Preparation and in vitro/in vivo evaluation of uniform-sized Goserelin-loaded sustained release microspheres.

Sustained release microspheres loaded with goserelin are regarded as a promising candidate for treating prostate cancer and other sex hormone diseases. However, their widespread adoption has been hindered by issues such as wide particle size distribution and unstable release characteristics. To address these challenges, we employed a combination of the solid-in-oil-in-water microspheres preparation approach (S/O/W) and innovative premix membrane emulsification technology and deeply investigated the effects of four key parameters on the loaded performance of microspheres and the microscopic mechanisms behind them. With this approach, we successfully produced goserelin-loaded sustained release microspheres of narrow particle size distribution (Span 0.642), remarkable encapsulation efficiency (DL = 4.23 %, EE = 93.98 %), low initial burst release (about 0.50 % within 2 h), and compatibility with small injection needles (23-G, inner diameter 0.33 mm, outer diameter 0.64 mm, maximal force 59 N). In the animal model(administered dose, 2.4 mg·Kg-1), goserelin long-acting sustained release microspheres sustained release for over 32 days, maintaining effective concentrations above 2 ng·mL-1, and effectively reduced serum testosterone concentrations to castration levels (<1.0 ng·mL-1) by day 4, maintaining this inhibition for up to 21 days, exhibiting comparable efficacy to the positive control group. In vivo release kinetics analysis revealed that goserelin-loaded sustained release microspheres exhibited a release pattern dominated by diffusion with corrosion assistance in vivo. In summary, the systematic and comprehensive evaluation of uniform-sized goserelin-loaded sustained release microspheres has highlighted their excellent translational potential, and the study herein may provide new strategies and ideas for the development of microsphere dosage forms.

Constructing neural networks with pre-specified dynamics.

A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm-termed generalised Firing-to-Parameter (gFTP)-provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics.

Modeling the growth curve in ducks: a sinusoidal model as an alternative to classical nonlinear models.

The present study aimed to apply a sinusoidal model to duck body weight records in order to introduce it to the field of poultry science. Using 8 traditional growth functions as a guide (Bridges, Janoschek, logistic, Gompertz, Von Bertalanffy, Richards, Schumacher, and Morgan), this study looked at how well the sinusoidal equation described the growth patterns of ducks. By evaluating statistical performance and examining model behavior during nonlinear regression curve fitting, models were compared. The data used in this study came from 3 published articles reporting 1) body weight records of Kuzi ducks aged 1 to 70 d, 2) body weight records for Polish Peking ducks aged 1 to 70 d, and 3) average body weight of Peking ducks aged 1 to 42 d belonging to 5 different breeds. The general goodness-of-fit of each model to the various data profiles was assessed using the adjusted coefficient of determination, root mean square error, Akaike's information criterion (AIC), and Bayesian information criterion. All of the models had adjusted coefficient of determination values that were generally high, indicating that the models generally fit the data well. Duck growth dynamics are accurately described by the chosen sinusoidal equation. The sinusoidal equation was found to be one of the best functions for describing the age-related changes in body weight in ducks when the growth functions were compared using the goodness-of-fit criteria. To date, no research has been conducted on the use of sinusoidal equations to describe duck growth development. To describe the growth curves for a variety of duck strains/lines, the sinusoidal function employed in this study serves as a suitable substitute for conventional growth functions.

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network.

This paper aims to accurately assess and effectively manage various security risks in the community and overcome the challenges faced by traditional models in handling large amounts of features and high-dimensional data. Hence, this paper utilizes the back propagation neural network (BPNN) to optimize the security risk assessment model. A key challenge of researching community security risk assessment lies in accurately identifying and predicting a range of potential security threats. These threats may encompass natural disasters, public health crises, accidents, and social security issues. The intricate interplay of these risk factors, combined with the dynamic nature of community environments, presents difficulties for traditional risk assessment methodologies to address effectively. Initially, this paper delves into the factors influencing safety incidents within communities and establishes a comprehensive system of safety risk assessment indicators. Leveraging the adaptable and generalizable nature of the BPNN model, the paper proceeds to optimize the BPNN model, enhancing the security risk assessment model through this optimization. Subsequent comparison experiments with traditional models validate the rationality and effectiveness of the proposed model, with hidden layer nodes set at various levels like 10, 15, 20, 25, 30, and 35. These traditional models include Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and eXtreme Gradient Boosting (XGBOOST). Experimental findings demonstrate that with 20 hidden layer nodes, the optimized model achieves a remarkable final recognition accuracy of 99.1 %. Moreover, the optimized model exhibits significantly lower final function loss compared to models with different node numbers. Increasing the number of hidden layer nodes may diminish the optimized model's fit and accuracy. Comparison with traditional models reveals that the average accuracy of the optimized model in community risk identification reaches 98.5 %, with a maximum accuracy of 99.6 %. This marks an improvement of 9%-11 % in recognition accuracy across various risk factors compared to traditional models. Regarding system response time and resource utilization, the optimized model exhibits a response time ranging from 100 ms to 120 ms and consistently lower resource utilization rates across all scenarios, underscoring its efficiency in community security risk assessment. In conclusion, this experiment sheds light on the underlying mechanisms and patterns of community safety risk formation, offering novel perspectives and methodologies for researching community safety risk assessment. The paper concludes by presenting recommendations and strategies for addressing community safety risks based on experimental analysis.

