Artificial immune systems

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self or nonself substances. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years

Two years after molecular diagnosis of familial hypercholesterolemia: Majority on cholesterol-lowering treatment but a minority reaches treatment goal

Background: The risk of premature cardiovascular disease in patients with familial hypercholesterolemia (FH) can be profoundly reduced by cholesterol-lowering therapy, and current guidelines for FH advocate ambitious low-density lipoprotein cholesterol (LDL-C) goals. In the present study, we determined whether these goals are reflected in current clinical practice once FH has been diagnosed. Methodology/Principal Findings: In 2008, we sent questionnaires to all subjects (aged 18-65 years) who were molecularly diagnosed with FH in the year 2006 through the screening program in the Netherlands. Of these 1062 subjects, 781 completed the questionnaire (46% males; mean age: 42±12 years; mean LDL-C at molecular diagnosis (baseline): 4.1±1.3 mmol/L). The number of persons that used cholesterol-lowering therapy increased from 397 (51%) at baseline to 636 (81%) after diagnosis. Mean treated LDL-C levels decreased significantly to 3.2±1.1 mmol/L two years after diagnosis. Only 22% achieved the LDL-C target level of ≤2.5 mmol/L. Conclusions/Significance: The proportion of patients using cholesterol-lowering medication was significantly increased after FH diagnosis through

An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data

Citation: Shi, Z. Z., Chapes, S. K., Ben-Arieh, D., & Wu, C. H. (2016). An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data. Plos One, 11(8), 39. doi:10.1371/journal.pone.0161131We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-a ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies

Neural substrates of individual differences in human fear learning: Evidence from concurrent fMRI, fear-potentiated startle, and US-expectancy data

To provide insight into individual differences in fear learning, we examined the emotional and cognitive expressions of discriminative fear conditioning in direct relation to its neural substrates. Contrary to previous behavioral–neural (fMRI) research on fear learning—in which the emotional expression of fear was generally indexed by skin conductance—we used fear-potentiated startle, a more reliable and specific index of fear. While we obtained concurrent fear-potentiated startle, neuroimaging (fMRI), and US-expectancy data, healthy participants underwent a fear-conditioning paradigm in which one of two conditioned stimuli (CS(+) but not CS(–)) was paired with a shock (unconditioned stimulus [US]). Fear learning was evident from the differential expressions of fear (CS(+) > CS(–)) at both the behavioral level (startle potentiation and US expectancy) and the neural level (in amygdala, anterior cingulate cortex, hippocampus, and insula). We examined individual differences in discriminative fear conditioning by classifying participants (as conditionable vs. unconditionable) according to whether they showed successful differential startle potentiation. This revealed that the individual differences in the emotional expression of discriminative fear learning (startle potentiation) were reflected in differential amygdala activation, regardless of the cognitive expression of fear learning (CS–US contingency or hippocampal activation). Our study provides the first evidence for the potential of examining startle potentiation in concurrent fMRI research on fear learning

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens

Background: Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. Methods: A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5′ and 3′ untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernelbased ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Results: Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. Conclusions: All genic and non-genic regions contributed to phenotypic variation for the three traits studied. Overall, the contribution of additive genetic variance to the total genetic variance was much greater than that of dominance variance. Our results show that all genomic regions are important for the prediction of the targeted traits, and the whole-genome approach was reaffirmed as the best tool for genome-enabled prediction of quantitative traits

Neuro-musculoskeletal simulation of instrumented contracture and spasticity assessment in children with cerebral palsy

BACKGROUND: Increased resistance in muscles and joints is an important phenomenon in patients with cerebral palsy (CP), and is caused by a combination of neural (e.g. spasticity) and non-neural (e.g. contracture) components. The aim of this study was to simulate instrumented, clinical assessment of the hamstring muscles in CP using a conceptual model of contracture and spasticity, and to determine to what extent contracture can be explained by altered passive muscle stiffness, and spasticity by (purely) velocity-dependent stretch reflex. METHODS: Instrumented hamstrings spasticity assessment was performed on 11 children with CP and 9 typically developing children. In this test, the knee was passively stretched at slow and fast speed, and knee angle, applied forces and EMG were measured. A dedicated OpenSim model was created with motion and muscles around the knee only. Contracture was modeled by optimizing the passive muscle stiffness parameters of vasti and hamstrings, based on slow stretch data. Spasticity was modeled using a velocity-dependent feedback controller, with threshold values derived from experimental data and gain values optimized for individual subjects. Forward dynamic simulations were performed to predict muscle behavior during slow and fast passive stretches. RESULTS: Both slow and fast stretch data could be successfully simulated by including subject-specific levels of contracture and, for CP fast stretches, spasticity. The RMS errors of predicted knee motion in CP were 1.1 ± 0.9° for slow and 5.9 ± 2.1° for fast stretches. CP hamstrings were found to be stiffer compared with TD, and both hamstrings and vasti were more compliant than the original generic model, except for the CP hamstrings. The purely velocity-dependent spasticity model could predict response during fast passive stretch in terms of predicted knee angle, muscle activity, and fiber length and velocity. Only sustained muscle activity, independent of velocity, was not predicted by our model. CONCLUSION: The presented individually tunable, conceptual model for contracture and spasticity could explain most of the hamstring muscle behavior during slow and fast passive stretch. Future research should attempt to apply the model to study the effects of spasticity and contracture during dynamic tasks such as gait. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12984-016-0170-5) contains supplementary material, which is available to authorized users