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Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune systems capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

Organic molecules in the environment oxidatively degrade by a variety of free radical, microbial, and biogeochemical pathways. A significant pathway is heterogeneous autoxidation, in which degradation occurs via a network of carbon and oxygen centered free radicals. Recently, we found evidence for a new heterogeneous autoxidation mechanism of squalene that is initiated by hydroxyl (OH) radical addition to a carbon-carbon double bond and apparently propagated through pathways involving Criegee Intermediates (CI) produced from β-hydroxy peroxy radicals (β-OH-RO2•). It remains unclear, however, exactly how CI are formed from β-OH-RO2•, which could occur by a unimolecular or bimolecular pathway. Combining kinetic models and multiphase OH oxidation measurements of squalene, we evaluate the kinetic viability of three mechanistic scenarios. Scenario 1 assumes that CI are formed by the unimolecular bond scission of β-OH-RO2•, whereas Scenarios 2 and 3 test bimolecular pathways of β-OH-RO2• to yield CI. Scenario 1 best replicates the entire experimental data set, which includes effective uptake coefficients vs [OH] as well as the formation kinetics of the major products (i.e., aldehydes and secondary ozonides). Although the unimolecular pathway appears to be kinetically viable, future high-level theory is needed to fully explain the mechanistic relationship between CI and β-OH-RO2• in the condensed phase.

The lungs are supplied by both the pulmonary arteries carrying deoxygenated blood originating from the right ventricle and the bronchial arteries carrying oxygenated blood downstream from the left ventricle. However, this effect of dual blood supply has never been investigated using PET, partially because the temporal resolution of conventional dynamic PET scans is limited. The advent of PET scanners with a long axial field of view, such as the uEXPLORER total-body PET/CT system, permits dynamic imaging with high temporal resolution (HTR). In this work, we modeled the dual-blood input function (DBIF) and studied its impact on the kinetic quantification of normal lung tissue and lung tumors using HTR dynamic PET imaging. Methods: Thirteen healthy subjects and 6 cancer subjects with lung tumors underwent a dynamic 18F-FDG scan with the uEXPLORER for 1 h. Data were reconstructed into dynamic frames of 1 s in the early phase. Regional time-activity curves of lung tissue and tumors were analyzed using a 2-tissue compartmental model with 3 different input functions: the right ventricle input function, left ventricle input function, and proposed DBIF, all with time delay and dispersion corrections. These models were compared for time-activity curve fitting quality using the corrected Akaike information criterion and for differentiating lung tumors from lung tissue using the Mann-Whitney U test. Voxelwise multiparametric images by the DBIF model were further generated to verify the regional kinetic analysis. Results: The effect of dual blood supply was pronounced in the high-temporal-resolution time-activity curves of lung tumors. The DBIF model achieved better time-activity curve fitting than the other 2 single-input models according to the corrected Akaike information criterion. The estimated fraction of left ventricle input was low in normal lung tissue of healthy subjects but much higher in lung tumors (∼0.04 vs. ∼0.3, P < 0.0003). The DBIF model also showed better robustness in the difference in 18F-FDG net influx rate [Formula: see text] and delivery rate [Formula: see text] between lung tumors and normal lung tissue. Multiparametric imaging with the DBIF model further confirmed the differences in tracer kinetics between normal lung tissue and lung tumors. Conclusion: The effect of dual blood supply in the lungs was demonstrated using HTR dynamic imaging and compartmental modeling with the proposed DBIF model. The effect was small in lung tissue but nonnegligible in lung tumors. HTR dynamic imaging with total-body PET can offer a sensitive tool for investigating lung diseases.

BackgroundKinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort.Methods18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ([Formula: see text] was also calculated from the multi-reference tissue model (MRTM).ResultsAmyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with [Formula: see text] analysis. [Formula: see text]and VT from kinetic models were correlated (r² = 0.46, P

Eukaryotic mRNAs are transcribed, processed, translated, and degraded in different subcellular compartments. Here, we measured mRNA flow rates between subcellular compartments in mouse embryonic stem cells. By combining metabolic RNA labeling, biochemical fractionation, mRNA sequencing, and mathematical modeling, we determined the half-lives of nuclear pre-, nuclear mature, cytosolic, and membrane-associated mRNAs from over 9000 genes. In addition, we estimated transcript elongation rates. Many matured mRNAs have long nuclear half-lives, indicating nuclear retention as the rate-limiting step in the flow of mRNAs. In contrast, mRNA transcripts coding for transcription factors show fast kinetic rates, and in particular short nuclear half-lives. Differentially localized mRNAs have distinct rate constant combinations, implying modular regulation. Membrane stability is high for membrane-localized mRNA and cytosolic stability is high for cytosol-localized mRNA. mRNAs encoding target signals for membranes have low cytosolic and high membrane half-lives with minor differences between signals. Transcripts of nuclear-encoded mitochondrial proteins have long nuclear retention and cytoplasmic kinetics that do not reflect co-translational targeting. Our data and analyses provide a useful resource to study spatiotemporal gene expression regulation. Synopsis: Transcriptome-wide measurement of subcellular mRNA dynamics in mouse ES cells reveals that nuclear retention is the rate-limiting step in the life-cycle of most mRNAs and provides a resource for understanding spatiotemporal gene expression regulation. This study quantified nuclear retention, cytosolic stability and membrane stability of mRNAs transcribed from more than 9000 genes. Mature mRNAs exhibit long nuclear half-lives with a median of 78 min, indicating that nuclear retention is the primary rate-limiting step in mRNA flow. Cytosol-localized transcripts have a median cytosolic half-life of 13 min, while membrane-localized transcripts have median cytosolic and membrane half-lives of 1 min and 20 min, respectively. Functionally related genes show similar kinetic profiles, with mRNAs encoding transcription factors having short nuclear and cytosolic half-lives, while those encoding mitochondrial and ribosomal proteins have long nuclear retention and overall high stability. Transcriptome-wide measurement of subcellular mRNA dynamics in mouse ES cells reveals that nuclear retention is the rate-limiting step in the life-cycle of most mRNAs and provides a resource for understanding spatiotemporal gene expression regulation. [ABSTRACT FROM AUTHOR]

Heparosan, an unsulfated polysaccharide, plays a pivotal role as a primary precursor in the biosynthesis of heparin—an influential anticoagulant with diverse therapeutic applications. To enhance heparosan production, the utilization of metabolic engineering in nonpathogenic microbial strains is emerging as a secure and promising strategy. In the investigation of heparosan production by recombinant Bacillus megaterium, a kinetic modeling approach was employed to explore the impact of initial substrate concentration and the supplementation of precursor sugars. The adapted logistic model was utilized to thoroughly analyze three vital parameters: the B. megaterium growth dynamics, sucrose utilization, and heparosan formation. It was noted that at an initial sucrose concentration of 30 g L−1 (S1), it caused an inhibitory effect on both cell growth and substrate utilization. Intriguingly, the inclusion of N‐acetylglucosamine (S2) resulted in a significant 1.6‐fold enhancement in heparosan concentration. In addressing the complexities of the dual substrate system involving S1 and S2, a multi‐substrate kinetic models, specifically the double Andrew's model was employed. This approach not only delved into the intricacies of dual substrate kinetics but also effectively described the relationships among the primary state variables. Consequently, these models not only provide a nuanced understanding of the system's behavior but also serve as a roadmap for optimizing the design and management of the heparosan production method. [ABSTRACT FROM AUTHOR]