Diffusion-weighted MR spectroscopy of the prostate.

PURPOSE: Prostate tissue has a complex microstructure, mainly composed of epithelial and stromal cells, and of extracellular (acinar-luminal) spaces. Diffusion-weighted MR spectroscopy (DW-MRS) is ideally suited to explore complex microstructure in vivo with metabolites selectively distributed in different subspaces. To date, this technique has been applied to brain and muscle. This study presents the development and pioneering utilization of 1H-DW-MRS in the prostate, accompanied by in vitro studies to support interpretations of in vivo findings. METHODS: Nine healthy volunteers underwent a prostate MR examination (mean age, 56 years; range, 31-66). Metabolic complexation was studied in vitro using solutions with major compounds found in prostatic fluid of the lumen. DW-MRS was performed at 3 T with a non-water-suppressed single-voxel sequence with metabolite-cycling to concurrently measure metabolite and water signals. The water signal was used in postprocessing as a reference in a motion-compensation scheme. The spectra were fitted simultaneously in the spectral and diffusion-weighting dimensions. Apparent diffusion coefficients (ADCs) were derived by fitting signal decays that were assumed to be mono-exponential for metabolites and biexponential for water. RESULTS: DW-MRS of the prostate revealed relatively low ADCs for Cho and Cr compounds, aligning with their intracellular location and higher ADCs for citrate and spermine supporting their luminal origin. In vitro assessments of the ADCs of citrate and spermine demonstrated their complex formation and protein binding. Tissue concentrations of MRS-detectable metabolites were as expected for the voxel location. CONCLUSIONS: This work successfully demonstrates the feasibility of 1H-DW-MRS of the prostate and its potential for providing valuable microstructural information.

Anionic species from multivalent metal salts are differentially retained during aqueous ionic gelation of sodium alginate and could fine-tune the hydrogel properties.

The role of anionic counterions of divalent metal salts in alginate gelation and hydrogel properties has been thoroughly investigated. Three anions were selected from the Hofmeister series, namely sulphate, acetate and chloride, paired in all permutations and combinations with divalent metal cations like calcium, zinc and copper. Spectroscopic analysis revealed the presence of anions and their interaction with the respective metal cations in the hydrogel. The data showed that the gelation time and other hydrogel properties were largely controlled by cations. However, subtle yet significant variations in viscoelasticity, water uptake, drug release and cytocompatibility properties were anion dependent in each cationic group. Computational modelling based study showed that metal-anion-alginate configurations were energetically more stable than the metal-alginate models. The in vitro and in silico studies concluded that acetate anions preceded chlorides in the drug release, swelling and cytocompatibility fronts, followed by sulphate anions in each cationic group. Overall, the data confirmed that anions are an integral part of the metal-alginate complex. Furthermore, anions offer a novel option to further fine-tune the properties of alginate hydrogels for myriads of applications. In addition, full exploration of this novel avenue would enhance the usability of alginate polymers in the pharmaceutical, environmental, biomedical and food industries.

Building Individual Player Performance Profiles According to Pre-Game Expectations and Goal Difference in Soccer.

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players' energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder-Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87±61.42 and root mean squared error (RMSE) of 520.69±88.66 achieved by our model, as opposed to the B1 MAE of 429.04±84.87 and RMSE of 581.34±185.84, and B2 MAE of 421.57±95.96 and RMSE of 613.47±300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players' responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics.

Kinetics Study of PVA Polymer by Model-Free and Model-Fitting Methods Using TGA.

