Hierarchical Structuration in Protocellular System

Spatial control is one of the ubiquitous features in biological systems and the key to the functional complexity of living cells. The strategies to achieve such precise spatial control in protocellular systems are crucial to constructing complex artificial living systems with functional collective behavior. Herein, the authors review recent advances in the spatial control within a single protocell or between different protocells and discuss how such hierarchical structured protocellular system can be used to understand complex living systems or to advance the development of functional microreactors with the programmable release of various biomacromolecular payloads, or smart protocell-biological cell hybrid system.

Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology

The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.

Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling

PURPOSE

The effective transverse relaxation rate (R 2 * $$ {\mathrm{R}}_2^{\ast } $$ ) is influenced by biological features that make it a useful means of probing brain microstructure. However, confounding factors such as dependence on flip angle (α) and fiber orientation with respect to the main field (θ $$ \uptheta $$ ) complicate interpretation. The α- andθ $$ \uptheta $$ -dependence stem from the existence of multiple sub-voxel micro-environments (e.g., myelin and non-myelin water compartments). Ordinarily, it is challenging to quantify these sub-compartments; therefore, neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a singleR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.

METHODS

We used 2-pool (myelin and non-myelin water) simulations to characterize the bias in single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates. Based on our numeric observations, we introduced a linear model that partitionsR 2 * $$ {\mathrm{R}}_2^{\ast } $$ into α-dependent and α-independent components and validated this in vivo at 7T. We investigated the dependence of both components on the sub-compartment properties and assessed their robustness, orientation dependence, and reproducibility empirically.

RESULTS

R 2 * $$ {\mathrm{R}}_2^{\ast } $$ increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numeric and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fiber orientation, although it suffered from marginally higher noise sensitivity.

CONCLUSION

We have demonstrated and validated a simple approach that mitigates flip angle and orientation biases in single-compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.

Dynamic routing for waste collection and transportation with multi-compartment electric vehicle using smart waste bins

The municipal solid waste (MSW) collection and transportation issue has been studied by numerous researchers; however, a few studies consider the chance-constrained programming for co-collection of sorted waste with electric vehicles (EVs). Therefore, this article attempts to study on the chance-constrained collection and transportation problem for sorted waste with multiple separated compartments EVs. Considering the uncertainty of the waste generation rate under the scenario of application of smart waste bins, chance-constrained programming is applied to transform the uncertain model into a certain one. A Chance-Constrained Multi-Compartment Electric Vehicle Routing Problem (CCMCEVRP) is introduced and the corresponding mathematical formulation is established. A diversity-enhanced particle swarm optimisation with neighbourhood search and simulated annealing (DNSPSOSA) is proposed to solve this problem, and effectiveness of the proposed algorithms is verified by extensive numerical experiments on the newly generated instances. In addition, the application of the model is tested by comparing different compartment and different type vehicles. It is found that, compared with fuel vehicles, 32.66% of the average cost could be saved with EVs. Furthermore, the rate of cost-saving of EVs increases with the increase in the number of compartments: the improvement rate of cost-saving of two-compartment EVs and three-compartment EVs is 52.77% and 68.13%, respectively.

Reliability and validity of various laboratory methods of body composition assessment in young adults

Accurate measures of body composition (BC) are essential for performance and health. In addition to accuracy, BC measures should be practical and be minimally invasive to maximize their utility. The purpose of the present study was to compare the day-to-day variability and validity of four common laboratory-based body composition assessments to a criterion four-compartment model. Dual x-ray absorptiometry (DXA), air displacement plethysmography (BP), multi-frequency bioelectrical impedance (MF-BIA) and underwater weighing (UWW) were performed twice in a sample of 32 young men and women. Participants were assessed in a fasted, euhydrated state 2-7 days apart. All methods were compared to a criterion four-compartment model using BP-derived body volume, DXA-derived bone mineral content and MF-BIA-derived total body water (4C_BP ). Additional four-compartment models using UWW- and DXA-derived body volume were also examined (4C_UWW ) and (4C_DXA ). Validity results were conducted with paired t-tests and Bland-Altman analysis. Reliability was determined using intraclass correlations (ICC), coefficients of variation (CV) and standard error of the measurement (SEM). Validity analysis revealed that all methods overestimated per cent body fat and fat mass, and underestimated fat-free mass when compared with 4C_BP , but only DXA and BP were significantly different (P<0·008). All measures were highly reliable across days (ICCs > 0·9, CVs < 12%). Results of the present study indicate that typical laboratory-based methods of body composition are valid and reliable. However, we caution that results should not be translated between methods and assessments should be performed with the same instrument when the goal is to monitor changes in body composition over time.

Pattern Recognition in Pharmacokinetic Data Analysis

Pattern recognition is a key element in pharmacokinetic data analyses when first selecting a model to be regressed to data. We call this process going from data to insight and it is an important aspect of exploratory data analysis (EDA). But there are very few formal ways or strategies that scientists typically use when the experiment has been done and data collected. This report deals with identifying the properties of a kinetic model by dissecting the pattern that concentration-time data reveal. Pattern recognition is a pivotal activity when modeling kinetic data, because a rigorous strategy is essential for dissecting the determinants behind concentration-time courses. First, we extend a commonly used relationship for calculation of the number of potential model parameters by simultaneously utilizing all concentration-time courses. Then, a set of points to consider are proposed that specifically addresses exploratory data analyses, number of phases in the concentration-time course, baseline behavior, time delays, peak shifts with increasing doses, flip-flop phenomena, saturation, and other potential nonlinearities that an experienced eye catches in the data. Finally, we set up a series of equations related to the patterns. In other words, we look at what causes the shapes that make up the concentration-time course and propose a strategy to construct a model. By practicing pattern recognition, one can significantly improve the quality and timeliness of data analysis and model building. A consequence of this is a better understanding of the complete concentration-time profile.

Viable and fixed white matter: diffusion magnetic resonance comparisons and contrasts at physiological temperature

PURPOSE

Fixed samples have been used extensively in diffusion MRI (dMRI) studies. However, fixation causes significant structural changes in tissue. The purpose of this study was to evaluate fixed white matter as a surrogate for viable white matter during development and validation of dMRI methods.

METHODS

dMRI data was acquired from fixed and viable rat optic nerves maintained in identical conditions in a viable isolated tissue (VIT) chamber. The chamber preserves tissue integrity for 10 h at 37°C. Diffusion tensors (DT) and multi-compartment white matter signal models were fitted to the data.

RESULTS

When comparing VIT and fixed tissue, DT parameters demonstrated that fixation causes significant reductions in axial diffusivity and increases in radial diffusivity. However, both tissues exhibited similar responses to changes in diffusion times and gradient strengths. Multicompartment models demonstrated differences in parameter estimates (e.g., directional diffusivities) that were analogous to differences in DT parameters. Similarities in multi-compartment model rankings suggested that tissue water populations were broadly maintained postfixation.

CONCLUSIONS

The data demonstrate that fixed tissue, while maintaining the broad water environment of viable tissue, differs significantly in diffusion parameters. Results from dMRI experiments on fixed tissue may correlate with-but will not directly translate into-results from viable tissue.