A highly efficient modified nano-activated carbon adsorbent for separation of ammonia from water: Experimental and kinetics elucidations.

Water is essential for the survival of all living things; however, its extensive use in agriculture, high-tech manufacturing, energy production, and the rapid development of the chemical and petroleum industries has led to significant contamination, making water pollution a major concern today. Ammonia is one of the most harmful contaminants present in water, posing significant environmental and health risks that require appropriate remediation methods. To remove ammonia from contaminated water, we employ Carbon Nanotubes (CNTs) and Activated Carbon (AC). To ensure appropriate metal impregnation on the adsorbents, Fe, Al, Ag, and Cu were impregnated into both CNT and AC, followed by extensive characterization using Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), and Energy Dispersive X-rays (EDX). To optimize ammonia removal from water, several parameters were adjusted, including pH, dose amount, contact time, shaking speed, and temperature. Astonishingly, the highest removal efficiency of 40% was achieved with a 1 g dosage at pH 10.5 and 200 RPM, while silver oxide had a lower removal rate of 10% under the same conditions. Temperature additionally had a significant impact, with removal percentages reaching 40% at 70 °C as compared to 21.5% at 25 °C. Adsorption isotherms were used to analyze the experimental data, along with Langmuir and Freundlich's models. Notably, Langmuir produced superior curve fitting, resulting in a correlation factor close to one. Furthermore, kinetic modeling was carried out with 2nd-order and pseudo-2nd-order equations, with the latter responding better according to curve analysis. Because the ammonia removal rate was low, this study indicates the feasibility of implementing an adsorption technique using CNT and AC as a pre-treatment method for this purpose. This approach has the potential for future optimization and deployment in tackling water contamination concerns effectively.

Modeling of moisture content during baking with different approaches for effective diffusivity and evaluation of quality variables in baked potato slices.

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.

Noninvasive quantification of [18F]SynVesT-1 binding using simplified reference tissue model 2.

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 [Formula: see text] (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 [Formula: see text]. 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 [Formula: see text](ref: CS) and DVRCER(ref: CER) values. For SRTM2, the population average of [Formula: see text] 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 [Formula: see text] was much higher than 1TC [Formula: see text], SRTMC underestimated BPND(ref: CS) and DVRCER by an average of 3% and 1% across regions, respectively, due to similar bias in k2 and [Formula: see text] 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 [Formula: see text] values, SRTM2 BPND and DVRCER showed the best agreement with 1TC estimates. CONCLUSION: Our findings support the use of population [Formula: see text] value in SRTM2 with [18F]SynVesT-1 for the estimation of [Formula: see text] or DVRCER, regardless of the choice of reference region.

Photo-reductive decomposition of perfluorooctane sulfonate (PFOS) and its common alternatives by UV/VUV/sulfite process: Mechanism, kinetic modeling, and water matrix effects.

The present study investigated the photo-reduction of perfluorooctane sulfonate (PFOS) and its alternatives, focusing on decomposition mechanisms, active species involvement, the influence of background water constituents, and kinetic model development. The decomposition and defluorination rates followed the order of PFOS > PFHxS > 6:2 FTSA > PFBS, with shorter chains and CH2 linkers enhancing the resistance of PFOS alternatives against the attack of hydrated electrons (eaq-). Two primary pathways were identified during the photodegradation of PFAS: (i) H/F exchange at CF bonds with the lowest bond dissociation energies (BDEs) and (ii) functional group cleavage followed by short-chain PFCAs formation, with OH playing a crucial role in transforming intermediates. Adding iodide and elevated temperatures demonstrated a synergistic effect on PFBS decomposition and defluorination, with high temperatures promoting functional group cleavage as the preferred defluorination pathway. The study examined the impact of background water constituents in different aqueous environments, from surface waters to wastewater streams and ion-exchange brine concentrates. Chloride exhibited a concentration-based dual impact on the UV/VUV/sulfite process: promotive effects at low dosages (1-10 mM) by acting as a secondary eaq- mediator, and adverse effects at high dosages (20-500 mM) due to the scavenging effect of generated chlorine radicals (Cl). High ionic strength adversely affected eaq- quantum efficiency. Additionally, bicarbonate and natural organic matter (NOM) had opposing effects on PFOS photo-reduction, primarily through eaq- scavenging and pH alteration. Kinetic modeling revealed reaction rate constants of the studied PFAS with eaq- ranging from 1.8 × 106 to 1.3 × 109 M-1 s-1, corroborating the concentration profiles of active species and highlighting the reductive nature of sulfite-mediated processes.

Performance evaluation of ICX reactor in treatment of paper mill wastewater: a case study in South Vietnam.

