pulse interval method and the bounded pulse interval method. Furthermore, the numerical findings indicate the influences of impulse, control strategies and noise intensity on the FTCS.

The deubiquitinase ZRANB1 is an E3 ubiquitin ligase for SLC7A11 and regulates ferroptotic resistance.

The dependency of cancer cells on iron increases their susceptibility to ferroptosis, thus providing new opportunities for patients with treatment-resistant tumors. However, we show that lipid peroxidation, a hallmark of ferroptosis, was found in various areas of patient samples, indicating the potential resistance of ferroptosis. Using whole deubiquitinases (DUBs) sgRNA screening, we found that loss of ZRANB1 confers cancer cell resistance to ferroptosis. Intriguingly, functional studies revealed that ZRANB1 ubiquitinates and represses SLC7A11 expression as an E3 ubiquitin ligase and that ZRANB1 inhibits glutathione (GSH) synthesis through SLC7A11 degradation, leading to elevated lipid peroxidation and ferroptosis. Deletion of the region (residues 463-584) abolishes the E3 activity of ZRANB1. Moreover, we show that ZRANB1 has lower expression in tumors, which is positively correlated with lipid peroxidation. Collectively, our results demonstrate the role of ZRANB1 in ferroptosis resistance and unveil mechanisms involving modulation of E3 ligase activity through an unconventional catalytic domain.

ZHX2 deficiency enriches hybrid MET cells through regulating E-cadherin expression.

Growing evidence indicates that the epithelial to mesenchymal (E/M) hybrid state plays a key role in tumorigenesis. Importantly, a hybrid mesenchymal to epithelial transition (MET) state in which individual cells express both epithelial and mesenchymal markers was recently identified in vivo, further strengthening the bonds between the hybrid EMT state and cancer progression. However, the role and the molecular mechanisms by which the hybrid MET state is maintained in triple-negative breast cancer cells (TNBC) remain elusive. Here, we find that loss of ZHX2 expression results in the hybrid MET phenotype in mesenchymal TNBC cells. Mechanistically, through directly binding to the CDH1 promoter, depletion of ZHX2 specifically reactivates expression of CDH1 encoding E-cadherin, an epithelial marker that is crucial for maintaining epithelial phenotype. Functionally, loss of ZHX2 expression enriches the hybrid MET cells and inhibits the migration and dissemination of TNBC cells or organoids, which could be reversed by restoration of E-cadherin. Moreover, depletion of ZHX2 suppresses lung metastasis in preclinical models of TNBC. In patients with TNBC, ZHX2 expression was amplified and negatively correlated with the expression of E-cadherin. These findings suggest that loss of ZHX2 promotes the hybrid MET state to impair TNBC progression.

Dynamics and calculation of the basic reproduction number for a nonlocal dispersal epidemic model with air pollution.

In order to reflect the dispersal of pollutants in non-adjacent areas and the large-scale movement of individuals, this paper proposes an epidemic model of nonlocal dispersal with air pollution, where the transmission rate is related to the concentration of pollutants. This paper checks the uniqueness and existence of the global positive solution and defines the basic reproduction number, R0. We simultaneously explore the global dynamics: when R0<1, the disease-free stable point is global asymptotic stability; when R0>1, the disease is uniformly persistent. Additionally, in order to approximate R0, a numerical method has been introduced. Illustrative examples are used to verify the theoretical outcomes and show the effect of the dispersal rate on the basic reproduction number R0.

Sliding mode dynamics and optimal control for HIV model.

Considering the drug treatment strategy in both virus-to-cell and cell-to-cell transmissions, this paper presents an HIV model with Filippov control. Given the threshold level $ N_t $, when the total number of infected cells is less or greater than threshold level $ N_t $, the threshold dynamics of the model are studied by using the Routh-Hurwitz Criterion. When the total number of infected cells is equal to $ N_t $, the sliding mode equations are obtained by Utkin equivalent control method, and the dynamics are studied. In addition, the optimal control strategy is introduced for the case that the number of infected cells is greater than $ N_t $. By dynamic programming, the Hamilton-Jacobi-Bellman (HJB) equation is constructed, and the optimal control is obtained. Numerical simulations are presented to illustrate the validity of our results.

Transcript Assembly and Annotations: Bias and Adjustment.

Motivation: Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. Results: We investigate the impact of annotations on transcript assembly. We observe that conflicting conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.

Positivity-Preserving Numerical Method and Relaxed Control for Stochastic Susceptible-Infected-Vaccinated Epidemic Model with Markov Switching.

The stochastic susceptible-infected-vaccinated (SIV) epidemic model includes a nonlinear term, making it difficult to obtain analytical solutions. Thus, numerical approximation schemes are an important tool for predicting the dynamics of infectious diseases and establishing optimal control strategies. However, the convergence rate of the existing numerical methods [e.g., Euler-Maruyama (EM) and truncated EM scheme] is only 1/2 order of the time step Δt. This article describes the construction of a logarithmic truncated EM scheme that achieves order-1 convergence and ensures positive numerical solutions of the stochastic SIV epidemic model. The existence of an invariant measure is proved for the stochastic SIV epidemic model with Markov switching. In addition, relaxed controls for the stochastic SIV epidemic model are investigated by using the Markov chain approximation method. It is demonstrated that the approximation schemes converge to the optimal strategy as the mesh size goes to zero. Finally, the results of numerical examples are presented to illustrate the theoretical results derived in this article.

Positivity Preserving Truncated Euler-Maruyama Scheme for the Stochastic Age-Structured HIV/AIDS Model.

Since the analytical solution of the stochastic age-structured human immunodeficiency virus/acquired immune deficiency syndrome model is difficult to solve, establishing an efficient numerical approximation is an important way to predict the dynamic behavior of the model. In this article, a full-discrete scheme is proposed, where the Galerkin finite element method and the positivity preserving truncated Euler-Maruyama scheme are used to discrete the age variable and the time variable, respectively. The error between the numerical solution and the analytical solution is analyzed. Finally, the theoretical results are illustrated by the numerical examples.

Stationary distribution and optimal control of a stochastic population model in a polluted environment.

This paper is concerned with a stochastic population model in a polluted environment. First, within the framework of Lyapunov method, the existence and uniqueness of a global positive solution of the model are proposed, and the sufficient conditions are established for existence of an ergodic stationary distribution of the positive solution. Second, the control strategy is introduced into the stochastic population model in a polluted environment. By using Pontryagin's maximum principle, the first-order necessary conditions are derived for the existence of optimal control. Finally, some numerical simulations are presented to illustrate the analytical results.

Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.

From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.

LGP2 is essential for zebrafish survival through dual regulation of IFN antiviral response.

In mammals, LGP2 is the enigmatic RLR family member, being initially believed as an inhibitor of RLR-triggered IFN response but subsequently as an activator of MDA5 signaling and an inhibitor of RIG-I signaling. The contradiction happens to fish LGP2. Here, we generate a lgp2 loss-of-function (lgp2 lof/lof ) zebrafish mutant, which is highly susceptible to SVCV infection, displaying an initially decreased activation of IFN response and a following increased one. Mechanistically, zebrafish LGP2 functions as the essential activator of IFN response dependent on MDA5 at the early stage of viral infection but as a negative regulator by impairing mRNA levels of tbk1 and ikki at the late stage of viral infection. The function switch of LGP2 is related to cellular IFN production during viral infection. Our data demonstrate that zebrafish LGP2 is a key homeostatic regulator of IFN response and thus essential for zebrafish survival against SVCV infection.