A heterogeneous continuous age-structured model of mumps with vaccine.

In classical mumps models, individuals are generally assumed to be uniformly mixed (homogeneous), ignoring population heterogeneity (preference, activity, etc.). Age is the key to catching mixed patterns in developing mathematical models for mumps. A continuous heterogeneous age-structured model for mumps with vaccines has been developed in this paper. The stability of age-structured models is a difficult question. An explicit formula of R 0 was defined for the various mixing modes (isolation, proportional and heterogeneous mixing) with or without the vaccine. The results show that the endemic steady state is unique and locally stable if R 0 > 1 without any additional conditions. A number of numerical examples are given to support the theory.

Comparing the benthic nitrogenase activity and diazotrophic community assembly of three large river-connected freshwater lakes in eastern China.

Biological nitrogen fixation (BNF) is a crucial process that provides bioavailable nitrogen and supports primary production in freshwater lake ecosystems. However, the characteristics of diazotrophic community and nitrogenase activity in freshwater lake sediments remain poorly understood. Here, we investigated the diazotrophic communities and nitrogenase activities in the sediments of three large river-connected freshwater lakes in eastern China using 15N-isotope tracing and nifH sequencing. The sediments in these lakes contained diverse nitrogenase genes that were phylogenetically grouped into Clusters I and III. The diazotrophic communities in the sediments were dominated by stochastic processes in Hongze Lake and Taihu Lake, which had heterogeneous habitats and shallower water depths, while in Poyang Lake, which had deeper water and a shorter hydraulic retention time, the assembly of the diazotrophic community in the sediments was dominated by homogeneous selection processes. Temperature and water depth were also found the key environmental factors affecting the sediment diazotrophic communities. Sediment nitrogenase activities varied in the three lakes and within distinct regions of an individual lake, ranging from 0 to 14.58 nmol/(kg·hr). Nitrogenase activity was significantly correlated with ferric iron, total phosphorus, and organic matter contents. Our results suggested that freshwater lake sediment contain high diversity of nitrogen-fixing microorganisms with potential metabolic diversity, and the community assembly patterns and nitrogenase activities varied with the lake habitat.

Enhanced VOC emission with increased temperature contributes to the shift of O3-precursors relationship and optimal control strategy.

Assessing the impact of anthropogenic volatile organic compounds (VOCs) on ozone (O3) formation is vital for the management of emission reduction and pollution control. Continuous measurement of O3 and the major precursors was conducted in a typical light industrial city in the YRD region from 1 May to 25 July in 2021. Alkanes were the most abundant VOC group, contributing to 55.0% of TVOCs concentration (56.43 ± 21.10 ppb). OVOCs, aromatics, halides, alkenes, and alkynes contributed 18.7%, 9.6%, 9.3%, 5.2% and 1.9%, respectively. The observational site shifted from a typical VOC control regime to a mixed regime from May to July, which can be explained by the significant increase of ROx production, resulting in the transition of environment from NOx saturation to radical saturation with respect to O3 production. The optimal O3 control strategy should be dynamically changed depending on the transition of control regime. Under NOx saturation condition, minimizing the proportion of NOx in reduction could lead to better achievement of O3 alleviation. Under mixed control regime, the cut percentage gets the top priority for the effectiveness of O3 control. Five VOCs sources were identified: temperature dependent source (28.1%), vehicular exhausts (19.9%), petrochemical industries (7.2%), solvent & gasoline usage (32.3%) and manufacturing industries (12.6%). The increase of temperature and radiation would enhance the evaporation related VOC emissions, resulting in the increase of VOC concentration and the change of ROx circulation. Our results highlight determination of the optimal control strategies for O3 pollution in a typical YRD industrial city.

Effect of industrial alkene ozonolysis on atmospheric H2SO4 formation.

This study has employed the master chemical mechanism (MCM) to investigate the influence of the ozone oxidation pathways in the atmospheric formation of H2SO4 from short-chain olefins in industrialized areas. In-situ H2SO4 formation data were obtained using a high-resolution chemical ionization time-of-flight mass spectrometer, and the simulated H2SO4 concentrations calculated using updated parameters for the MCM model exhibited good agreement with observations. In the simulation analysis of different reaction pathways involved in H2SO4 formation, hydroxyl radicals were found to dominate H2SO4 production during the daytime, while olefin ozone oxidation contributed up to 65% of total H2SO4 production during the night-time. A sensitivity analysis of the H2SO4 production parameters has revealed a high sensitivity to changes in sulfur dioxide, and a relatively high sensitivity to olefins with fast ozonolysis reaction rates and bimolecular reaction rates of resulting stabilized Criegee Intermediates. A high relative humidity promotes daytime H2SO4 formation, but has an inhibiting effect during the night-time due to the different dominant reaction pathways.