Purpose: [18F]SynVesT-1, a positron emission tomography (PET) radiotracer for the synaptic vesicle glycoprotein 2A (SV2A), demonstrates kinetics similar to [11C]UCB-J, with high brain uptake, fast kinetics fitting well with the one-tissue compartment (1TC) model, and excellent test-retest reproducibility. Challenges arise due to the similarity between k2 and (efflux rate of the reference region), when applying the simplified reference tissue model (SRTM) and related methods in [11C]UCB-J studies to accurately estimate . This study evaluated the suitability of these methods to estimate [18F]SynVesT-1 binding using centrum semiovale (CS) or cerebellum (CER) as reference regions. Method: Seven healthy participants underwent 120-min PET scans on the HRRT scanner with [18F]SynVesT-1. Six participants underwent test and retest scans. Arterial blood sampling and metabolite analysis provided input functions for the 1TC model, serving as the gold standard for kinetic parameters values. SRTM, coupled SRTM (SRTMC) and SRTM2 estimated were applied to estimate (ref: CS) and DVRCER(ref: CER) values. For SRTM2, the population average of was determined from the 1TC model applied to the reference region. Test-retest variability and minimum scan time were also calculated. Results: The 1TC k2 (1/min) values for CS and CER were 0.031 ± 0.004 and 0.021 ± 0.002, respectively. Although SRTMC was much higher than 1TC , SRTMC underestimated BPND(ref: CS) and DVRCER by an average of 3% and 1% across regions, respectively, due to similar bias in k2 and estimation. SRTM underestimated BPND(ref: CS) by an average of 3%, but with the CER as reference region, SRTM estimation was unstable and DVRCER underestimation varied by region (mean 10%). Using population average values, SRTM2 BPND and DVRCER showed the best agreement with 1TC estimates. Conclusion: Our findings support the use of population value in SRTM2 with [18F]SynVesT-1 for the estimation of or DVRCER, regardless of the choice of reference region. [ABSTRACT FROM AUTHOR]

Detailed kinetic models play a crucial role in comprehending and enhancing chemical processes. A cornerstone of these models is accurate thermodynamic and kinetic properties, ensuring fundamental insights into the processes they describe. The prediction of these thermochemical and kinetic properties presents an opportunity for machine learning, given the challenges associated with their experimental or quantum chemical determination. This study reviews recent advancements in predicting thermochemical and kinetic properties for gas-phase, liquid-phase, and catalytic processes within kinetic modeling. We assess the state-of-the-art of machine learning in property prediction, focusing on three core aspects: data, representation, and model. Moreover, emphasis is placed on machine learning techniques to efficiently utilize available data, thereby enhancing model performance. Finally, we pinpoint the lack of high-quality data as a key obstacle in applying machine learning to detailed kinetic models. Accordingly, the generation of large new datasets and further development of data-efficient machine learning techniques are identified as pivotal steps in advancing machine learning’s role in kinetic modeling. [ABSTRACT FROM AUTHOR]

This study focuses on the selection and evaluation of a kinetic model for the release of vitamin C from different delivery systems, including microcapsules, hydrogels, and a hybrid system combining both. The microcapsules were synthesized from a 2% sodium alginate solution and with vitamin C incorporated in selected formulations. Hydrogels were obtained through photopolymerization using poly(ethylene glycol) diacrylate and polyvinyl alcohol, with and without the addition of vitamin C. The hybrid system incorporated the vitamin C-containing microcapsules within the hydrogel matrix. Physicochemical properties, such as density, porosity, and water vapor transmission rate (WVTR), were evaluated. Kinetic studies of vitamin C release were conducted under dynamic and static conditions, and the experimental data were fitted to six different kinetic models: zero-order, first-order, second-order, Higuchi, Korsmeyer–Peppas, and Hixson–Crowell. The Higuchi and Korsmeyer–Peppas models provided the best fit for most systems, indicating that the release is predominantly controlled by diffusion and, in dynamic conditions, swelling of the matrix. The hybrid system, while exhibiting slower release than the microcapsules and hydrogel alone, demonstrated more controlled and sustained release, which is advantageous for applications requiring prolonged action. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Total‐body positron emission tomography (PET), already well‐established in the pre‐clinical setting, makes it possible to study multi‐parameters in biological systems as a whole, rather than focusing on single tissues analysis. Simultaneous kinetic analysis of multiple organs poses some daunting new challenges.To explore quantifying the pharmacokinetics of Na[18F]F in multiple dissimilar murine organs simultaneously in vivo with total‐body PET imaging using different compartmental models for each organ and a shared cardiovascular system.Six mice underwent a 60‐min total‐body PET scan following intravenous bolus injection of Na[18F]F. Compartmental models were constructed for each organ (heart, lungs, liver, kidneys, and bone) using an image derived input function. Non‐linear least squares fitting of a model that connects the five organs to a shared cardiovascular system was used to analyze both the first 3 min of data and the full hour. Analysis was repeated 5000 times using different initial parameter values for each duration, permitting analysis of correlations between parameters.The models give a good qualitative account of the activity curves irrespective of the duration of the data; however, the quality of the fits to 3 min of data (average χν2$\chi _\nu ^2$ is 2.72) was generally better. Comparison of perfusion values to literature values was possible for the liver and lungs with the former (liver, 0.540 ± 0.177 mL/ml/min) being well‐above expectations and the latter (lungs, 0.184 ± 0.413 mL/ml/min) in rough agreement. Correlations between microparameter values (especially affecting k2) caused very noticeable problems for data modeling from both the kidneys and the femur.The present study demonstrates an approach to performing kinetic modeling for multiple organs simultaneously with Na[18F]F. The observed correlations between microparameter values remain a challenge. Nonetheless, many microparameters can be estimated reliably with a quantitative analysis of perfusion being possible for some organs. [ABSTRACT FROM AUTHOR]

Herein, laboratory experiments are conducted to evaluate the effect of sulfur and lanthanum content in the steel on the modification of inclusions. The amount, composition, morphology, and size of nonmetallic inclusions in the steel are observed using an automatic scanning electron microscope. Results show that as the lanthanum content in the steel increased from 0 to 72 ppm, the inclusions in the steel with 12 ppm sulfur are modified in the following order: Al2O3–CaO–MgO → LaAlO3 → LaAlO3–La2O2S → La2O2S. Similarly, when the sulfur content is 27 ppm, inclusions transformed in the order of Al2O3–CaO–MgO → LaAlO3 → LaAlO3–La2O2S → La2O2S → La2O2S–LaxS → LaxS as the lanthanum content increases from 0 to 150 ppm. Thermodynamic analysis using FactSage based on a private database is employed to reveal the mechanism for the modification of inclusions. Additionally, a kinetic model is established to study the transformation rate of inclusions in the steel after the addition of lanthanum and sulfur. Liquid Al2O3–CaO–MgO inclusions disappear after 800 s when the steel contains 12 ppm sulfur and 12 ppm lanthanum. The composition of inclusions is 53%LaAlO3–47%La2O2S at 1800 s, while it is 100%La2O2S and 80%La2O2S–18%LaxS–2%CaS when the lanthanum content is 33 and 72 ppm, respectively. [ABSTRACT FROM AUTHOR]

The experimental results of the present study show that phenol had a certain inhibitory effect on its utilization by phenol-acclimatized activated sludge (PAAS), which was the inoculum for the microbial fuel cell (MFC) inoculation. In co-metabolic study, utilization of para-nitrophenol (PNP) at low concentrations (< 20 mg L−1) when used with phenol at a fixed concentration (250 mg L−1) favorably proceeded. The behavior was interpreted in terms of carbon catabolite repression (CCR), indicating phenol (250 mg L−1) positively affected consumption of PNP (< 20 mg L−1). The calculated values of degradation rate show the necessity of phenol presence in the system where phenol acted on the inoculum's ability to withstand the inhibitory effect of PNP. The MFC functionality in electricity generation is also definable by considering CCR applicability and the results show that the negative effect of PNP was repressed by the presence of phenol. For instance, 20 mg L−1 PNP + 250 mg L−1 phenol yielded the highest power density (66.2 mW m−2) and the lowest internal resistance (189 Ω). The PAAS performance was characterized to evaluate cells' capacity in utilizing inhibitory substrates, and several different models were used. The relevant kinetic parameters are described in terms of PAAS affinity toward the substrate (ks) and the microbe's sensitivity in responding to the toxic substrate (ki). Luong and Aiba equations were chosen to describe MFC behavior when the PAAS utilized phenol as the sole substrate. While Haldane model was more capable of addressing co-metabolic degradation of PNP. [ABSTRACT FROM AUTHOR]