Thermogravimetric Analysis (TGA) serves a pivotal technique for evaluating the thermal behavior of Polyvinyl alcohol (PVA), a polymer extensively utilized in the production of fibers, films, and membranes. This paper targets the kinetics of PVA thermal degradation using high three heating rate range 20, 30, and 40 K min-1. The kinetic study was performed using six model-free methods: Freidman (FR), Flynn-Wall-Qzawa (FWO), Kissinger-Akahira-Sunose (KAS), Starink (STK), Kissinger (K), and Vyazovkin (VY) for the determination of the activation energy (Ea). TGA showed two reaction stages: the main one at 550-750 K and the second with 700-810 K. But only the first step has been considered in calculating Ea. The average activation energy values for the conversion range (0.1-0.7) are between minimum 104 kJ mol-1 by VY to maximum 199 kJ mol-1 by FR. Model-fitting has been applied by combing Coats-Redfern (CR) with the master plot (Criado's) to identify the most convenient reaction mechanism. Ea values gained by the above six models were very similar with the average value of (126 kJ mol-1) by CR. The reaction order models-Second order (F2) was recommended as the best mechanism reaction for PVA pyrolysis. Mechanisms were confirmed by the compensation effect. Finally, (∆H, ∆G, and ∆S) parameters were presented and proved that the reaction is endothermic.

Comparison of methods for intravoxel incoherent motion parameter estimation in the brain from flow-compensated and non-flow-compensated diffusion-encoded data.

PURPOSE: Joint analysis of flow-compensated (FC) and non-flow-compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain. METHODS: Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non-linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b-values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning-based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b-values 0-200 s/mm2 and corresponding flow weighting factors 0-2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis. RESULTS: Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning-based algorithm for IVIM parameters D $$ D $$ and f $$ f $$ , and for the Bayesian algorithm only for v d $$ {v}_d $$ , relative to the other methods. CONCLUSION: A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning-based algorithms appear promising.

Chelation of iron(II) ions by ellagitannins-Effects of hexahydroxydiphenoyl and nonahydroxytriphenoyl groups.

Tannins represent secondary plant metabolites that are used to control bacterial populations by chelation of essential metal ions. Their presence in food also affects the bioavailability of iron. This study investigates the influence of ellagitannins (vescalin, castalin, vescalagin, castalagin) structure and pH on the stoichiometry and formation constants of ellagitannin-Fe(II) coordination compounds. We demonstrated that ellagitannins are stable for at least one hour at pH values lower than 7.25. The spectra of neutral compounds were measured and explained with the help of TDDFT calculations. Furthermore, the pH-dependence of the ellagitannins UV-Vis spectra was examined to obtain insight into their protolytic equilibrium. Using Job's method in the pH range 3.50-5.51, the stoichiometry of the formed ellagitannin-Fe(II) ions complexes was determined. A model explaining interactions between ellagitannins and Fe(II) ions, that took into account the protolytic equilibrium of ellagitannins, was fitted globally to all four Job plots, whereby the corresponding formation constants were obtained.

Adsorption performance of nanoplastics in carbon filtration column.

Nanoplastics (NPs) are usually formed by the decomposition of large plastics, which will cause water pollution after entering the water body. Carbon filter column is used to adsorb and remove polystyrene nanoparticles (PSNPs). The influence of experimental conditions on adsorption was investigated and fitted by kinetic model. The results show that increasing the height of carbon filter column and decreasing the initial concentration of PSNPs and water flow rate can prolong the breakthrough time of carbon filter column. When the initial concentration of PSNPs is 0.8 mg L-1, the influent flow rate is 4 mL min-1 and the height of carbon filter bed is 8.5 cm, the removal effect is the best, and the depletion point of carbon filter column is extended to 48 h. Adams-Bohart model is suitable for describing the initial stage of adsorption. Thomas and Yoon-Nelson models can well describe the whole dynamic adsorption process of PSNPs, and Yoon-Nelson model can accurately predict the time required for 50% PSNPs to penetrate the carbon column. The adsorption mechanism of NPs by carbon filter column is mainly through the attachment sites and pore retention provided by particles on the surface of activated carbon. This study can provide new technical and theoretical support for the removal of NPs.

The relationship between weight-related indicators and depressive symptoms during adolescence and adulthood: results from two twin studies.