Establish of air pollutants and greenhouse gases emission inventory and co-benefits of their reduction of transportation sector in Central China.

Recently, the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth. Considering air pollutants and greenhouse gases come from the same emission sources, it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China, the most polluted region in China. The inventory includes on-road mobile, non-road mobile, oil storage and transportation, and covers 9 types of air pollutants and 3 types of greenhouse gases. Based on the Long-range Energy Alternatives Planning System (LEAP) model, the emissions of pollutants were predicted for the period from 2020 to 2035 in different scenarios. Results showed that in 2020, emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, BC, OC, CO2, CH4, and N2O in Henan Province were 27.5, 503.2, 878.6, 20.1, 17.4, 222.1, 21.5, 9.4, 2.9, 92,077.9, 6.0, and 10.4 kilotons, respectively. Energy demand and pollutant emissions in Henan Province are simulated under four scenarios (Baseline Scenario (BS), Pollution Abatement Scenario (PA), Green Transportation Scenario (GT), and Reinforcing Low Carbon Scenario (RLC)). The collaborative emission reduction effect is most significant in the RLC scenario, followed by the GT scenario. By 2035, under the RLC scenario, energy consumption and emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, CO2, CH4, and N2O are projected to decrease by 72.0%, 30.0%, 55.6%, 56.0%, 38.6%, 39.7%, 51.5%, 66.1%, 65.5%, 55.4%, and 52.8%, respectively. This study provides fundamental data support for subsequent numerical simulations.

A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.

Scientific evidence sustains PM2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM2.5 particles is primordial to providing additional information on PM2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE: 1.80 µg/m3, 3.24 µg/m3, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM2.5 concentrations from 2000 to 2021. The highest PM2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations.

CRISPR-Mediated Construction of Gene-Knockout Mice for Investigating Antiviral Innate Immunity.

With the rapid development of CRISPR-Cas9 technology, gene editing has become a powerful tool for studying gene function. Specifically, in the study of the mechanisms by which natural immune responses combat viral infections, gene knockout mouse models have provided an indispensable platform. This article describes a detailed protocol for constructing gene knockout mice using the CRISPR-Cas9 system. This field focuses on the design of single-guide RNAs (sgRNAs) targeting the antiviral immune gene cGAS, embryo microinjection, and screening and verification of gene editing outcomes. Furthermore, this study provides methods for using cGAS gene knockout mice to analyze the role of specific genes in natural immune responses. Through this protocol, researchers can efficiently generate specific gene knockout mouse models, which not only helps in understanding the functions of the immune system but also offers a powerful experimental tool for exploring the mechanisms of antiviral innate immunity.

Absorption mechanism of SO3 on various alkaline absorbents in the presence of SO2.

Sulfur trioxide (SO3) as a condensable particle matter has a significant influence on atmospheric visibility, which easily arouses formation of haze. It is imperative to control the SO3 emission from the industrial flue gas. Three commonly used basic absorbents, including Ca(OH)2, MgO and NaHCO3 were selected to explore the effects of temperature, SO2 concentration on the SO3 absorption, and the reaction mechanism of SO3 absorption was further illustrated. The suitable reaction temperature for various absorbents were proposed, Ca(OH)2 at the high temperatures above 500°C, MgO at the low temperatures below 320°C, and NaHCO3 at the temperature range of 320-500°C. The competitive absorption between SO2 and SO3 was found that the addition of SO2 reduced the SO3 absorption on Ca(OH)2 and NaHCO3, while had no effect on MgO. The order of the absorption selectivity of SO3 follows MgO, NaHCO3 and Ca(OH)2 under the given conditions in this work. The absorption process of SO3 on NaHCO3 follows the shrinking core model, thus the absorption reaction continues until NaHCO3 was exhausted with the utilization rate of nearly 100%. The absorption process of SO3 on Ca(OH)2 and MgO follows the grain model, and the dense product layer hinders the further absorption reaction, resulting in low utilization of about 50% for Ca(OH)2 and MgO. The research provides a favorable support for the selection of alkaline absorbent for SO3 removal in application.

Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions.

