Viable Models Research Articles

Murine models of erythroid 5ALA synthesis disorders and their conditional synthetic lethal dependency on pyridoxine

AbstractX-linked sideroblastic anemia (XLSA) and X-linked protoporphyria (XLPP) are uncommon diseases caused by loss-of-function and gain-of-function mutations, respectively, in the erythroid form of 5-aminolevulinic acid synthetase (ALAS), ALAS2, which encodes the first enzyme in heme biosynthesis. A related congenital sideroblastic anemia (CSA) is due to mutations in SLC25A38 (solute carrier family 25 member A38), which supplies mitochondrial glycine for ALAS2 (SLC25A38–CSA). The lack of viable animal models has limited the studies on pathophysiology and development of therapies for these conditions. Here, using CRISPR-CAS9 gene editing technology, we have generated knockin mouse models that recapitulate the main features of XLSA and XLPP; and using conventional conditional gene targeting in embryonic stem cells, we also developed a faithful model of the SLC25A38-CSA. In addition to examining the phenotypes and natural history of each disease, we determine the effect of restriction or supplementation of dietary pyridoxine (vitamin B6), the essential cofactor of ALAS2, on the anemia and porphyria. In addition to the well-documented response of XLSA mutations to pyridoxine supplementation, we also demonstrate the relative insensitivity of the XLPP/EPP protoporphyrias, severe sensitivity of the XLSA models, and an extreme hypersensitivity of the SLC25A38-CSA model to pyridoxine deficiency, a phenotype that is not shared with another mouse hereditary anemia model, Hbbth3/+ β-thalassemia intermedia. Thus, in addition to generating animal models useful for examining the pathophysiology and treatment of these diseases, we have uncovered an unsuspected conditional synthetic lethality between the heme synthesis–related CSAs and pyridoxine deficiency. These findings have the potential to inform novel therapeutic paradigms for the treatment of these diseases.

Nanoscale chemical reaction exploration with a quantum magnifying glass

Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid models come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mechanism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a “quantum magnifying glass” that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction exploration. It represents an approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demonstrate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability.

Constraints on the Minimally Extended Varying Speed of Light Model Using Pantheon Dataset+

In the context of the minimally extended varying speed of light (meVSL) model, both the absolute magnitude and the luminosity distance of type Ia supernovae (SNe Ia) deviate from those predicted by general relativity (GR). Using data from the Pantheon+ survey, we assess the plausibility of various dark energy models within the framework of meVSL. Both the constant equation of state (EoS) of the dark energy model (ωCDM) and the Chevallier–Polarski–Linder (CPL) parameterization model (ω=ω0+ωa(1−a)) indicate potential variations in the cosmic speed of light at the 1−σ confidence level. For Ωm0=0.30,0.31, and 0.32 with (ω0,ωa)=(−1,0), the 1−σ range of c˙0/c0(10−13yr−1) is (−8.76, −0.89), (−11.8, 3.93), and (−14.8, −6.98), respectively. Meanwhile, the 1−σ range of c˙0/c0(10−12yr−1) for CPL dark energy models with −1.05≤ω0≤−0.95 and 0.28≤Ωm0≤0.32 is (−6.31, −2.98). The value of c at z=3 can exceed that of the present by 0.2∼3% for ωCDM models and 5∼13% for CPL models. Additionally, for viable models except for the CPL model with Ωm0=0.28, we find −25.6≤G˙0/G0(10−12yr−1)≤−0.36. For this particular model, we obtain an increasing rate of the gravitational constant within the range 1.65≤G˙0/G0(10−12yr−1)≤3.79. We obtain some models that do not require dark matter energy density through statistical interpretation. However, this is merely an effect of the degeneracy between model parameters and energy density and does not imply that dark matter is unnecessary.

Novel Mouse Model of Myocardial Infarction, Plaque Rupture, and Stroke Shows Improved Survival With Myeloperoxidase Inhibition.