Pathways for NH3 decomposition to N2 and N2H4 by atmospheric pressure nonthermal plasma are analyzed using a combination of molecular beam mass spectrometry measurements and zero-dimensional kinetic modeling. Experimental measurements show that NH3 conversion and selectivity towards N2 formation scale monotonically with the specific energy input into the plasma with ~ 100% selectivity to N2 formation achieved at specific energy inputs above 0.12 J cm−3 (3.1 eV (molecule NH3)−1). The kinetic model recovers these trends, although it underpredicts N2 selectivity at low specific energy input. These discrepancies can be explained by the underestimation of reaction rate coefficients for reactions that consume N2Hx species in collisions with H radicals and/or radial nonuniformities in power deposition, gas temperature, and species concentrations that are not represented by the plug flow approximation used in the model. The kinetic model shows that N2 formation proceeds through N2Hx decomposition pathways rather than NHx decomposition pathways in low temperature, atmospheric pressure plasma. Higher selectivity toward N2 production can be achieved by operating at higher NH3 conversion and with a higher gas temperature. The high energy cost of NH3 decomposition by atmospheric pressure nonthermal plasma found in this work (25–50 eV (molecule NH3 converted)−1; 17–33 eV (molecule H2 formed)−1) is a result of the energy requirement for electron-impact dissociation of NH3 and the significant re-formation of NH3 by three-body recombination reactions between NH2 and H. [ABSTRACT FROM AUTHOR]

This paper presents the results of investigations on the kinetic modeling of Brilliant Blue FCF (BB) discoloration reactions in aqueous solutions with different ozone concentrations and pH conditions. Kinetic studies involve knowledge of the structure and properties of dye and ozone, as well as of the experimental conditions. In general, scientists admit that the predominant oxidation pathway is direct (by free oxygen atoms) or indirect (by free hydroxyl radicals); this will depend on influencing factors such as the physicochemical properties of the dye, the pH of the aqueous solution, ozone concentration, reaction time, and the contact mode with/without stirring. In this experimental research, two pathways were chosen following CBB = f(t)—1. a constant dye concentration and different ozone concentrations, in the concentration range of 100–250 mg/L, in three pH media (acidic, neutral, and basic), with and without stirring; 2. a constant concentration of ozone and different dyes in the concentration range of 2.5–10 mg/L, under the conditions of point 1. With the obtained experimental data, the curves CBB = f(t) were drawn and processed according to the integral method of classical kinetics, based on first- and second-order equations. Unfortunately, this simple procedure did not give any results for the pH values studied. The rate constants were negative, and/or the reaction order depended on the initial conditions. Due to its structure, the BB dye has several chromophore groups, and thus multiple attack centers, resulting in several oxidation by-products, which is why the 1H-NMR spectrum was recorded for the discoloration of BB with ozone. Since the stoichiometry of the overall oxidation reaction, as well as the relationship between the rate constant and the reaction conditions mentioned above, is not known, a kinetic model based on mass transfer coupled with a chain reaction in the bulk liquid phase was proposed and successfully tested at pH = 7. This research approach also involves the consolidation of the theoretical bases of the ozonation process through the kinetic study carried out, as well as the proposal of a kinetic model. These systematics lead to results that are applicable to other aqueous systems that are impure with dyes, allowing for generalizations and the development of the field, ensuring the sustainability of the research. [ABSTRACT FROM AUTHOR]

Dialkyl carbonates (DACs) own an environmentally friendly synthesis route, making them potential candidates as alternative fuels. However, for DACs to be widely accepted as an alternative fuel, a comprehensive understanding of their combustion behavior is essential. Dipropyl carbonate (DPrC) represents a transition from short‐chain to mid‐chain carbonates, understanding its combustion behaviors holds significance in unraveling the combustion chemistry of carbonates. In this study, the oxidation of DPrC was investigated with the initial fuel mole fraction of 0.5% at three equivalence ratios of 0.5, 1.0, and 2.0 within a temperature range of 550–1100 K in a jet‐stirred reactor for the first time. Gas chromatography was utilized for the quantitative detection of reactants, intermediates, and products. A detailed DPrC mechanism was first developed, and good agreements between measurements and simulations were obtained. A notable negative temperature coefficient (NTC) behavior was first observed in the oxidation of DACs. Such NTC phenomenon occurred at fuel‐lean conditions in the temperature range of 620–660 K, while only a weak low‐temperature consumption was observed at the stoichiometric condition. Kinetic modeling studies showed that this unique low‐temperature chemistry of DPrC can be attributed to the differences in the RO2 isomerization reactions between DPrC and short‐chain DACs. The RO2 isomerization via a six‐member ring transition state could happen in DPrC oxidation but not in dimethyl carbonate and diethyl carbonate oxidation, due to the different fuel molecular structure. Therefore, the subsequent reaction pathways via QOOH → O2QOOH → HO2Q = O + OH → OQ = O + OH were promoted and two OH radicals were released in this process. Moreover, it is conceivable that mid or long‐chain DACs could also exhibit an NTC phenomenon due to the increased potential for RO2 isomerization via a six‐ or seven‐member ring transition state, thereby increasing the likelihood of RO2 isomerization occurrence. [ABSTRACT FROM AUTHOR]

Immuno-positron emission tomography (immunoPET) enables imaging of specific targets that play a role in targeted therapy and immunotherapy, such as antigens on cell membranes, targets in the disease microenvironment, or immune cells. The most common immunoPET applications use a monoclonal antibody labeled with a relatively long-lived positron emitter such as 89Zr (T1/2 = 78.4 h), but smaller antibody-based constructs labeled with various other positron emitting radionuclides are also being investigated. This molecular imaging technique can thus guide the development of new drugs and may have a pivotal role in selecting patients for a particular therapy. In early phase immunoPET trials, multiple imaging time points are used to examine the time-dependent biodistribution and to determine the optimal imaging time point, which may be several days after tracer injection due to the slow kinetics of larger molecules. Once this has been established, usually only one static scan is performed and semi-quantitative values are reported. However, total PET uptake of a tracer is the sum of specific and nonspecific uptake. In addition, uptake may be affected by other factors such as perfusion, pre-/co-administration of the unlabeled molecule, and the treatment schedule. This article reviews imaging methodologies used in immunoPET studies and is divided into two parts. The first part summarizes the vast majority of clinical immunoPET studies applying semi-quantitative methodologies. The second part focuses on a handful of studies applying pharmacokinetic models and includes preclinical and simulation studies. Finally, the potential and challenges of immunoPET quantification methodologies are discussed within the context of the recent technological advancements provided by long axial field of view PET/CT scanners. [ABSTRACT FROM AUTHOR]

Ammonia (NH 3), as an ideal zero-carbon fuel, plays a positive role in mitigating global warming. By cofiring NH 3 with highly reactive small molecule fuels such as hydrogen (H 2), carbon monoxide (CO) and methane (CH 4), the reactivity of NH 3 combustion can be significantly enhanced. In this work, a detailed kinetic mechanism for NH 3 /CH 4 /H 2 /CO containing 146 species and 1099 reactions was developed and extensively validated with selected experimental data from the literature on laminar burning velocity (LBV), ignition delay time (IDT), and species concentrations measured in burner-stabilized flames (BSF), plug flow reactors (PFR) and jet-stirred reactors (JSR), covering temperatures of 273–2000 K, pressures of 0.053–40 atm and equivalence ratios of 0.1–2. In addition, the detailed mechanism was simplified to include 53 species and 353 reactions using three simplification methods. Kinetic modeling analysis showed that the chemical and transport effects of H 2 , CO and CH 4 are the main factors enhancing NH 3 combustion, which rising with the increasing initial temperature and decreasing ammonia blending ratio. Among the four reactants, H 2 is consumed first, followed by CH 4 and NH 3 , with CO reacting last. By modifying the Metghalchi-Kech power law equation, it was found that a clear linear relationship exists between LBV and the sum of the maximum mole fractions of O, H, OH, and NH 2 radicals. [Display omitted] • A kinetic mechanism for NH 3 /CH 4 /H 2 /CO was proposed and simplified. • Validation range includes T 0 of 273–2000 K, P 0 of 0.053–40 atm and Ф of 0.1–2. • Chemical kinetics of LBV for NH 3 /H 2 , NH 3 /CO and NH 3 /CH 4 blends were studied. • The correlation between LBV and free radicals was investigated. [ABSTRACT FROM AUTHOR]