BACKGROUND: The association between weight and depressive symptoms is well established, but the direction of effects remains unclear. Most studies rely on body mass index (BMI) as the sole weight indicator, with few examining the aetiology of the association between weight indicators and depressive symptoms. METHODS: We analysed data from the Twins Early Development Study (TEDS) and UK Adult Twin Registry (TwinsUK) (7658 and 2775 twin pairs, respectively). A phenotypic cross-lagged panel model assessed the directionality between BMI and depressive symptoms at ages 12, 16, and 21 years in TEDS. Bivariate correlations tested the phenotypic association between a range of weight indicators and depressive symptoms in TwinsUK. In both samples, structural equation modelling of twin data investigated genetic and environmental influences between weight indicators and depression. Sensitivity analyses included two-wave phenotypic cross-lagged panel models and the exclusion of those with a BMI <18.5. RESULTS: Within TEDS, the relationship between BMI and depression was bidirectional between ages 12 and 16 with a stronger influence of earlier BMI on later depression. The associations were unidirectional thereafter with depression at 16 influencing BMI at 21. Small genetic correlations were found between BMI and depression at ages 16 and 21, but not at 12. Within TwinsUK, depression was weakly correlated with weight indicators; therefore, it was not possible to generate precise estimates of genetic or environmental correlations. CONCLUSIONS: The directionality of the relationship between BMI and depression appears to be developmentally sensitive. Further research with larger genetically informative samples is needed to estimate the aetiological influence on these associations.

SEIRS model for malaria transmission dynamics incorporating seasonality and awareness campaign.

Malaria, a devastating disease caused by the Plasmodium parasite and transmitted through the bites of female Anopheles mosquitoes, remains a significant public health concern, claiming over 600,000 lives annually, predominantly among children. Novel tools, including the application of Wolbachia, are being developed to combat malaria-transmitting mosquitoes. This study presents a modified susceptible-exposed-infectious-recovered-susceptible (SEIRS) compartmental mathematical model to evaluate the impact of awareness-based control measures on malaria transmission dynamics, incorporating mosquito interactions and seasonality. Employing the next-generation matrix approach, we calculated a basic reproduction number (R0) of 2.4537, indicating that without robust control measures, the disease will persist in the human population. The model equations were solved numerically using fourth and fifth-order Runge-Kutta methods. The model was fitted to malaria incidence data from Kenya spanning 2000 to 2021 using least squares curve fitting. The fitting algorithm yielded a mean absolute error (MAE) of 2.6463 when comparing the actual data points to the simulated values of infectious human population (Ih). This finding indicates that the proposed mathematical model closely aligns with the recorded malaria incidence data. The optimal values of the model parameters were estimated from the fitting algorithm, and future malaria dynamics were projected for the next decade. The research findings suggest that social media-based awareness campaigns, coupled with specific optimization control measures and effective management methods, offer the most cost-effective approach to managing malaria.

Distinct roles of monkey OFC-subcortical pathways in adaptive behavior.

To be the most successful, primates must adapt to changing environments and optimize their behavior by making the most beneficial choices. At the core of adaptive behavior is the orbitofrontal cortex (OFC) of the brain, which updates choice value through direct experience or knowledge-based inference. Here, we identify distinct neural circuitry underlying these two separate abilities. We designed two behavioral tasks in which macaque monkeys updated the values of certain items, either by directly experiencing changes in stimulus-reward associations, or by inferring the value of unexperienced items based on the task's rules. Chemogenetic silencing of bilateral OFC combined with mathematical model-fitting analysis revealed that monkey OFC is involved in updating item value based on both experience and inference. In vivo imaging of chemogenetic receptors by positron emission tomography allowed us to map projections from the OFC to the rostromedial caudate nucleus (rmCD) and the medial part of the mediodorsal thalamus (MDm). Chemogenetic silencing of the OFC-rmCD pathway impaired experience-based value updating, while silencing the OFC-MDm pathway impaired inference-based value updating. Our results thus demonstrate a dissociable contribution of distinct OFC projections to different behavioral strategies, and provide new insights into the neural basis of value-based adaptive decision-making in primates.

Dynamic Release Characteristics and Kinetics of a Persulfate Sustained-Release Material.

Sustained-release materials are increasingly being used in the delivery of oxidants for in situ chemical oxidation (ISCO) for groundwater remediation. Successful implementation of sustained-release materials depends on a clear understanding of the mechanism and kinetics of sustained release. In this research, a columnar sustained-release material (PS@PW) was prepared with paraffin wax and sodium persulfate (PS), and column experiments were performed to investigate the impacts of the PS@PW diameter and PS/PW mass ratio on PS release. The results demonstrated that a reduction in diameter led to an increase in both the rate and proportion of PS release, as well as a diminished lifespan of release. The release process followed the second-order kinetics, and the release rate constant was positively correlated with the PS@PW diameter. A matrix boundary diffusion model was utilized to determine the PS@PW diffusion coefficient of the PS release process, and the release lifespan of a material with a length of 500 mm and a diameter of 80 mm was predicted to be more than 280 days. In general, this research provided a better understanding of the release characteristics and kinetics of persulfate from a sustained-release system and could lead to the development of columnar PS@PW as a practical oxidant for in situ chemical oxidation of contaminated aquifers.