Thromboembolic events, including myocardial infarction (MI) or stroke, caused by the rupture or erosion of unstable atherosclerotic plaques are the leading cause of death worldwide. Although most mouse models of atherosclerosis develop lesions in the aorta and carotid arteries, they do not develop advanced coronary artery lesions. Moreover, they do not undergo spontaneous plaque rupture with MI and stroke or do so at such a low frequency that they are not viable experimental models to study late-stage thrombotic events or to identify novel therapeutic approaches for treating atherosclerotic disease. This has stymied the development of more effective therapeutic approaches for reducing these events beyond what has been achieved with aggressive lipid lowering. Here, we describe a diet-inducible mouse model that develops widespread advanced atherosclerosis in coronary, brachiocephalic, and carotid arteries with plaque rupture, MI, and stroke. We characterized a novel mouse model with a C-terminal mutation in the scavenger receptor class B, type 1 (SR-BI), combined with Ldlr knockout (designated SR-BI∆CT/∆CT/Ldlr-/-). Mice were fed Western diet (WD) for 26 weeks and analyzed for MI and stroke. Coronary, brachiocephalic, and carotid arteries were analyzed for atherosclerotic lesions and indices of plaque stability. To validate the utility of this model, SR-BI∆CT/∆CT/Ldlr-/- mice were treated with the drug candidate AZM198, which inhibits myeloperoxidase, an enzyme produced by activated neutrophils that predicts rupture of human atherosclerotic lesions. SR-BI∆CT/∆CT/Ldlr-/- mice show high (>80%) mortality rates after 26 weeks of WD feeding because of major adverse cardiovascular events, including spontaneous plaque rupture with MI and stroke. Moreover, WD-fed SR-BI∆CT/∆CT/Ldlr-/- mice displayed elevated circulating high-sensitivity cardiac troponin I and increased neutrophil extracellular trap formation within lesions compared with control mice. Treatment of WD-fed SR-BI∆CT/∆CT/Ldlr-/- mice with AZM198 showed remarkable benefits, including >90% improvement in survival and >60% decrease in the incidence of plaque rupture, MI, and stroke, in conjunction with decreased circulating high-sensitivity cardiac troponin I and reduced neutrophil extracellular trap formation within lesions. WD-fed SR-BI∆CT/∆CT/Ldlr-/- mice more closely replicate late-stage clinical events of advanced human atherosclerotic disease than previous models and can be used to identify and test potential new therapeutic agents to prevent major adverse cardiac events.

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay.

Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).

Assessing techno-economic strategies to implement circular business models: The case of fibre-reinforced thermoset polymers

We present a comprehensive review of recent techno-economic analyses concerning the recycling of fibre-reinforced thermoset polymers, delving into the strategies employed. Additionally, we offer insights into the critical issues that necessitate consideration when evaluating solutions for valorizing these materials. Our emphasis lies in advocating for a demand-driven, entrepreneurial approach focused on identifying viable industrial applications for recycled materials, forming the foundation for sustainable techno-economic solutions and viable circular business models. We underscore the imperative for initial public intervention to regulate the recycling process, alter stakeholders’ incentives and effectively coordinate actions across involved value chains.

Sustainable Agriculture: Mitigating Greenhouse Gas Emissions through Enhanced Strip-Tilling

Agriculture plays a substantial role in greenhouse gas emissions, and finding environmentally friendly and economically viable practices is crucial for addressing climate change and ensuring the long-term sustainability of the farming industry. A modern farming practice called Enhanced Strip-till is introduced in this paper. Compared with the widespread Conventional Broadcast farming practice, the new farming practice can achieve more than a 90% decrease in nitrous oxide emissions, a 33% decrease in fertilizer use, and a 26% increase in yield. Adopting and promoting such a sustainable farming practice is essential for the agricultural sector to transition towards more environmentally friendly and economically viable models. It's a positive step forward in balancing food production with environmental conservation, aligning with the goals of sustainable agriculture and responsible resource management.

