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.

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.

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.

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.

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.

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.

OBJECTIVE: [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.
METHODS: 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.
CONCLUSIONS: 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.

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.

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-), hydroxyl‑radical (•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.

BACKGROUND: 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.
OBJECTIVE: 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.
CONCLUSIONS: 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/ .