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The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.

BackgroundRecent years have witnessed a rising trend in exploring microalgae for valuable carotenoid products as the demand for lutein and many other carotenoids in global markets has increased significantly. In green microalgae lutein is a major carotenoid protecting cellular components from damage incurred by reactive oxygen species under stress conditions. In this study, we investigated the effects of abiotic stressors on lutein accumulation in a strain of the marine microalga D. salina which had been selected for growth under stress conditions of combined blue and red lights by adaptive laboratory evolution.ResultsNitrate concentration, salinity and light quality were selected as three representative influencing factors and their impact on lutein production in batch cultures of D. salina was evaluated using response surface analysis. D. salina was found to be more tolerant to hyper-osmotic stress than to hypo-osmotic stress which caused serious cell damage and death in a high proportion of cells while hyper-osmotic stress increased the average cell size of D. salina only slightly. Two models were developed to explain how lutein productivity depends on the stress factors and for predicting the optimal conditions for lutein productivity. Among the three stress variables for lutein production, stronger interactions were found between nitrate concentration and salinity than between light quality and the other two. The predicted optimal conditions for lutein production were close to the original conditions used for adaptive evolution of D. salina. This suggests that the conditions imposed during adaptive evolution may have selected for the growth optima arrived at.ConclusionsThis study shows that systematic evaluation of the relationship between abiotic environmental stresses and lutein biosynthesis can help to decipher the key parameters in obtaining high levels of lutein productivity in D. salina. This study may benefit future stress-driven adaptive laboratory evolution experiments and a strategy of applying stress in a step-wise manner can be suggested for a rational design of experiments.

Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support. [ABSTRACT FROM AUTHOR]

Background: Anterior cruciate ligament (ACL) injuries have been studied using a variety of methods and tools. However, each is hindered by specific limitations with respect to its application. Aim: To assess the combined effects of external moments and muscle activations on ACL loading using serial, forward dynamics (FD) simulations of single leg, hyperextension landings in OpenSim. Methods: The FD tool of OpenSim was iteratively run using different combinations of knee-spanning muscle activation levels, internal rotation and valgus knee moment magnitudes. A regression was conducted on the data in order to predict ACL loading under different conditions. Results: A purely abduction moment leads to greater mean ACL loading than a purely internal rotation moment or any combination of the two. Additionally, the generalized boosted regression model using both external moments and certain knee muscles identified the internal rotation moment as the most important variable in predicting the ACL load (R2 = 0.9; p < 0.0001). Conclusion: This study demonstrated a novel and practical application of an OpenSim musculoskeletal model that supports the ACL injury mechanism of landing with low knee flexion angles, high muscle forces of the Quadriceps muscles and an external knee valgus moment, though further investigation is needed. [ABSTRACT FROM AUTHOR]

Featured Application: The novel mechanical testing method for prosthetic feet presented here may be used by researchers and engineers to evaluate sagittal plane stiffness characteristics. The method gives comparable results to state-of-the-art biomechanical testing and can be beneficial for evaluating and optimizing a prosthetic foot before user testing. Energy-storing-and-returning prosthetic feet are frequently recommended for lower limb amputees. Functional performance and stiffness characteristics are evaluated by state-of-the-art biomechanical testing, while it is common practice for design engineers and researchers to use test machines to measure stiffness. The correlation between user-specific biomechanical measures and machine evaluation has not been thoroughly investigated, and mechanical testing for ramps is limited. In this paper, we propose a novel test method to assess prosthetic foot stiffness properties in the sagittal plane. First, biomechanical data were collected on five trans-tibial users using a variable stiffness prosthetic foot on a split-belt treadmill. Gait trials were performed on level ground and on an incline and a decline of 7.5°. The same prosthetic foot was tested on a roll-over test machine for the three terrains. The sagittal ankle moment and angle were compared for the two test methods. The dorsiflexion moment and angle were similar, while more variability was observed in the plantarflexion results. A good correlation was found for level-ground walking, while decline walking showed the largest differences in the results of the maximum angles. The roll-over test machine is a useful tool to speed up design iterations with a set design goal prior to user testing. [ABSTRACT FROM AUTHOR]