This study evaluates the performance of the Internal Circulation eXperience (ICX) reactor in treating high-strength paper mill wastewater in the south of Vietnam. The ICX reactor effectively managed organic concentrations (sCOD) of up to 11,800 mg/L. Results indicate a volumetric loading rate (VLR) of 26.8 kg/m3 × day, achieving processing efficiency exceeding 81% while consistently maintaining volatile fatty acids (VFA) below 300 mg/L. The study employed Monod and Stover-Kincannon kinetic modeling, revealing dynamic parameters including Ks = 56.81 kg/m3, Y = 0.121 kgVSS/kgsCOD, Kd = 0.0242 1/day, µmax = 0.372 1/day, Umax = 151 kg/m3 × day, and KB = 175.92 kg/m3 × day, underscoring the ICX reactor's superior efficiency compared to alternative technologies. Notably, the reactor's heightened sensitivity to VFA levels necessitates influent concentrations below 1,400 mg/L for effective sludge treatment. Furthermore, the influence of calcium on treatment efficiency requires post-treatment alkalinity maintenance below 19 meq/L to stabilize MLVSS/MLSS concentration. Biogas production ranged from 0.6 to 0.7 Nm3 biogas/kg sCOD; however, calcium impact diminished this ratio, reducing overall treatment efficiency and biogas production. The study contributes valuable insights into anaerobic treatment processes for complex industrial wastewaters, emphasizing the significance of controlling VFA, calcium, and alkalinity for optimal system performance.

UV-induced reactive species dynamics and product formation by chlorite.

Chlorite (ClO2-) is a regulated byproduct of chlorine dioxide water treatment processes. The transformation of chlorite under UV irradiation into chloride (Cl-) and chlorate (ClO3-) involves reactive species chain reactions that could enhance chlorine dioxide water treatment efficiency while reducing residual chlorite levels. This study conducted a mechanistic investigation of chlorite phototransformation by analyzing reaction intermediates and stable end products, including chlorine dioxide (ClO2), free chlorine (HOCl/OCl-), hydroxyl­radical (•OH), Cl-, and ClO3- through combined experimental and modeling approaches. Experiments were performed at UV254 irradiation in pure buffered water within the pH range of 6 to 8. Results indicated that the apparent quantum yields for chlorite phototransformation increased from 0.86 to 1.45, and steady-state •OH concentrations at 1 mM initial chlorite concentration rose from 8.16 × 10-14 M - 16.1 × 10-14 M with decreasing pH values. It was observed that under UV irradiation, chlorite acts as both a significant producer and consumer of reactive species through three distinct reaction pathways. The developed kinetic model, which incorporates optimized intrinsic chlorite quantum yields Φchloritein ranging from 0.33 to 0.39, effectively simulated the loss of oxidants and the formation of major products. It also accurately predicted steady-state concentrations of various species, including •OH, •ClO, Cl• and O3. For the first time, this study provides a comprehensive transformation pathway scheme for chlorite phototransformation. The findings offer important insights into the mechanistic aspects of product and oxidizing species formation during chlorite phototransformation.

Mössbauer and EPR detection of iron trafficking kinetics and possibly labile iron pools in whole Saccharomyces cerevisiae cells.

The kinetics of iron trafficking in whole respiring S. cerevisiae cells was investigated using Mössbauer and EPR spectroscopies. The Mössbauer-active isotope 57Fe was added to cells growing under iron-limited conditions; cells were analysed at different times post iron addition. Spectroscopic changes suggested that the added 57Fe initially entered the labile iron pool, and then distributed to vacuoles and mitochondria. The first spectroscopic feature observed, ∼ 3 min after adding 57Fe plus a 5-15 min processing deadtime, was a quadrupole doublet typical of nonheme high-spin FeII. This feature likely arose from labile FeII pools in the cell. At later times (15-150 min), magnetic features due to S = 5/2 FeIII developed; these likely arose from FeIII in vacuoles. Corresponding EPR spectra were dominated by a g = 4.3 signal from the S = 5/2 FeIII ions that increased in intensity over time. Developing at a similar rate was a quadrupole doublet typical of S = 0 [Fe4S4]2+ clusters and low-spin FeII hemes; such centers are mainly in mitochondria, cytosol, and nuclei. Development of these features was simulated using a published mathematical model, and simulations compared qualitatively well with observations. In the five sets of experiments presented, all spectroscopic features developed within the doubling time of the cells, implying that the detected iron trafficking species are physiologically relevant. These spectroscopy-based experiments allow the endogenous labile iron pool within growing cells to be detected without damaging or altering the pool as definitely occurs using chelator-probe detection and possibly occurs using chromatographic separations.