The presence of aluminum (Al3+) and fluoride (F-) ions in the environment can be harmful to ecosystems and human health, highlighting the need for accurate and efficient monitoring. In this paper, an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum (Al3+) and fluoride (F-) ions in aqueous solutions. The proposed method involves the synthesis of sulfur-functionalized carbon dots (C-dots) as fluorescence probes, with fluorescence enhancement upon interaction with Al3+ ions, achieving a detection limit of 4.2 nmol/L. Subsequently, in the presence of F- ions, fluorescence is quenched, with a detection limit of 47.6 nmol/L. The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python, followed by data preprocessing. Subsequently, the fingerprint data is subjected to cluster analysis using the K-means model from machine learning, and the average Silhouette Coefficient indicates excellent model performance. Finally, a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions. The results demonstrate that the developed model excels in terms of accuracy and sensitivity. This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment, making it a valuable tool for safeguarding our ecosystems and public health.

Mechanism of inhibition of melanoma by fucoxanthin simulated in vitro digestion products in cell models constructed using human malignant melanoma cells (A375) and keratinocytes (HaCaT).

Recently, the increasing incidence of malignant melanoma has become a major public health concern owing to its poor prognosis and impact on quality of life. Consuming foods with potent antitumor compounds can help prevent melanoma and maintain skin health. Fucoxanthin (FX), a naturally occurring carotenoid found in brown algae, possesses antitumor properties. However, its bioavailability, safety risks, and in vivo effects and mechanisms against melanoma remain unclear. This research focused on evaluating the safety and prospective antimelanoma impact of simulated gastrointestinal digestion products (FX-ID) on HaCaT and A375 cells.The results indicate that FX-ID exerts negative effects on mitochondria in A375 cells, increases Bax expression, releases Cytochrome C, and activates cleaved caspase-3, ultimately promoting apoptosis. Additionally, FX-ID influences the mitogen-activated protein kinase (MAPK) pathway by enhancing cyclooxygenase-2 (COX-2) and nuclear factor kappa B (NF-κB) levels, consequently facilitating apoptosis and inflammation without significantly impacting HaCaT cells. These findings provide insight into inhibitory mechanism of FX-ID against melanoma, guiding the development of functional foods for prevention.

Obtaining and characterization of natural extracts from mango (Mangifera Indica) peel and its effect on the rheological behavior in new mango kernel starch hydrogels.

Hydrogels based on natural polymers have aroused interest from the scientific community. The aim of this investigation was to obtain natural extracts from mango peels and to evaluate their addition (1, 3, and 5%) on the rheological behavior of mango starch hydrogels. The total phenolic content, antioxidant activities, and phenolic acid profile of the natural extracts were evaluated. The viscoelastic and thixotropic behavior of hydrogels with the addition of natural extracts was evaluated. The total phenol content and antioxidant activity of the extracts increased significantly (p<0.05) with the variation of the ethanol-water ratio; the phenolic acid profile showed the contain of p-coumaric, ellagic, ferulic, chlorogenic acids, epicatechein, catechin, querecetin, and mangiferin. The viscoelastic behavior of the hydrogels showed that the storage modulus G' is larger than the loss modulus G'' indicating a viscoelastic solid behavior. The addition of extract improved the thermal stability of the hydrogels. 1% of the extracts increase viscoelastic and thixotropic properties, while concentrations of 3 to 5% decreased. The recovery percentage (%Re) decreases at concentrations from 0% to 1% of natural extracts, however, at concentrations from 3% to 5% increased.

Impact of vaccination on Omicron's escape variants: Insights from fine-scale modelling of waning immunity in Hong Kong.

COVID-19 vaccine-induced protection declines over time. This waning of immunity has been described in modelling as a lower level of protection. This study incorporated fine-scale vaccine waning into modelling to predict the next surge of the Omicron variant of the SARS-CoV-2 virus. In Hong Kong, the Omicron subvariant BA.2 caused a significant epidemic wave between February and April 2022, which triggered high vaccination rates. About half a year later, a second outbreak, dominated by a combination of BA.2, BA.4 and BA.5 subvariants, began to spread. We developed mathematical equations to formulate continuous changes in vaccine boosting and waning based on empirical serological data. These equations were incorporated into a multi-strain discrete-time Susceptible-Exposed-Infectious-Removed model. The daily number of reported cases during the first Omicron outbreak, with daily vaccination rates, the population mobility index and daily average temperature, were used to train the model. The model successfully predicted the size and timing of the second surge and the variant replacement by BA.4/5. It estimated 655,893 cumulative reported cases from June 1, 2022 to 31 October 2022, which was only 2.69% fewer than the observed cumulative number of 674,008. The model projected that increased vaccine protection (by larger vaccine coverage or no vaccine waning) would reduce the size of the second surge of BA.2 infections substantially but would allow more subsequent BA.4/5 infections. Increased vaccine coverage or greater vaccine protection can reduce the infection rate during certain periods when the immune-escape variants co-circulate; however, new immune-escape variants spread more by out-competing the previous strain.