Singlet Dirac dark matter streamlined

We propose a new and compact realization of singlet Dirac dark matter within the WIMP framework. Our model replaces the standard Z 2 stabilizing symmetry with a Z 6, and uses spontaneous symmetry breaking to generate the dark matter mass, resulting in a much simplified scenario for Dirac dark matter. Concretely, we extend the Standard Model (SM) with just two new particles, a Dirac fermion (the dark matter) and a real scalar, both charged under the Z 6 symmetry. After acquiring a vacuum expectation value, the scalar gives mass to the dark matter and mixes with the Higgs boson, providing the link between the dark sector and the SM particles. With only four free parameters, this new model is extremely simple and predictive. We study the dark matter density as a function of the model's free parameters and use a likelihood approach to determine its viable parameter space. Our results demonstrate that the dark matter mass can be as large as 6 TeV while remaining consistent with all known theoretical and experimental bounds. In addition, a large fraction of viable models turns out to lie within the sensitivity of future direct detection experiments, furnishing a promising way to test this appealing scenario.

Spatial dependence of the growth factor in scalar-tensor cosmology

Scalar-tensor theories have taken on a key role in attempts to confront the growing open questions in standard cosmology. It is important to understand entirely their dynamics at perturbative level including any possible spatial dependence in their growth of large scale structures. In this work, we investigate the spatial dependence of the growth rate of scalar-tensor theories through the Mészáros equation. We confirm that at subhorizon level this dependence does not play a major role for viable models. However, we establish conditions on which this criterion is met which may be important for developing new models. In our work, we consider three specific models that exhibit spatial dependence of the growth rate at subhorizon modes, which may also be important for early Universe models.

Gravitational decoupled interior solutions from Kohler–Chao–Tikekar cosmological model

This paper is devoted to obtaining and studying two interior exact solutions of Einstein’s Field Equations (EFE) for spherical geometry in the context of gravitational decoupling (GD) through minimal geomentric deformation (MGD). We take the well-known Kohler–Chao–Tikekar cosmological solution as a seed in the framework of GD to first obtain an isotropic solution, which is decoupled again in order to obtain a second stellar anisotropic solution. Both resulting models turn out to be physically viable stellar models. Their stability is also being studied.

End-to-end dimensionality reduction and regression from 3D geological uncertainties to estimate oil reservoir simulations

Managing geological uncertainties in reservoir engineering involves significant challenges mainly due to the prohibitive computational costs of traditional simulation methods. These simulations, essential for generating geological models, often require extensive computational resources and can take days or weeks to complete. The computational load limits the number of viable models while the high dimensionality of properties compounds the challenge, and the abundance of producing wells, each associated with different objective functions, further complicates the problem. Current methodologies have focused on training an Artificial Neural Network (ANN) for each producer well to create a proxy model. Instead, we propose the development of a single ANN designed to simultaneously predict the behavior of multiple objective functions. This work proposes a novel end-to-end deep neural network that can handle 3D geological uncertainties and replicate the simulator’s response for several cumulative production curves. This approach leverages 3D convolutions to process spatial and depth dimensions within heterogeneous reservoirs, including diverse geostatistical realizations. The end-to-end solution was successfully implemented in a Brazilian offshore field and accurately replicated the simulator’s behavior and yielded results that outperformed state-of-the-art methods. The results indicate correlations that exceeded 0.95 between the proxy response and the numerical simulator. Additionally, our approach demonstrated robustness even when trained with a limited number of simulation models, and it notably reduced computational costs. The findings highlight our architecture’s capacity to integrate dimensional reduction and regression analysis within a unified framework, effectively predicting different fluid behaviors in the reservoir and showcasing robustness against high dimensionality and sparse data.

Techno-economic and environmental analysis of organic municipal solid waste for energy production

Addressing the critical conundrum of escalating municipal solid waste (MSW) and shrinking landfill spaces in urban areas, this research pioneers a sustainable approach for Bangladesh by exploring the potential of biogas production from MSW. Distinctly, it fills the research gap by providing a detailed techno-economic and environmental analysis of decentralized fixed-dome anaerobic digestion facilities in the urban context of Chittagong, Bangladesh, a domain previously underexplored. Our findings demonstrate the feasibility of converting MSW into a renewable energy source, offering an innovative solution that simultaneously tackles waste management and energy generation challenges. Each proposed plant showcases the capability to generate 536 m³ of biogas daily, sufficient to power a 50 kW gas engine and supply 44 households, thereby contributing significantly to urban waste reduction and CO2 emissions mitigation by approximately 500 tons monthly. The economic analysis reveals an attractive investment payback period of two years, underscoring the model's viability and its potential as a replicable framework for similar urban settings grappling with waste management crises. This study not only bridges a critical knowledge gap but also introduces a novel, sustainable waste-to-energy model, marking a pivotal step towards achieving energy security and environmental sustainability in developing nations.