This work aims at comparing the ability of 7 modeling approaches to simulate the pyrolysis kinetics of spruce wood, wheat straw, swine manure, miscanthus and switchgrass. Measurements were taken using a thermogravimetric analyzer (TGA) with 4 heating rates comprised between 5 and 30 K min−1. The obtained results were processed using 3 isoconversional methods (Kissinger–Akahira–Sunose (KAS), Ozawa–Flynn–Wall (OFW) and Friedman), 1-step and 3-step Kissinger models, as well as an advanced fitting method recently proposed by Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424). Seventeen reaction models were considered to derive rate constant parameters, which were used to simulate the variation of the fuel conversion degree α as a function of the temperature T . To complement this benchmarking analysis of the modeling approaches commonly used to simulate biomass pyrolysis, a network model, the bio-CPD (chemical percolation devolatilization), was additionally considered. The suitability of each model was assessed by computing the root-mean-square deviation between simulated and measured α = f (T) profiles. As highlights, the model-free methods were found to accurately reproduce experimental results. The agreement between simulated and measured data was found to be higher with the Friedman model, followed by the KAS, FWO, 3-step, and 1-step Kissinger models. As for the bio-CPD, it failed to predict measured data as well as the above-listed models. To conclude, although it was less efficient than the Friedman, KAS or OFW models, the fitting approach from Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424) still led to satisfactory results, while having the advantage of not requiring the selection of a reaction model a priori. [ABSTRACT FROM AUTHOR]

The kinetic modeling of high-temperature reforming of oxygen-free mixtures of methane with H2, H2O, CO, and CO2 additives into synthesis gas with strong dilution with argon under conditions of variable temperature and the formation of microheterogeneous soot particles was carried out. Such mixtures are typical for biomass gasification products, in which H2O, CO, and CO2 additives act as oxidizing agents. A direct comparison of kinetic calculations with the results of published experiments in a flow reactor at temperatures of 1100–1800 K, atmospheric pressure, and a reaction time of 0.68 s was carried out. The yields of soot were calculated for all test mixtures and conditions. A comparison of the results of kinetic calculations and experiments made it possible to evaluate the effect of soot formation on the reforming of methane with the additions of H2, H2O, CO, and CO2. The work analyzes two ways for carbon atoms to leave a reacting gas-phase system. The first way is the heterogeneous deposition of acetylene molecules from the gas phase onto the surface of the reactor with the subsequent formation of solid carbon, and the second way is the formation of microheterogeneous soot particles from nuclei in the gas phase. The paper compares the results of experiments in reflected shock waves and our kinetic calculations of the absolute concentration of CO for the process of methane oxidation in oxygen-free mixtures of methane and CO2. Mixtures with various CH4/CO2 ratios, 90/10, 75/25, and 50/50, were studied at temperatures above 2200 K and atmospheric pressure. It has been shown that the agreement between the calculated and measured CO concentrations improved with increasing temperature and CO2 fraction in the mixture. [ABSTRACT FROM AUTHOR]

An integrated approach involving response surface methodology (RSM) and artificial neural network-ant-colony hybrid optimization (ANN-ACO) was adopted to develop a bioprocess medium to increase the yield of Bacillus cereus neutral protease under submerged fermentation conditions. The ANN-ACO model was comparatively superior (predicted r2 = 98.5%, mean squared error [MSE] = 0.0353) to RSM model (predicted r2 = 86.4%, MSE = 23.85) in predictive capability arising from its low performance error. The hybrid model recommended a medium containing (gL−1) molasses 45.00, urea 9.81, casein 25.45, Ca2+ 1.23, Zn2+ 0.021, Mn2+ 0.020, and 4.45% (vv−1) inoculum, for a 6.75-fold increase in protease activity from a baseline of 76.63 UmL−1. Yield was further increased in a 5-L bioreactor to a final volumetric productivity of 3.472 mg(Lh)−1. The 10.0-fold purified 46.6-kDa-enzyme had maximum activity at pH 6.5, 45–55 °C, with Km of 6.92 mM, Vmax of 769.23 µmolmL−1 min−1, kcat of 28.49 s−1, and kcat/Km of 4.117 × 103 M−1 s−1, at 45 °C, pH 6.5. The enzyme was stabilized by Ca2+, activated by Zn2+ but inhibited by EDTA suggesting that it was a metallo-protease. The biomolecule significantly clarified orange and pineapple juices indicating its food industry application. [ABSTRACT FROM AUTHOR]

The results of an experimental and kinetic modeling study of the ignition of H 2 - CO - O 2 - Ar mixtures behind the reflected shock wave are reported. The experiments were performed with test mixtures containing 0.75 - 3.0 % H 2 , 0 % - 3.0 % CO , and 1.5 % O 2 in argon at temperatures from 950 to 1650 K and a total gas concentration of ∼ 10 - 5 mol / cm 3 . The reaction was monitored by recording the time evolution of the pressure behind the reflected shock wave, intensity of the chemiluminescence of electronically excited OH* radicals at 308.0 ± 2.0 nm, and the absorption by ground-state OH radicals at a 306.772-nm bismuth atomic line. The measured parameters were the time τ 1 it took to reach a ground-state OH concentration of 2.0 × 10 - 9 mol / cm 3 and the time τ 2 to reach the maximum OH* emission intensity. Kinetic simulations demonstrated that τ 1 corresponds to the beginning of fuel consumption, and τ 2 to the time for most of the fuel to be consumed. Therefore, the process of ignition was treated as consisting of two stages: the induction period τ 1 and the burnout time τ 2 - τ 1 . These two time intervals demonstrate different sensitivity to the elementary reactions of the kinetic mechanism. A numerical model capable of predicting the effects of the presence of hydrocarbon impurity, oxygen vibrational relaxation, and pressure rise was used to simulate the experiment. The best agreement between experimental and theoretical results is achieved when these additional factors are taken into account. In addition to the sensitivity coefficient analysis for identifying the most important reactions, a new criterion, referred to as the relative integrated production, was proposed, which compliments the sensitivity coefficient analysis through its ability to identify the most productive reactions. [ABSTRACT FROM AUTHOR]

Introduction: Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling. Objectives: To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature. Methods: To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue. Results: With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined. Conclusion: KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at https://deepamahm.github.io/kiphynet_docs/. [ABSTRACT FROM AUTHOR]

The petroleum fraction of crude oil is laden with notorious nitrogen compounds, whose removal facilitates fuel oil transport and enhances the longevity of hydrodesulfurization catalysts. Petroleum refining operation thus demands the adoption of unconventional techniques, such as coupled oxidative–extractive denitrogenation. Kinetic models can provide insights into the reaction mechanism and the design and performance of reactors. In this work, nine different kinetic models were developed by applying the Langmuir–Hinshelwood and Eley–Rideal approaches to various reaction pathways. The screening of the kinetic models was done based on the values of the rate law parameters and corresponding R2 and adjusted R2 values calculated through a multi-parametric, non-linear regression based on the Levenberg–Marquardt algorithm. Out of all the models, the Eley–Rideal model corresponding to the rate expression rs9 was found to be the best fit, reflecting that hydrogen peroxide remained in bulk while pyridine alone was adsorbed on the catalyst. The selection of suitable extractants for the extractive removal of oxidation products was made based on their extraction efficiency and partition coefficients. The ternary liquid–liquid equilibrium (LLE) data for the (acidulated water-pyridine-isooctane) and (acidulated water-pyridine N-oxide-isooctane) systems were plotted, and Othmer–Tobias, Hand, and Bachman correlations explained the consistency of the experimental LLE data. [ABSTRACT FROM AUTHOR]

The combination of pretreatment with ultrasonic technology and air‐frying could improve the quality and efficiency of apple slice processing. This research aimed to explore how ultrasonic treatment prior to frying and varying temperatures during air‐frying impact the drying and thermodynamic characteristics of apple slices. The study also aimed to assess mathematical models that explain the moisture transfer kinetics and to numerically simulate the moisture distribution in apple slices during air‐frying operation. The results revealed that the moisture content and water activity (aw) of apple slices consistently decreased with longer frying times, regardless of the temperature. The mathematical models' precision was confirmed through regression analysis, which accurately represented the dynamics of moisture transfer, activation energy (Ea), and Gibbs free energy (ΔG) throughout the frying process. The results of the statistical analysis showed that the two‐term (exponential) model was effective in predicting moisture transfer in apple slices throughout air‐frying process, while the spatial distribution of moisture was successfully illustrated by the diffusion model under different conditions. Additionally, numerical simulations showed that moisture removal is faster at higher frying temperatures, and ultrasonic pretreatment led to a shorter frying time. Ultrasonic pretreatment combined with higher frying temperatures led to reduced differential enthalpy, along with an increased effective moisture diffusivity and Gibbs free energy difference. These findings are essential for improving and optimizing the frying process in food manufacturing. [ABSTRACT FROM AUTHOR]