Predicting influenza in China from October 1, 2023, to February 5, 2024: A transmission dynamics model based on population migration.

Introduction: Since November 2023, influenza has ranked first in reported cases of infectious diseases in China, with the outbreak in both northern and southern provinces exceeding the levels observed during the same period in 2022. This poses a serious health risk to the population. Therefore, short to medium-term influenza predictions are beneficial for epidemic assessment and can reduce the disease burden. Methods: A transmission dynamics model considering population migration, encompassing susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) was used to predict the dynamics of influenza before the Spring Festival travel rush. Results: The overall epidemic shows a declining trend, with the peak expected to occur from week 47 in 2023 to week 1 in 2024. The number of cases of A (H3N2) is greater than that of influenza B, and the influenza situation is more severe in the southern provinces compared to the northern ones. Conclusion: Our method is applicable for short-term and medium-term influenza predictions. As the spring festival travel rush approaches. Therefore, it is advisable to advocate for nonpharmaceutical interventions (NPIs), influenza vaccination, and other measures to reduce healthcare and public health burden.

Planning and adjusting the COVID-19 booster vaccination campaign to reduce disease burden.

As public health policies shifted in 2023 from emergency response to long-term COVID-19 disease management, immunization programs started to face the challenge of formulating routine booster campaigns in a still highly uncertain seasonal behavior of the COVID-19 epidemic. Mathematical models assessing past booster campaigns and integrating knowledge on waning of immunity can help better inform current and future vaccination programs. Focusing on the first booster campaign in the 2021/2022 winter in France, we used a multi-strain age-stratified transmission model to assess the effectiveness of the observed booster vaccination in controlling the succession of Delta, Omicron BA.1 and BA.2 waves. We explored counterfactual scenarios altering the eligibility criteria and inter-dose delay. Our study showed that the success of the immunization program in curtailing the Omicron BA.1 and BA.2 waves was largely dependent on the inclusion of adults among the eligible groups, and was highly sensitive to the inter-dose delay, which was changed over time. Shortening or prolonging this delay, even by only one month, would have required substantial social distancing interventions to curtail the hospitalization peak. Also, the time window for adjusting the delay was very short. Our findings highlight the importance of readiness and adaptation in the formulation of routine booster campaign in the current level of epidemiological uncertainty.

Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning.

Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Design: Retrospective analysis of OCT images and model comparison. Participants: One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Related Macular Degeneration Imaged with Swept-Source OCT (SS-OCT) study. Methods: The manual segmentations of GA lesions were conducted on structural subretinal pigment epithelium en face images from the SS-OCT images. Models were developed for 2 approximate Bayesian deep learning techniques, Monte Carlo dropout and ensemble, to assess the uncertainty of GA semantic segmentation and compared to a traditional deep learning model. Main Outcome Measures: Model performance (Dice score) was compared. Uncertainty was calculated using the formula for Shannon Entropy. Results: The output of both Bayesian technique models showed a greater number of pixels with high entropy than the standard model. Dice scores for the Monte Carlo dropout method (0.90, 95% confidence interval 0.87-0.93) and the ensemble method (0.88, 95% confidence interval 0.85-0.91) were significantly higher (P < 0.001) than for the traditional model (0.82, 95% confidence interval 0.78-0.86). Conclusions: Quantifying the uncertainty in a prediction of GA may improve trustworthiness of the models and aid clinicians in decision-making. The Bayesian deep learning techniques generated pixel-wise estimates of model uncertainty for segmentation, while also improving model performance compared with traditionally trained deep learning models. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Ex-ante life cycle evaluation of spent lithium-ion battery recovery: Modeling of complex environmental and economic impacts.