An investigation of waqf-based Islamic micro financial institution models to identify the most effective model for Indonesia

Purpose This study aims to determine the best waqf-based Islamic microfinancial institution (IMFI) model by first determining various viable waqf-based IMFI models and then evaluating them based on certain criteria to obtain the best model. Design/methodology/approach A combination of Delphi and analytic network process (ANP) methods was used. The Delphi method was used to determine various waqf-based IMFI models and validate them, whereas the ANP method was used to evaluate those models to prioritize and find the best model. Findings The Delphi results show nine proposed waqf-based IMFI models that have commercial, social or integrated commercial-social orientation, where each could be in the form of a micro bank, micro venture capital (MV) or micro cooperative (MC). Delphi and ANP then determined the strategic, commercial and social criteria to evaluate the models. Finally, the ANP results show that the best waqf-based IMFI models are the integrated waqf-based micro bank, integrated waqf-based MV and integrated waqf-based MC. Integrated waqf-based IMFI provides Islamic microfinance services as well as Islamic social finance services, including waqf, zakat and infaq. Research limitations/implications The adoption of waqf-based IMFI could solve the structural problems of IMFIs, such as funding, low capital, mismatch, liquidity, outreach and sustainability. Practical implications The conceptual framework and method used in this study can be applied to determine and evaluate waqf-based IMFI models in other countries. Originality/value This study begins by determining various viable waqf-based IMFI models and then evaluating them to determine the priority and best waqf-based IMFI model.

Developing Time Series Forecasting Models with Generative Large Language Models

Nowadays, Generative Large Language Models (GLLMs) have made a significant impact in the field of Artificial Intelligence (AI). One of the domains extensively explored for these models is their ability as generators of functional source code for software projects. Nevertheless, their potential as assistants to write the code needed to generate and model Machine Learning (ML) or Deep Learning (DL) architectures has not been fully explored to date. For this reason, this work focuses on evaluating the extent to which different tools based on GLLMs, such as ChatGPT or Copilot, are able to correctly define the source code necessary to generate viable predictive models. The use case defined is the forecasting of a time series that reports the indoor temperature of a greenhouse. The results indicate that, while it is possible to achieve good accuracy metrics with simple predictive models generated by GLLMs, the composition of predictive models with complex architectures using GLLMs is still far from improving the accuracy of predictive models generated by human data scientists.

Ghost dark energy in Tsallis and Barrow cosmology

According to the thermodynamics-gravity conjecture, any modification to the entropy expression leads to the modified cosmological field equations. Based on this, we investigate the cosmological consequences of the modified Friedmann equations when the entropy associated with the horizon is in the form of Tsallis/Barrow entropy and the dark energy (DE) component is in the form of ghost dark energy (GDE). We perform a dynamical system analysis and see that the Tsallis GDE(TGDE) and Barrow GDE(BGDE) can exhibit a correct phase space evolution for suitable range of the free parameters(0<Δ<1 for BGDE and β<3/2 for TGDE). We find that in BGDE and TGDE (with Q=3b2H(ρD+ρm)), there exist an early radiation dominated phase of expansion which is absent for GDE model in standard cosmology. It is worth mentioning that seeking an unstable phase of matter dominated in TGDE leads to a new constraint on β (<3/2). Using the resulting range of free parameters from latter step we find that both models are capable to explain the cosmic evolution from deceleration to an accelerated phase. We observe that increasing Δ (β) parameters leads to a delay in the cosmic phase transition. We conclude that BGDE and TGDE are viable cosmological models which predict a consistent phase transition of the cosmic expansion for suitable ranges of the parameters. We also calculate the squared sound speed for both models and find out that they are unstable against perturbations. Next, we proceed the statefinder analysis and see that both models are significantly distinct from ΛCDM as well as with respect to each other at past epochs, while both of them catch the standard cosmology at far future. We also explore the impacts of the non-extensive parameters on the density perturbations and find out that the pattern of density contrast (δ) evolution in TGDE/BGDE is distinct from GDE in standard cosmology.