BACKGROUND: Platelet concentrates (PCs) can be prepared using three methods: platelet (PLT)-rich plasma, apheresis, and buffy coat. The aim of this study was to obtain a comprehensive data set that describes metabolism of buffy coat-derived PLTs during storage and to compare it with a previously published parallel data set obtained for apheresis-derived PLTs. STUDY DESIGN AND METHODS: During storage we measured more than 150 variables in 8 PLT units, prepared by the buffy coat method. Samples were collected at seven different time points resulting in a data set containing more than 8000 measurements. This data set was obtained by combining a series of standard quality control assays to monitor the quality of stored PLTs and a deep coverage metabolomics study using liquid chromatography coupled with mass spectrometry. RESULTS: Stored PLTs showed a distinct metabolic transition occurring 4 days after their collection. The transition was evident in PLT produced by both production methods. Apheresis-derived PLTs showed a clearer phenotype of PLT activation during early days of storage. The activated phenotype of apheresis PLTs was accompanied by a higher metabolic activity, especially related to glycolysis and the tricarboxylic acid cycle. Moreover, the extent of the activation differed between bags resulting in interbag variability in the storage lesion of apheresis-prepared PLTs. This may be related to donor-related polymorphism. CONCLUSION: This study demonstrated two discrete metabolic phenotypes in stored PLTs prepared with both apheresis and buffy coat methods. PLT activation occurs during the first metabolic phenotype and might lead to a low reproducibility of the apheresis PCs. [ABSTRACT FROM AUTHOR]

Background Platelet ( PLT) concentrates are routinely stored for 5 to 7 days. During storage they exhibit what has been termed PLT storage lesion ( PSL), which is evident by a loss of hemostatic function when transfused into patients. The overall goal of this study was to obtain a comprehensive data set describing PLT metabolism during storage. Study Design and Methods The experimental approach adopted to achieve this goal combined a series of standard assays to monitor the quality of stored PLTs and a deep-coverage metabolomics study using liquid chromatography coupled with mass spectrometry performed on both the extracellular and the intracellular environments. During storage we measured 174 different variables in 6 PLT units, collected by apheresis. Samples were collected at eight different time points resulting in a data set containing more than 8000 measurements. Results Stored PLTs did not undergo a monotonic decay, but experienced systematic changes in metabolism reflected in three discrete metabolic phenotypes: The first ( Days 0-3) was associated with active glycolysis, pentose phosphate pathway, and glutathione metabolism and down regulation of tricarboxylic acid ( TCA) cycle. The second ( Days 4-6) was associated with a more active TCA cycle as well as increased purine metabolism. A third metabolic phenotype of less clinical relevance ( Days 7-10) was associated with a faster decay of cellular metabolism. Conclusion PSL is not associated with a linear decay of metabolism, but rather with successive metabolic shifts. These findings may give new insight into the mechanisms underlying PSL and encourage the deployment of systems biology methods to PSL. [ABSTRACT FROM AUTHOR]

Motivation: A genome-scale reconstruction of human metabolism, Recon 2, is available but no interface exists to interactively visualize its content integrated with omics data and simulation results. Results: We manually drew a comprehensive map, ReconMap 2.0, that is consistent with the content of Recon 2. We present it within a web interface that allows content query, visualization of custom datasets and submission of feedback to manual curators. [ABSTRACT FROM AUTHOR]

Background Recent years have witnessed a rising trend in exploring microalgae for valuable carotenoid products as the demand for lutein and many other carotenoids in global markets has increased significantly. In green microalgae lutein is a major carotenoid protecting cellular components from damage incurred by reactive oxygen species under stress conditions. In this study, we investigated the effects of abiotic stressors on lutein accumulation in a strain of the marine microalga D. salina which had been selected for growth under stress conditions of combined blue and red lights by adaptive laboratory evolution. Results Nitrate concentration, salinity and light quality were selected as three representative influencing factors and their impact on lutein production in batch cultures of D. salina was evaluated using response surface analysis. D. salina was found to be more tolerant to hyperosmotic stress than to hypo-osmotic stress which caused serious cell damage and death in a high proportion of cells while hyper-osmotic stress increased the average cell size of D. salina only slightly. Two models were developed to explain how lutein productivity depends on the stress factors and for predicting the optimal conditions for lutein productivity. Among the three stress variables for lutein production, stronger interactions were found between nitrate concentration and salinity than between light quality and the other two. The predicted optimal conditions for lutein production were close to the original conditions used for adaptive evolution of D. salina. This suggests that the conditions imposed during adaptive evolution may have selected for the growth optima arrived at. Conclusions This study shows that systematic evaluation of the relationship between abiotic environmental stresses and lutein biosynthesis can help to decipher the key parameters in obtaining high levels of lutein productivity in D. salina. This study may benefit future stress-driven adaptive laboratory evolution experiments and a strategy of applying stress in a stepwise manner can be suggested for a rational design of experiments. [ABSTRACT FROM AUTHOR]