The recycling of lithium-ion batteries (LIBs) is essential for promoting the closed-loop sustainable development of the LIB industry. However, progress in LIB recycling technologies is slow. There are significant gaps between academic research and industrial application, which hinder the industrialization of new technologies and the improvement of existing ones. Here we show a universal model for spent LIB-lithium recycling (SliRec) to evaluate the applicability and upgrading potential across various recycling technologies. Instead of modeling the entire recycling process, we focus on partial processes to enable a comparative analysis of environmental and economic impacts. We find a strong correlation between lithium concentration (LC) and the advancement of recycling technologies, where higher LC is associated with a reduced carbon footprint and increased economic benefits. The implementation of high-level recycling technology can result in an 85.91% reduction in carbon footprint and a 5.97-fold increase in economic returns. Additionally, we explore the effects of technological interventions through scenario analysis, demonstrating that while low-level recycling technology faces more substantial challenges in upgrading, it holds greater potential for reducing carbon emissions (-2.38 kg CO2-eq mol-1) and enhancing economic benefits (CNY 11.04 mol-1). Our findings emphasize the significance of process modeling in evaluating the quality of spent LIB recycling technologies, and can provide comparative information for the application of emerging technologies or the upgrade of existing ones.

The effect of adsorbent textural and functional properties on model naphthenic acid adsorption.

Naphthenic acids, NAs, are a major contaminant of concern and a focus of much research around remediation of oil sand process affected waters, OSPW. Using activated carbon adsorbents are an attractive option given their low cost of fabrication and implementation. A deeper evaluation of the effect NA structural differences have on uptake affinity is warranted. Here we provide an in-depth exploration of NA adsorption including many more model NA species than have been assessed previously with evaluation of adsorption kinetics and isotherms at the relevant alkaline pH of OSPW using several different carbon adsorbents with pH buffering to simulate the behaviour of real OSPW. Uptake for the NA varied considerably regardless of the activated carbon used, ranging from 350 mg/g to near zero highlighting recalcitrant NAs. The equilibrium data was explored to identify structural features of these species and key physiochemical properties that influence adsorption. We found that certain NA will be resistant to adsorption when hydrophobic adsorbents are used. Adsorption isotherm modelling helped explore interactions occurring at the interface between NA and adsorbent surfaces. We identified the importance of NA hydrophobicity for activated carbon uptake. Evidence is also presented that indicates favorable hydrogen bonding between certain NA and surface site hydroxyl groups, demonstrating the importance of adsorbent surface functionality for NA uptake. This research highlights the challenges associated with removing NAs from OSPW through adsorption and also identifies how adsorbent surface chemistry modification can be used to increase the removal efficiency of recalcitrant NA species.

Insight into the sorption and desorption pattern of pyrrolizidine alkaloids and their N-oxides in acidic tea (Camellia sinensis) plantation soils.

Pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) are phytotoxins produced by various plant species and have been emerged as environmental pollutants. The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots. This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics. Sorption amounts for seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) in three acidic soils ranged from 2.9 to 5.9 µg/g and 1.7 to 2.8 µg/g, respectively. Desorption percentages for Sp and SpNO were from 22.2% to 30.5% and 36.1% to 43.9%. In the mixed PAs/PANOs systems, stronger sorption of PAs over PANOs was occurred in tested soils. Additionally, the Freundlich models more precisely described the sorption/desorption isotherms. Cation exchange capacity, sand content and total nitrogen were identified as major influencing factors by linear regression models. Overall, the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity. PANOs were more likely to migrate within soils and be absorbed by tea plants. It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.

On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles.

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

Generating Artificial Ribozymes Using Sparse Coevolutionary Models.

RNA ribozyme (Walter Engelke, Biologist (London, England) 49:199-203, 2002) datasets typically contain from a few hundred to a few thousand naturally occurring sequences. However, the potential sequence space of RNA is huge. For example, the number of possible RNA sequences of length 150 nucleotides is approximately 1 0 90 , a figure that far surpasses the estimated number of atoms in the known universe, which is around 1 0 80 . This disparity highlights a vast realm of sequence variability that remains unexplored by natural evolution. In this context, generative models emerge as a powerful tool. Learning from existing natural instances, these models can create artificial variants that extend beyond the currently known sequences. In this chapter, we will go through the use of a generative model based on direct coupling analysis (DCA) (Russ et al., Science 369:440-445, 2020; Trinquier et al., Nat Commun 12:5800, 2021; Calvanese et al., Nucleic Acids Res 52(10):5465-5477, 2024) applied to the twister ribozyme RNA family with three key applications: generating artificial twister ribozymes, designing potentially functional mutations of a natural wild type, and predicting mutational effects.