A Case Study of Maritime Hydrogen Energy Development in Jiangsu Province in China

This study aims to develop a systematic review of hydrogen energy storage technologies and maritime applications, intending to present an international perspective on the matter. This study analyses the role of advanced storage methods in enabling renewable energy systems integration in coastal regions, using Jiangsu Province of China as an example. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used as the research method. Data was obtained from research studies over the period 2015–2023. The analysis included a total of 96 papers from Web of Science, Scopus, IEEE Xplore, and ScienceDirect databases. Results show that Europe and East Asia led publications at the intersection of hydrogen storage and marine applications over the past decade. One of the main issues associated with maritime hydrogen adoption found in the dissertation is related to the mismatch between technological readiness levels and commercial maturity. Findings indicate promise for fuel cell integration with ships, ports, offshore wind farms, and other coastal transport sectors. However, the realization of the project depends on infrastructure development, policy support, and viable business models tailored to the marine environment. Focused demonstration projects and public-private collaboration can further progress towards robust hydrogen-based energy storage ecosystems in suitable regions like Jiangsu and beyond.

Anisotropic Durgapal–Fuloria neutron stars in f(ℛ,T2) gravity

The main purpose of this paper is to obtain physically stable stellar models coupled with anisotropic matter distribution in the context of [Formula: see text] theory. For this, we consider a static spherical geometry and formulate modified field equations containing various unknowns such as matter determinants and metric potentials. We then obtain a unique solution to these equations by employing Durgapal–Fuloria ansatz possessing a constant doublet. We also use matching criteria to calculate the values of these constants by considering the Schwarzschild exterior spacetime. Two different viable models of this modified theory are adopted to analyze the behavior of effective matter variables, anisotropy, energy conditions, compactness and redshift in the interiors of Her X-1, PSR J0348-0432, LMC X-4, SMC X-1, Cen X-3, and SAX J 1808.4-3658 star candidates. We also check the stability of these models by using three different physical tests. It is concluded that our considered stars satisfy all the physical requirements and are stable in this modified gravity for the considered parametric values.

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools.

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.

Role of ICT in Teacher Empowerment

Education plays an important role in all countries and India is no exception. It always remains a challenge and there are huge gaps to fill. As for India, poor teacher training attitudes, poor infrastructure, shortage Insufficient resources, low literacy rates and existing socio-economic conditions Indicates that a state full interface is required to access knowledge. Aiming to provide quality education for social success development. Literacy rate of socially disadvantaged and vulnerable groups are declining. Developing viable models for developing countries is essential. Recently, digital resources and ICT tools have been made available to support this learning and teaching. Using technology to improve key questions

Modeling of self-gravitating compact configurations using radial metric deformation approach

This study analyzes new analytical solution types that explore the influence of complexity on time-independent, spherically symmetric astrophysical configurations, based on a radial metric deformation scheme also known as minimal geometric deformation. We demonstrate that the complexity factor, a scalar function obtained via splitting the Riemann tensor orthogonally, exhibits an additive property. This implies that the overall complexity of a system containing two interacting fluid distributions (represented by Tμν and Φμν) is simply the sum of the individual complexities of each fluid. This work employs the radial metric deformation approach, a powerful tool for constructing astrophysically viable models of anisotropic matter, by building upon the Vaidya–Tikekar and Finch–Skea relativistic spacetimes. We observe that both the metric ansatzes produce qualitatively similar features, though the magnitudes may vary slightly, for any non-zero value of the decoupling parameter (0≤α<1). Interestingly, both models preserve their anisotropic nature despite reaching the zero-complexity limit (α=1). Finally,we delve into the physical implications of these novel solution types, examining their potential to accurately represent real compact configurations.