Abstract Eukaryotic mRNAs are transcribed, processed, translated, and degraded in different subcellular compartments. Here, we measured mRNA flow rates between subcellular compartments in mouse embryonic stem cells. By combining metabolic RNA labeling, biochemical fractionation, mRNA sequencing, and mathematical modeling, we determined the half-lives of nuclear pre-, nuclear mature, cytosolic, and membrane-associated mRNAs from over 9000 genes. In addition, we estimated transcript elongation rates. Many matured mRNAs have long nuclear half-lives, indicating nuclear retention as the rate-limiting step in the flow of mRNAs. In contrast, mRNA transcripts coding for transcription factors show fast kinetic rates, and in particular short nuclear half-lives. Differentially localized mRNAs have distinct rate constant combinations, implying modular regulation. Membrane stability is high for membrane-localized mRNA and cytosolic stability is high for cytosol-localized mRNA. mRNAs encoding target signals for membranes have low cytosolic and high membrane half-lives with minor differences between signals. Transcripts of nuclear-encoded mitochondrial proteins have long nuclear retention and cytoplasmic kinetics that do not reflect co-translational targeting. Our data and analyses provide a useful resource to study spatiotemporal gene expression regulation.

Tissue perfusion can be affected by physiology or disease. With the advent of total-body PET, quantitative measurement of perfusion across the entire body is possible. [11C]-butanol is a perfusion tracer with a superior extraction fraction compared with [15O]-water and [13N]-ammonia. To develop the methodology for total-body perfusion imaging, a pilot study using [11C]-butanol on the uEXPLORER total-body PET/CT scanner was conducted. Methods: Eight participants (6 healthy volunteers and 2 patients with peripheral vascular disease [PVD]) were injected with a bolus of [11C]-butanol and underwent 30-min dynamic acquisitions. Three healthy volunteers underwent repeat studies at rest (baseline) to assess test-retest reproducibility; 1 volunteer underwent paired rest and cold pressor test (CPT) studies. Changes in perfusion were measured in the paired rest-CPT study. For PVD patients, local changes in perfusion were investigated and correlated with patient medical history. Regional and parametric kinetic analysis methods were developed using a 1-tissue compartment model and leading-edge delay correction. Results: Estimated baseline perfusion values ranged from 0.02 to 1.95 mL·min-1·cm-3 across organs. Test-retest analysis showed that repeat baseline perfusion measurements were highly correlated (slope, 0.99; Pearson r = 0.96, P < 0.001). For the CPT subject, the largest regional increases were in skeletal muscle (psoas, 142%) and the myocardium (64%). One of the PVD patients showed increased collateral vessel growth in the calf because of a peripheral stenosis. Comorbidities including myocardial infarction, hypothyroidism, and renal failure were correlated with variations in organ-specific perfusion. Conclusion: This pilot study demonstrates the ability to obtain reproducible measurements of total-body perfusion using [11C]-butanol. The methods are sensitive to local perturbations in flow because of physiologic stressors and disease.

Positron emission tomography (PET) radioligands that bind with high-affinity to α4β2-type nicotinic receptors (α4β2Rs) allow for in vivo investigations of the mechanisms underlying nicotine addiction and smoking cessation. Here, we investigate the use of an image-derived arterial input function and the cerebellum for kinetic analysis of radioligand binding in mice. Two radioligands were explored: 2-[18F]FA85380 (2-FA), displaying similar pKa and binding affinity to the smoking cessation drug varenicline (Chantix), and [18F]Nifene, displaying similar pKa and binding affinity to nicotine. Time-activity curves of the left ventricle of the heart displayed similar distribution across wild type mice, mice lacking the β2-subunit for ligand binding, and acute nicotine-treated mice, whereas reference tissue binding displayed high variation between groups. Binding potential estimated from a two-tissue compartment model fit of the data with the image-derived input function were higher than estimates from reference tissue-based estimations. Rate constants of radioligand dissociation were very slow for 2-FA and very fast for Nifene. We conclude that using an image-derived input function for kinetic modeling of nicotinic PET ligands provides suitable results compared to reference tissue-based methods and that the chemical properties of 2-FA and Nifene are suitable to study receptor response to nicotine addiction and smoking cessation therapies.

In the present work, a study was carried out on the effect of the accelerated aging process on the thermal decomposition of two composite solid propellants based on ammonium perchlorate (AP) and hydroxy‑terminated polybutadiene (HTPB). The first one (CP1) was enriched with aluminum (Al) powders, while the second (CP2) contained a mixture of aluminum and lithium alanate (LiAlH4) as a high-energy fuel additive. The thermal properties of the investigated propellant samples were determined using differential scanning calorimetry (DSC) and thermogravimetry (TG) techniques. The obtained results clearly demonstrated the effect of the aging process on the thermal degradation of the aged samples compared to the unaged ones, by shifting the temperature peaks of their main decomposition step to lower temperatures with a decrease in their DSC heat release. In addition, the residual unburnt propellant was increased, particularly for the complex metal hydride-based propellant. Kinetic modeling of the main thermal degradation phase, applying two advanced isoconversional methods, revealed a significant decrease in activation energy for the aged samples. Furthermore, the three-dimensional diffusion model involved during the investigated decomposition phase of the unaged samples was changed to a random nucleation model after 60 days of aging time.

In this study, plain zeolite (ZSM5) and hierarchical zeolite (H-ZSM5) were synthesized as efficient adsorbents for the removal of polyphenolic/antioxidant compounds from olive mill wastewater (OMW). The XRD and FTIR results confirmed the successful synthesis of ZSM5 zeolites. SEM images and BET demonstrated that H-ZSM5 possessed a markedly greater surface area of 337.99 m2/g in contrast to ZSM5 (162.84 m2/g). For H-ZSM5, the values determined for pore volume, total volume, and pore diameters are 77.655 cm3 g−1, 0.1844 cm3 g−1, and 2.1826 nm. The absolute zeta potential values varied between 5.36 ± 1.21 and − 46.41 ± 1.32 mV, with the peak absolute value of − 46.41 ± 1.32 mV occurring at pH 4. The optimal conditions for the removal of phenolic compounds, achieving an efficiency exceeding 90%, were identified as a pH of 4, an adsorbent dosage of 50 mg, and a contact duration of 90 minutes. The adsorption isotherm was found to align with the Langmuir model rather than the Freundlich model, indicating a monolayer adsorption capacity of 48.7 mg/g. Kinetic studies were consistent with a pseudo-first-order model (R2 >0.99). Ultimately, the thermodynamic analysis suggested that the adsorption of OMW onto ZSM5 is both spontaneous and governed by a physisorption mechanism. [ABSTRACT FROM AUTHOR]

This study investigates the synthesis and characterization of Al2O3 nanoparticles derived from multilayered packaging (MLP) waste, specifically postconsumer coffee capsules, and evaluates their efficacy in fluoride adsorption from aqueous solutions. The nanoparticles were synthesized using a facile thermal degradation technique followed by leaching and precipitation processes. Characterization techniques, including inductively coupled plasma‐mass spectroscopy, XRF, Fourier transform infrared spectroscopy, X‐ray diffraction, and transmission electron microscope, confirmed the formation of γ‐Al2O3 nanoparticles with a cubic crystal structure. The adsorption process was found to be endothermic and spontaneous, with a maximum adsorption capacity of 9.60 mg/g. Kinetic studies revealed that the pseudo‐second order model best described the adsorption process, indicating a complex multistep mechanism involving both physisorption in the initial rapid adsorption and chemisorption in the latter slower phase. The Freundlich–Langmuir isotherm provided the best fit for equilibrium data suggesting a complex adsorption mechanism involving both homogeneous and heterogeneous surface interactions. The presence of competing anions, particularly carbonate and phosphate, significantly affected the adsorption efficiency. This research demonstrates the potential of upcycling MLP waste into valuable adsorbents for wastewater treatment applications, offering both environmental and economic benefits. [ABSTRACT FROM AUTHOR]