Precise metabolic modeling in post-omics era: accomplishments and perspectives.

Microbes 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.

A blood-free modeling approach for the quantification of the blood-to-brain tracer exchange in TSPO PET imaging.

Introduction: Recent evidence suggests the blood-to-brain influx rate (K1 ) in TSPO PET imaging as a promising biomarker of blood-brain barrier (BBB) permeability alterations commonly associated with peripheral inflammation and heightened immune activity in the brain. However, standard compartmental modeling quantification is limited by the requirement of invasive and laborious procedures for extracting an arterial blood input function. In this study, we validate a simplified blood-free methodologic framework for K1 estimation by fitting the early phase tracer dynamics using a single irreversible compartment model and an image-derived input function (1T1K-IDIF). Methods: The method is tested on a multi-site dataset containing 177 PET studies from two TSPO tracers ([11C]PBR28 and [18F]DPA714). Firstly, 1T1K-IDIF K1 estimates were compared in terms of both bias and correlation with standard kinetic methodology. Then, the method was tested on an independent sample of [11C]PBR28 scans before and after inflammatory interferon-α challenge, and on test-retest dataset of [18F]DPA714 scans. Results: Comparison with standard kinetic methodology showed good-to-excellent intra-subject correlation for regional 1T1K-IDIF-K1 (ρintra = 0.93 ± 0.08), although the bias was variable depending on IDIF ability to approximate blood input functions (0.03-0.39 mL/cm3/min). 1T1K-IDIF-K1 unveiled a significant reduction of BBB permeability after inflammatory interferon-α challenge, replicating results from standard quantification. High intra-subject correlation (ρ = 0.97 ± 0.01) was reported between K1 estimates of test and retest scans. Discussion: This evidence supports 1T1K-IDIF as blood-free alternative to assess TSPO tracers' unidirectional blood brain clearance. K1 investigation could complement more traditional measures in TSPO studies, and even allow further mechanistic insight in the interpretation of TSPO signal.

Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.

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/ .

Formation and kinetic analysis of AGEs in Pacific white shrimp during frying.

Advanced glycation end products (AGEs) are complex and heterogeneous compounds closely associated with various chronic diseases. The changes in Nε-carboxymethyllysine (CML), Nε-carboxyethyllysine (CEL), Nε-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1), and fluorescent AGEs (F-AGEs) in fried shrimp during frying (170 °C, 0-210 s) were described by kinetic models. Besides,the correlations between AGEs contents and physicochemical indicators were analyzed to reveal their intrinsic relationship. Results showed that the changes of four AGEs contents followed the zero-order kinetic, and their rate constants were ranked as kCML < kCEL ≈ kMG-H1 < kF-AGEs. Oil content and lipid oxidation were critical factors that affected the AGEs levels of the surface layer. Protein content and Maillard reaction were major factors in enhancing the CML and CEL levels of the interior layer. Furthermore, the impact of temperature on the generation of CML and CEL was greater than that of MG-H1 and F-AGEs.

Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's 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.

Kinetic modeling of the photocatalytic degradation of acetaminophen and its main transformation products.

In this study, a kinetic model of the heterogeneous photocatalytic degradation of acetaminophen and its main transformation products is presented. Kinetic photocatalytic modeling and photon absorption rate modeling were included. Monte Carlo method was used to model the photon absorption process. Experiments were carried out in a reactor operated in batch mode and TiO2 nanotubes were used as photocatalyst irradiated with 254 nm UVC. Kinetic parameters were estimated from the experiments data by applying a non-linear regression procedure. Intrinsic expressions to the kinetics of acetaminophen degradation and its main transformation products were derived. Model, kinetics and photon absorption formulations and parameters proved to be affordable for describing the photocatalytic degradation of acetaminophen, but improvements should be done for better description of formation and oxidation kinetics of main transformation products. The model should be tested with other pharmaceuticals and emergent pollutants to calibrate it and evaluate its applicability in a wide range of compounds.

Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.

Challenges and opportunities in bioprocessing of gellan gum: A review.

Gellan gum (GG) - the microbial exopolysaccharide is increasingly being adopted into drug development, tissue engineering, and food and pharmaceutical products. In spite of the commercial importance and expanding application horizon of GG, little attention has been directed toward the exploration of novel microbial cultures, development of advanced screening protocols, strain engineering, and robust upstream or downstream processes. This comprehensive review not only attempts to summarize the existing knowledge pool on GG bioprocess but also critically assesses their inherent challenges. The process optimization design augmented with advanced machine learning modeling tools, widely adopted in other microbial bioprocesses, should be extended to GG. The unification of mechanistic insight into data-driven modeling would help to formulate optimal feeding and process control strategies.