The Seven-parameter Diffusion Model: an Implementation in Stan for Bayesian Analyses.

Diffusion models have been widely used to obtain information about cognitive processes from the analysis of responses and response-time data in two-alternative forced-choice tasks. We present an implementation of the seven-parameter diffusion model, incorporating inter-trial variabilities in drift rate, non-decision time, and relative starting point, in the probabilistic programming language Stan. Stan is a free, open-source software that gives the user much flexibility in defining model properties such as the choice of priors and the model structure in a Bayesian framework. We explain the implementation of the new function and how it is used in Stan. We then evaluate its performance in a simulation study that addresses both parameter recovery and simulation-based calibration. The recovery study shows generally good recovery of the model parameters in line with previous findings. The simulation-based calibration study validates the Bayesian algorithm as implemented in Stan.

The existence and strength of higher order interactions is sensitive to environmental context.

One strategy for understanding the dynamics of any complex system, such as a community of competing species, is to study the dynamics of parts of the system in isolation. Ecological communities can be decomposed into single species, and pairs of interacting species. This reductionist strategy assumes that whole-community dynamics are predictable and explainable from knowledge of the dynamics of single species and pairs of species. This assumption will be violated if higher order interactions (HOIs) are strong. Theory predicts that HOIs should be common. But it is difficult to detect HOIs, and to infer their long-term consequences for species coexistence, solely from short-term data. I conducted a protist microcosm experiment to test for HOIs among competing bacterivorous ciliates, and test the sensitivity of HOIs to environmental context. I grew three competing ciliate species in all possible combinations at each of two resource enrichment levels, and used the population dynamic data from the one- and two-species treatments to parameterize a competition model at each enrichment level. I then compared the predictions of the parameterized model to the dynamics of the whole community (three-species treatment). I found that the existence, and thus strength, of HOIs was environment dependent. I found a strong HOI at low enrichment, which enabled the persistence of a species that would otherwise have been competitively excluded. At high enrichment, three-species dynamics could be predicted from a parameterized model of one- and two-species dynamics, provided that the model accounted for nonlinear intraspecific density dependence. The results provide one of the first rigorous demonstrations of the long-term consequences of HOIs for species coexistence, and demonstrate the context dependence of HOIs. HOIs create difficult challenges for predicting and explaining species coexistence in nature.

Chitooligosaccharide reconstitutes intestinal mucus layer to improve oral absorption of water-soluble drugs.

Intestinal mucus is a complex natural hydrogel barrier with unique physical properties that impede the absorption of various oral drugs. Both washout from the upper water layer and the physical resistance of the mucus layer particularly affect bioavailability of, especially, highly water-soluble molecules. One potential strategy for designing pharmaceutical formulations is to add absorption enhancers (AEs). However, there are few reports of AEs that work on mucus and their underlying mechanisms, leading to imprecise application. In this study, we investigated chitooligosaccharide (COS) as a safe, low-cost, and effective oral drug AE. We revealed the hydrodynamic law of interaction between COS and the intestinal mucus layer, which was associated with absorption benefiting mucus structural reconstruction. Based on this, we designed a translational strategy to improve the bioavailability of a group of soluble oral drugs by drinking COS solution before administration. Moreover, this research is expected to expand its application scenario by reducing drug dosage such as avoiding gastro-intestinal irritation and slowing veterinary antibiotic resistance.

AugmentA: Patient-specific augmented atrial model generation tool.

Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.

Modeling the Batch Sedimentation of Calcium Carbonate Particles in Laboratory Experiments-A Systematic Approach.

The design of continuous thickeners and clarifiers is commonly based on the solid flux theory. Batch sedimentation experiments conducted with solid concentrations still provide useful information for their application. The construction of models for the velocity of settling allows the estimation of the flux of solids throughout time, which can, in turn, be used to find the area of the units required to achieve a given solid concentration in the clarified stream. This paper addresses the numerical treatment of data obtained from batch sedimentation experiments of calcium carbonate particles. We propose a systematic framework to fit a model that is capable of representing the process features that involve (i) the numerical differentiation of data to generate initial estimates for the instantaneous velocity of settling; (ii) the integration of a differential equation to fit the model for the velocity of settling; and (iii) the assessment of the quality of the fit using common statistical indicators. The model used for demonstration has a theoretical basis combined with an empirical component to account for the effect of the particle concentrations and their state of aggregation. The values of the numerical parameters obtained are related to the characteristic dimensions of the aggregates and their mass-length fractal dimensions.