Nitration of O-methylisouronium sulfate under mixed acid conditions gives O-methyl-N-nitroisourea, a key intermediate of neonicotinoid insecticides with high application value. The reaction is a fast and highly exothermic process with a high mass transfer resistance, making its control difficult and risky. In this paper, a homogeneous continuous flow microreactor system was developed for the nitration of O-methylisouronium sulfate under high concentrations of mixed acids, with a homemade static mixer eliminating the mass transfer resistance. In addition, the kinetic modeling of this reaction was performed based on the theory of NO2+ attack, with the activation energy and pre-exponential factor determined. Finally, based on the response surface generated by the kinetic model, the reaction was optimized with a conversion of 87.4% under a sulfuric acid mass fraction of 94%, initial reactant concentration of 0.5 mol/L, reaction temperature of 40 °C, molar ratio of reactants at 4.4:1, and a residence time of 12.36 minutes.

Abstract Background [18F]MK-6240 is a neurofibrillary tangles PET radiotracer that has been broadly used in aging and Alzheimer’s disease (AD) studies. Majority of [18F]MK-6240 PET studies use dynamic acquisitions longer than 60 min to assess the tracer kinetic parameters. As of today, no consensus has been established on the optimum dynamic PET scan time. In this study, we assess the reproducibility of [18F]MK-6240 quantitative metrics using shortest dynamic PET protocols in cognitively normal subjects. PET metrics were measured through two-tissue compartment model (2TCM) and Logan model to estimate VT and DVR, as well as SUVR from 90 to 120 min (SUVR90 − 120 min) post-tracer injection for brain regions. 2TCM was carried out using the 120 min dynamic coffee break dataset (first scan from 0 to 60 min p.i., second scan from 90 to 120 min p.i.) and then repeated after stepwise shortening it by 5 min. The dynamic scan length that reproduced the 120 min dynamic scans-based VT to within 10% error was defined as the shortest acquisition time (SAT). The SAT SUVR90 − 120 min was deduced from the SAT dataset by extrapolation of each image pixel time-activity curve to 120 min. The reproducibility of the 120 min dynamic scans-based VT2TCM, DVR2TCM, DVRLogan, and SUVR using the SAT was assessed using Passing-Bablock analysis. The limits of reproducibility of each PET metrics were determined using Bland-Altman analysis. Results A dynamic SAT of 40 min yielded

Y2O3–Al2O3–ZrO2 composite ceramic powders are an important structural and functional material. The agglomeration problem in the ceramic powder drying process must be resolved to prepare high-quality ceramic powder, and choosing the right drying techniques can significantly lessen ceramic powder agglomeration. Y2O3–Al2O3–ZrO2 ceramic powder was dried using microwave drying. The drying kinetics of the powders were examined with their initial mass, moisture content, and microwave power. As microwave power, beginning moisture content, and initial mass rose, the average drying rate also increased, according to the experimental data. To better characterize the microwave drying process, the drying data were fitted and analyzed using four thin-layer drying kinetic models, namely Quadratic Model, Modified Page, Wang and Singh, and Two-term exponential. The outcomes demonstrate the good fitting effect of the Modified Page model and the compliance of the fitting parameters with the law. The samples before and after drying were characterized by Fourier transform infrared spectroscopy. Calculating the diffusion coefficient by Fick's second law shows that the effective diffusion coefficient of Y2O3–Al2O3–ZrO2 composite ceramic powder is 5.6 × 10−11 m2/s when the initial moisture content is 5%, the microwave heating power is 560 W, and the initial mass is 25 g. The activation energy for microwave drying of Y2O3–Al2O3–ZrO2 ceramic powders was calculated to be 22.83 W/g according to the Arrhenius formula. This paper aims to provide a theoretical basis and rich experimental data for the study of microwave drying of Y2O3–Al2O3–ZrO2 composite ceramic powder.

Abstract Background Metabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype–phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function. Results A software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent with a priori expectations of fixation probabilities and systematic change in model parameters. Conclusions The PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available at https://github.com/nmccloskey/PEMPS .

Abstract Background Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that each tracer gives rise to indistinguishable 511 keV photon pairs, and thus no unique energy information for differentiating the source of each photon pair. Methods Recently, many applications of deep learning for mPET image separation have been concentrated on pure data-driven methods, e.g., training a neural network to separate mPET images into single-tracer dynamic/static images. These methods use over-parameterized networks with only a very weak inductive prior. In this work, we improve the inductive prior of the deep network by incorporating a general kinetic model based on spectral analysis. The model is incorporated, along with deep networks, into an unrolled image-space version of an iterative fully 4D PET reconstruction algorithm. Results The performance of the proposed method was evaluated on a simulated brain image dataset for dual-tracer [ $$^{18}$$ 18 F]FDG+[ $$^{11}$$ 11 C]MET PET image separation. The results demonstrate that the proposed method can achieve separation performance comparable to that obtained with single-tracer imaging. In addition, the proposed method outperformed the model-based separation methods (the conventional voxel-wise multi-tracer compartment modeling method (v-MTCM) and the image-space dual-tracer version of the fully 4D PET image reconstruction algorithm (IS-F4D)), as well as a pure data-driven separation [using a convolutional encoder-decoder (CED)], with fewer training examples. Conclusions This work proposes a kinetic model-informed unrolled deep learning method for mPET image separation. In simulation studies, the method proved able to outperform both the conventional v-MTCM method and a pure data-driven CED with less training data.

Producing biomass‐derived chemicals to substitute their petrochemical counterparts has long been an aspiration of the green chemistry research community. However, synthesizing secondary amines from biomass precursors presents several challenges related to catalyst nature and the mechanistic understanding of reaction systems. Here, we unravel the mechanistic and kinetic implications of the reductive amination of phenol with cyclohexylamine over Pd/C and Rh/C. A competitive Langmuir‐Hinshelwood reaction model well interpreted the kinetic data, suggesting that support‐metal interfaces serve as active sites for H2, ─NH2 and ═NH activation. The apparent activation energies for imine hydrogenation were 87.6 kJ mol−1 (Pd/C) and 34.5 kJ mol−1 (Rh/C), while ΔHads and ΔSads values confirmed the physicochemical consistency of the model. Moreover, the catalysts demonstrated their high stability to operate for several catalytic cycles, with minor activity losses due to metal leaching and partial sintering of Pd nanoparticles. Despite phenol reductive amination following similar mechanisms on Rh/C and Pd/C, they show differences in selectivity because the hydrogenation of imine is more efficient on Rh0 than on Pd0. This is the first mechanism‐oriented kinetic study for phenol reductive amination; thus, it provides valuable information for process design and scale‐up. [ABSTRACT FROM AUTHOR]

Background: [18F]MK-6240 is a neurofibrillary tangles PET radiotracer that has been broadly used in aging and Alzheimer's disease (AD) studies. Majority of [18F]MK-6240 PET studies use dynamic acquisitions longer than 60 min to assess the tracer kinetic parameters. As of today, no consensus has been established on the optimum dynamic PET scan time. In this study, we assess the reproducibility of [18F]MK-6240 quantitative metrics using shortest dynamic PET protocols in cognitively normal subjects. PET metrics were measured through two-tissue compartment model (2TCM) and Logan model to estimate VT and DVR, as well as SUVR from 90 to 120 min (SUVR90 − 120 min) post-tracer injection for brain regions. 2TCM was carried out using the 120 min dynamic coffee break dataset (first scan from 0 to 60 min p.i., second scan from 90 to 120 min p.i.) and then repeated after stepwise shortening it by 5 min. The dynamic scan length that reproduced the 120 min dynamic scans-based VT to within 10% error was defined as the shortest acquisition time (SAT). The SAT SUVR90 − 120 min was deduced from the SAT dataset by extrapolation of each image pixel time-activity curve to 120 min. The reproducibility of the 120 min dynamic scans-based VT2TCM, DVR2TCM, DVRLogan, and SUVR using the SAT was assessed using Passing-Bablock analysis. The limits of reproducibility of each PET metrics were determined using Bland-Altman analysis. Results: A dynamic SAT of 40 min yielded < 10% error in [18F]MK-6240 VT2TCM's for all brain regions, compared to those measured using the 120 min datasets. SAT-based analysis did not show statistically significant systemic or proportional biases in VT2TCM, DVR2TCM, DVRLogan, or SUVR compared to those deduced from the full dynamic dataset of 120 min. A mean difference between the 120 min- and SAT-based analysis of less than 4%, 10%, 15%, and 20% existed in the VT2TCM, DVR2TCM, DVRLogan, and SUVR respectively. Conclusion: Kinetic modeling of [18F]MK-6240 PET can be accurately performed using dynamic scan times as short as 40 min. This can facilitate studies with [18F]MK-6240 PET and improve patients accrual. Further work would be necessary to confirm the reproducibility of these results for patients in dementia spectra. [ABSTRACT FROM AUTHOR]