Unveiling the Mechanism and Kinetics of Pollutant Attenuation by Free Radicals Triggered from Goethite in Water Distribution Systems.

Investigating the fate of persistent organic pollutants in water distribution systems (WDSs) is of great significance for preventing human health risks. The role of iron corrosion scales in the migration and transformation of organics in such systems remains unclear. Herein, we determined that hydroxyl (•OH), chlorine, and chlorine oxide radicals are generated by Fenton-like reactions due to the coexistence of oxygen vacancy-related Fe(II) on goethite (a major constituent of iron corrosion scales) and hypochlorous acid (HClO, the main reactive chlorine species of residual chlorine at pH ∼ 7.0). •OH contributed mostly to the decomposition of atrazine (ATZ, model compound) more than other radicals, producing a series of relatively low-toxicity small molecular intermediates. A simplified kinetic model consisting of mass transfer of ATZ and HClO, •OH generation, and ATZ oxidation by •OH on the goethite surface was developed to simulate iron corrosion scale-triggered residual chlorine oxidation of organic compounds in a WDS. The model was validated by comparing the fitting results to the experimental data. Moreover, the model was comprehensively applicable to cases in which various inorganic ions (Ca2+, Na+, HCO3-, and SO42-) and natural organic matter were present. With further optimization, the model may be employed to predict the migration and accumulation of persistent organic pollutants under real environmental conditions in the WDSs.

Kinetic model-informed deep learning for multiplexed PET image separation.

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 F]FDG+[ 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.

Heparosan biosynthesis in recombinant Bacillus megaterium: Influence of N-acetylglucosamine supplementation and kinetic modeling.

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.

Indirect photodegradation of typical pyrrolizidine alkaloids in water induced by triplet states of dissolved organic matter.

The occurrence of pyrrolizidine alkaloids (PAs) in the aquatic environment has received growing attention due to their persistent mutagenicity and carcinogenicity. In this study, the photooxidation processes of four representative PAs (senecionine, senecionine N-oxide, europine, and heliotrine) in the presence of dissolved organic matter (DOM) were investigated. The excited triplet DOM (3DOM*) was demonstrated to play a dominant role in the phototransformation of PAs. The observed degradation rates of PAs largely depended on the DOM concentration. Alkaline conditions and the presence of HCO3-/CO32- were conducive to the photodegradation. Based on kinetic modeling, the second-order reaction rate constants of PAs with 3DOM* were predicted to be (1.7∼5.3)×108 M-1 s-1, nearly two orders of magnitude higher than those with singlet oxygen (1O2). The monoester structure and electron-withdrawing substituent (e.g., -O atom) substantially affected the one-electron oxidation potential of PAs, which dictates the reaction rates of PAs with 3DOM*. Finally, a tentative degradation pathway of PAs was proposed, involving the formation of an N-centered radical cation through one-electron transfer, which then likely deprotonated and further oxidized to more persistent and toxic phototransformation products with an added oxygen atom into the pyrrole ring.

Advancing PFAS risk assessment: Integrative approaches using agent-based modelling and physiologically-based kinetic for environmental and health safety.

Per- and polyfluoroalkyl substances (PFAS), ubiquitous in a myriad of consumer and industrial products, and depending on the doses of exposure represent a hazard to both environmental and public health, owing to their persistent, mobile, and bio accumulative properties. These substances exhibit long half-lives in humans and can induce potential immunotoxic effects at low exposure levels, sparking growing concerns. While the European Food Safety Authority (EFSA) has assessed the risk to human health related to the presence of PFAS in food, in which a reduced antibody response to vaccination in infants was considered as the most critical human health effect, a comprehensive grasp of the molecular mechanisms spearheading PFAS-induced immunotoxicity is yet to be attained. Leveraging modern computational tools, including the Agent-Based Model (ABM) Universal Immune System Simulator (UISS) and Physiologically Based Kinetic (PBK) models, a deeper insight into the complex mechanisms of PFAS was sought. The adapted UISS serves as a vital tool in chemical risk assessments, simulating the host immune system's reactions to diverse stimuli and monitoring biological entities within specific adverse health contexts. In tandem, PBK models unravelling PFAS' biokinetics within the body i.e. absorption, distribution, metabolism, and elimination, facilitating the development of time-concentration profiles from birth to 75 years at varied dosage levels, thereby enhancing UISS-TOX's predictive abilities. The integrated use of these computational frameworks shows promises in leveraging new scientific evidence to support risk assessments of PFAS. This innovative approach not only allowed to bridge existing data gaps but also unveiled complex mechanisms and the identification of unanticipated dynamics, potentially guiding more informed risk assessments, regulatory decisions, and associated risk mitigations measures for the future.