Adaptation of lower limb function to different gait tasks is inherently not as effective among individuals with lower limb amputation as compared to able-bodied individuals. Varying stiffness of a prosthetic foot may be a way of facilitating gait tasks that require larger ankle joint range of motion. Three stiffness settings of a novel prosthetic foot design were tested for level walking at three speeds as well as for 7,5° incline and decline walking. Outcome measures, describing ankle range of motion and ankle dynamic joint stiffness were contrasted across the three stiffness settings. Standardized mechanical tests were done for the hindfoot and forefoot. Dorsiflexion angle was incrementally increased with a softer foot and a faster walking speed / higher degree of slope. The concurrent dynamic joint stiffness exhibited a less systematic change, especially during INCLINE and DECLINE walking. The small difference seen between the stiffness settings for hindfoot loading limits analysis for the effects of stiffness during weight acceptance, however, a stiffer foot significantly restricted plantarflexion during DECLINE. Varying stiffness settings within a prosthetic foot does have an effect on prosthetic foot dynamics, and differences are task dependent, specifically in parameters involving kinetic attributes. When considering the need for increased ankle range of motion while performing more demanding gait tasks, a foot that allows the users themselves to adjust stiffness according to the task at hand may be of benefit for active individuals, possibly enhancing the user's satisfaction and comfort during various daily activities. • Three stiffness settings were analyzed with a novel prosthetic foot design. • Dynamic Joint Stiffness can provide insight into a prosthetic foot's dynamics. • Prosthetic foot dynamics and characteristics during gait are task dependent. • Mechanical data does not capture the prosthetic foot's dynamics across gait tasks. • Studies of long-term effects of stiffness change and users´ preferences are needed. [ABSTRACT FROM AUTHOR]

Abstract To convert waste CO 2 from flue gases of power plants into value-added products, bio-mitigation technologies show promise. In this study, we cultivated a fast-growing species of green microalgae, Chlorella vulgaris , in different sizes of photobioreactors (PBRs) and developed a strategy using small doses of sugars for enhancing CO 2 sequestration under light-emitting diode illumination. Glucose supplementation at low levels resulted in an increase of photoautotrophic growth-driven biomass generation as well as CO 2 capture by 10% and its enhancement corresponded to an increase of supplied photon flux. The utilization of urea instead of nitrate as the sole nitrogen source increased photoautotrophic growth by 14%, but change of nitrogen source didn't compromise glucose-induced enhancement of photoautotrophic growth. The optimized biomass productivity achieved was 30.4% higher than the initial productivity of purely photoautotrophic culture. The major pigments in the obtained algal biomass were found comparable to its photoautotrophic counterpart and a high neutral lipids productivity of 516.6 mg/(L·day) was achieved after optimization. A techno-economic model was also developed, indicating that LED-based PBRs represent a feasible strategy for converting CO 2 into value-added algal biomass. Graphical abstract Unlabelled Image Highlights • Fine-tuning mixotrophic culture of microalgae enhances CO 2 bio-fixation. • CO 2 capture in sugar-stimulated algal culture enhanced with increased light supply. • Sugar-stimulated algae have as much pigments as its photoautotrophic counterpart. • A techno-economic model evaluates the feasibility of large-scale algae cultivation. [ABSTRACT FROM AUTHOR]

Abstract: Background: In many published studies, elastic properties of bone are correlated to the bone density, in order to derive an empirical elasticity–density relationship. The most common use of these relationships is the prediction of the bone local properties from medical imaging data in subject-specific numerical simulation studies. The proposed relationships are substantially different one from the other. It is unclear whether such differences in elasticity–density relationships can be entirely explained in terms of methodological discrepancies among studies. Methods: All relevant literature was reviewed. Only elasticity–density relationships derived from similarly controlled experiments were included and properly normalized. The resulting relationships were grouped according to the most important methodological differences: type of end support during testing, specimen geometry, and anatomical sampling location. Findings: Even after normalization with respect to strain rate and densitometric measurement unit, substantial inter-study differences do exist, and they can only be partially explained by the methodological differences between studies. Interpretation: Some recommendations are made for the application of elasticity–density relationships to subject-specific finite element studies. The importance of defining a standardized mechanical testing methodology for bone specimens is stressed, and some guidelines that emerged from the literature are proposed. To identify density–elasticity relationships suitable for use in subject-specific FE studies, the development of a benchmark study is also proposed, where the elasticity–density relationship is taken as the variable under study, and a numerical model of known numerical accuracy predicts experimental strain measurements. [Copyright &y& Elsevier]

The data presented in this article are related to the research article entitled "Sugar-stimulated CO 2 sequestration by the green microalga Chlorella vulgaris " (Fu et al., 2019) [1]. The data describe a rational design and scale-up of LED-based photobioreactors for producing value-added algal biomass while removing waste CO 2 from flu gases from power plants. The dataset were created from growth rate experiments for biomass production including direct biomass productivity data, PBR size and setup parameters, medium composition as well as indirect energy cost and overhead in Iceland. A complete economic analysis is formed through a cost breakdown as well as PBR scalability predictions.