A key player in energy metabolism is phosphofructokinase-1 (PFK1) whose activity and behavior strongly influence glycolysis and thus have implications in many areas. In this research, PFK1 assays were performed to convert F6P and ATP into F-1,6-P and ADP for varied pH and ATP concentrations. PFK1 activity was assessed by evaluating F-1,6-P generation velocity in two ways: (1) directly calculating the time slope from the first two or more datapoints of measured product concentration (the initial-velocity method), and (2) by fitting all the datapoints with a differential equation explicitly representing the effects of ATP and pH (the modeling method). Similar general trends of inhibition were shown by both methods, but the former gives only a qualitative picture while the modeling method yields the degree of inhibition because the model can separate the two simultaneous roles of ATP as both a substrate of reaction and an inhibitor of PFK1. Analysis based on the model suggests that the ATP affinity is much greater to the PFK1 catalytic site than to the inhibitory site, but the inhibited ATP-PFK1-ATP complex is much slower than the uninhibited PFK1-ATP complex in product generation, leading to reduced overall reaction velocity when ATP concentration increases. The initial-velocity method is simple and useful for general observation of enzyme activity while the modeling method has advantages in quantifying the inhibition effects and providing insights into the process. [ABSTRACT FROM AUTHOR]

Baking is a healthier alternative to frying, since texture, color, smell, and flavor are developed, without adding oil. The objective was to estimate the moisture content in potato slices, during baking using Fick's law of diffusion to model internal moisture transport and to assess the impact on quality attributes. Moisture transport kinetics were examined at three baking temperatures of 120, 130, and 140°C. Fick's law was employed to estimate average moisture content using different methods: considering both a constant (method of slopes by subperiods, MSS; and method of successive approximations, MSA) and a variable (represented as a quadratic function of time, QFT) behavior of effective diffusivity (De). Three quality variables were analyzed: water activity (aw, dew point hygrometry), total color difference (∆E, colorimetry), and fracturability (F, universal testing machine). The diffusivity estimated with the time‐varying De method provided a more realistic description of moisture migration during baking. The aw, ∆E, and F for baked potato slices ranged from 0.234 to 0.276, 17.9 to 24.6, and 5.20 to 5.49 N, respectively. These attributes imply improved stability and extended shelf life, showing typical colors and texture changes for baked snacks. These changes are linked to variations in diffusivity, influenced by the size and quantity of micropores within the food structure. This study could allow an accurate prediction of mass transfer by considering variable De, facilitating the optimization of baking conditions. Practical Application: The analysis of the moisture content using Fick's law, considering a time‐varying diffusivity, enables the optimization of the baking process for foods. This helps minimize the occurrence of defective products. [ABSTRACT FROM AUTHOR]

Even if huge efforts are made to push alternative mobility concepts, such as electric cars and fuel-cell-powered cars, the significance and use of liquid fuels is anticipated to stay high during the 2030s. Biomethane and synthetic natural gas (SNG) might play a major role in this context, as they are raw material for chemical industry that is easy to be stored and distribute via existing infrastructure, and are a versatile energy carrier for power generation and mobile applications. Since biomethane and synthetic natural gas are suitable for power generation and for mobile applications, they can therefore replace natural gas without any infrastructure changes, thus playing a major role.In this paper, we aim to comprehend the direct production of synthetic natural gas from CO 2 and H 2 in a Sabatier process based on a thermodynamic analysis as well as a multi-step kinetic approach. For this purpose, we thoroughly discuss CO 2 methanation to control emissions in order to maximize the methane formation along with minimizing the CO formation and to understand the complex methanation process. We consider an equilibrium and kinetic modeling study on the NiO- SiO 2 catalyst for methanation focusing on CO 2 -derived SNG. The thermodynamic analysis of CO 2 hydrogenation is preformed to define the optimal process parameters followed by the kinetic simulations for catalyst development. The investigation presented in this paper can also be used for developing machine learning algorithms for methanation processes. [ABSTRACT FROM AUTHOR]

The thermal degradation behavior of forest fuels is crucial to optimize the environmental conditions before processing it for energy utilization. The ambient oxygen concentration affects the combustion kinetics and gas emissions during thermal degradation. In this study, the effects of oxygen concentration on the combustion kinetics and gas emission characteristics of forest duff (FD) were investigated by the coupling technique of thermogravimetric analysis—Fourier transform infrared spectrometry/mass spectrometry (TG-FTIR/MS). TG results reveal that thermal degradation rate was enhanced by increasing oxygen concentration. FTIR/MS results show that oxygen facilitated the generation of CO, CO2, H2O, carboxylic acids [COOH]+, and ester group [OCOH]+ but was not conducive to the formation of pyrolyzates of H2, –CH2–/–CH3, CH4, aldehyde groups [COH]+, acetaldehyde groups [CH2COH]+, and aryl groups [C7H7]+. The generation of a small amount of pyrolyzates except for [C7H7]+ at higher temperatures (> 400 °C) in oxidative atmospheres was confirmed to be attributed to the char volatilization. According to the integrated findings from TG-FTIR/MS analysis, a comprehensive six-step reaction scheme including drying, FD pyrolysis, FD oxidation, char volatilization, and the oxidation of char and char residue was proposed for the kinetic analysis. A favorable agreement between the experimental and simulated TG and derivative thermogravimetric (DTG) curves was obtained. The kinetic model reveals that secondary char oxidation (i.e., the oxidation of char residue) is more pronounced in conditions of oxygen deficiency and high heating rates. The results of this study are conductive to understanding the thermal degradation behavior of FD in different atmospheres. [ABSTRACT FROM AUTHOR]

• Sono-osmodehydration was used to accelerate the freezing process. • A new exponential model was proposed for the prediction of WL and SG. • Dehydration pretreatment shortened the freezing time significantly. • High sonication duration resulted in structural damage, as confirmed by SEM. • Sono-osmodehydration at 40 % concentration-35 °C was the most favorable condition. Considerable efforts have been made to minimize the adverse effects of freezing on the quality of frozen products. In this study, ultrasound-assisted osmodehydrofreezing (UOD) was used to accelerate the freezing process. The effects of osmotic solution concentration, temperature, and application of ultrasound on freezing characteristics and quality attributes of frozen carrot were investigated. The changes in water loss (WL) and solid gain (SG) were predicted using kinetic models, with effective diffusivities determined according to Fick's second law of diffusion for a finite cylinder. The results from the kinetic modeling demonstrated the good fitness of the new proposed exponential model. Effective diffusivities for WL and SG ranged from 4.49 × 10−8 m2 s−1 to 8.42 × 10−8 m2 s−1 and 4.30 × 10−8 m2 s−1 to 5.12 × 10−8 m2 s−1, respectively, with the highest diffusivities observed in ultrasound-assisted dehydrated samples at 45 °C in a 60 % sucrose solution. Dehydration pretreatment could shorten the freezing time significantly. Pretreated samples (after thawing) showed higher firmness and lower phenolic contents, as compared to the control samples (freezing without osmodehydration). In addition, the UOD process minimized drip loss and electrolyte leakage. The high duration of ultrasound exposure resulted in structural damage, which was confirmed by scanning electron microscopy. In general, it was revealed that the UOD process prior to freezing was more useful in preventing the quality loss of the samples, as compared to osmodeydration without sonication. These findings, thus indicate that applying UOD is a promising approach for freezing food materials. [ABSTRACT FROM AUTHOR]

The arginine-vasopressin (AVP) hormone plays a pivotal role in regulating various physiological processes, such as hormone secretion, cardiovascular modulation, and social behavior. Recent studies have highlighted the V1a receptor as a promising therapeutic target. In-depth insights into V1a receptor-related pathologies, attained through in vivo imaging and quantification in both peripheral organs and the central nervous system (CNS), could significantly advance the development of effective V1a inhibitors. To address this need, we develop a novel V1a-targeted positron emission tomography (PET) ligand, [18F]V1A-2303 ([18F] 8), which demonstrates favorable in vitro binding affinity and selectivity for the V1a receptor. Specific tracer binding in peripheral tissues was also confirmed through rigorous cell uptake studies, autoradiography, biodistribution assessments. Furthermore, [18F] 8 was employed in PET imaging and arterial blood sampling studies in healthy rhesus monkeys to assess its brain permeability and specificity, whole-body distribution, and kinetic properties. Our research indicated [18F] 8 as a valuable tool for noninvasively studying V1a receptors in peripheral organs, and as a foundational element for the development of next-generation, brain-penetrant ligands specifically designed for the CNS. This work described the synthesis and evaluation of a novel 18F-labeled vasopressin 1a receptor PET ligand, which showed peripheral binding specificity and could cross the blood‒brain barrier in non-human primates. [Display omitted] [ABSTRACT FROM AUTHOR]

Several nations are committed to developing an alternative energy source to achieve the net zero emissions (NZE) target. A typical alternative is bioethanol, which has been reported to be a renewable energy supporting the achievement of the target. Although banana pseudostem waste is often minimally utilized and discarded by the community, several studies have shown its potential to yield bioethanol due to the high cellulose content. Therefore, this study aimed to synthesize bioethanol from banana pseudostem waste (Musa balbisiana) pretreated with potassium hydroxide (KOH) microwave using hydrolysis and fermentation. The effect of yeast concentrations (8%, 10%, and 12%) and fermentation times (6, 7, 8, 12, and 13 days) on the pretreated sample was also analyzed. Fermentation was carried out using enzymatic kinetic modeling with Michaelis Menten's equations to determine the reaction rate. The results showed that the sample with 12% yeast and fermentation time of 13 days produced the highest ethanol content (41.5%). In addition, the appropriate kinetic modeling results were similar to Hanes Woolf's linearization modeling. The 10% yeast concentration led to KM values of 1.606×10-3 g mL-1 and Vmax of 6.837×10-4 g mL-1 h. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the optimal dual-time-point (DTP) approaches using dynamic 68Ga-PSMA-11 PET/CT imaging to generate parametric images for prostate cancer patients. Methods: Fifteen patients with prostate cancer were intravenously administered 68Ga-PSMA-11 of 181.9 ± 47.2 MBq, followed by an immediate 60 min dynamic PET/CT scan. List-mode data were reconstructed into 25 timeframes (6 × 10 s, 8 × 30 s, and 11 × 300 s) and corrected for motion and partial volume effect. DTP parametric images were generated using different interval time points of 5 min and 10 min, with a minimum of 30 min time interval. Net influx rates (Ki) were calculated through the fitting of a single irreversible two-tissue compartmental model. Intraclass correlation coefficient (ICC) values between DTP protocols and 60 min Ki were obtained. Lesion-to-background ratios (LBRs) of Ki and standardized uptake value (SUV) images in each DTP protocol were determined. Results: The DTP protocol of 5–10 min with a 40–45 min interval showed the highest ICC of 0.988 compared with the 60 min Ki, whereas the ICC values for the intervals of 0–5 min with 55–60 min and 0–10 min with 50–60 min were 0.941. The LBRs of the 60 min Ki, 5–10 min with 40–45 min Ki, 0–5 min with 55–60 min Ki, 0–10 min with 50–60 min Ki, SUVmean, and SUVmax images were 29.53 ± 27.33, 13.05 ± 15.28, 45.15 ± 53.11, 45.52 ± 70.31, 19.77 ± 23.43, and 25.06 ± 30.07, respectively. Conclusion: The 0–5 min with 55–60 min DTP parametric imaging exhibits a comparable Ki to 60 min parametric imaging and remarkable image quality and contrast than SUV imaging, enhancing prostate cancer diagnosis while maintaining time efficiency. [ABSTRACT FROM AUTHOR]

Ionic liquid (IL) can not only serve as solvents to reduce carbon capture energy consumption, but also may activate the CO2 absorption of amine solutions. Here, the absorption mechanism and kinetic modeling of IL‐activated single and mixed amines were studied in wetted wall column. N‐(2‐aminoethyl) ethanolamine (AEEA) and N,N‐diethylethanolamine (DEEA) were used as representatives to evaluate the IL activation effects on primary and tertiary amines. It was found that IL activated the reaction process of primary amine, but had no activation effect on tertiary amine. The activation energy of AEEA‐IL‐CO2 was 22.2 kJ/mol, which was 21.0% lower than AEEA‐CO2. Kinetic modeling of IL‐activated AEEA and mixed amines was established. Besides, the density functional theory calculations showed that IL can form hydrogen bonding and other interactions with AEEA and CO2 to activate the absorption reaction, which can reduce 29.3% activation energy during the zwitterion formation stage. [ABSTRACT FROM AUTHOR]

AbstractMicrobes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules. [ABSTRACT FROM AUTHOR]

A novel aerobic denitrifying bacterium Bacillus cereus J1 was isolated from a sewage treatment plant. Its characteristics under two distinct nitrogen sources were systematically investigated. According to the results of whole-genome sequencing, we inferred that strain J1 removes nitrogen through processes such as aerobic denitrification, dissimilatory nitrate reduction to ammonium, and ammonium assimilation. The degradation process of COD and total inorganic nitrogen (TIN) correlated to the zero-order degradation kinetics equation, and the maximum removal rate of NO3−−N reached 3.17 mg/L/h and that of NH4+−N was 3.79 mg/L/h. Utilizing single-factor experiments and response surface methodology, the optimal conditions for nitrate removal were determined as a shaking speed of 115 rpm, COD/nitrogen mass (C/N ratio) of 12.25, and salinity of 3.44 g/L, with the C/N ratio exerting the most significant influence. Similarly, for the maximum ammonium removal, the ideal conditions involved a shaking speed of 133 rpm, C/N ratio of 29, and salinity of 13.30 g/L, with the shaking speed exerting the most significant influence. These findings demonstrate that large amounts of ammonium and nitrate can be quickly removed with the help of Bacillus cereus J1, indicating that strain J1 may be applied to alleviate nitrogen pollution in aquatic environments. [ABSTRACT FROM AUTHOR]

This research delves into the development of a hydrogel based on sodium alginate (Na‐Alg), CMTKG and PSA for the controlled release of Metformin Hydrochloride (Metformin HCl), a commonly used drug for the treatment of type 2 diabetes. The hydrogel was synthesized via a free radical mechanism using N, N′‐methylene‐bis(acrylamide) (MBA) as a crosslinker and potassium persulphate (KPS) as an initiator. The hydrogels were analyzed using techniques such as ATR‐FTIR, and SEM. The research also investigated swelling behaviour, drug loading, drug encapsulation efficiency, and release kinetics of Metformin HCl. The finding revealed that the hydrogel exhibited pH‐responsive metformin HCl release, with higher drug release perceived at pH 7.4 compared to pH 1.2. Mathematical models were employed to study the drug release mechanism, indicating diffusion as a mechanism liable for the drug release from the hydrogel. Moreover, the synthesized metformin HCl‐loaded Na‐Alg/PSA/CMTKG hydrogel shows excellent anti‐diabetic activity on α‐amylase and α‐glucosidase enzyme. Henceforth, the formulated hydrogel reveals potential for facilitating the targeted release of Metformin HCl, offering opportunities for improving patient amenability and lessening side effects related to frequent dosing. [ABSTRACT FROM AUTHOR]