Data-Driven Parameter Selection and Modeling for Concrete Carbonation

Concrete carbonation is known as a stochastic process. Its uncertainties mainly result from parameters that are not considered in prediction models. Parameter selection, therefore, is important. In this paper, based on 8204 sets of data, statistical methods and machine learning techniques were applied to choose appropriate influence factors in terms of three aspects: (1) the correlation between factors and concrete carbonation; (2) factors’ influence on the uncertainties of carbonation depth; and (3) the correlation between factors. Both single parameters and parameter groups were evaluated quantitatively. The results showed that compressive strength had the highest correlation with carbonation depth and that using the aggregate–cement ratio as the parameter significantly reduced the dispersion of carbonation depth to a low level. Machine learning models manifested that selected parameter groups had a large potential in improving the performance of models with fewer parameters. This paper also developed machine learning carbonation models and simplified them to propose a practical model. The results showed that this concise model had a high accuracy on both accelerated and natural carbonation test datasets. For natural carbonation datasets, the mean absolute error of the practical model was 1.56 mm.     

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature.     

Cohort Size and Maximum Likelihood Estimation of Mortality Parameters

The purpose of this study was to investigate the effects of cohort size on maximum likelihood estimates of mortality parameters. Recent experimental investigations have stressed the importance of large cohorts for detecting leveling off of mortality rates at older ages. In the present study, emphasis was placed on evaluation of relatively small cohorts (about 150–300 individuals). Deaths were simulated under the assumption of the frailty mortality model. Two different parameter sets that resulted in differences in mean life span of more than twofold were used for simulations. Our smallest cohorts yielded parameter estimates that had generally good statistical properties, but relatively large standard errors. For tests of hypotheses concerning equality of parameters among populations or experimental treatments, empirical standard errors (obtained from several cohorts) were preferable to asymptotic standard errors (obtained for single cohorts). In particular, empirical standard errors yielded reliable type I error rates.     

Magnetic resonance imaging of neural circuits

A major goal of modern MRI research is to be able to image neural circuits in the central nervous system. Critical to this mission is the ability to describe a number of important parameters associated with neural circuits. These parameters include neural architecture, functional activation of neural circuits, anatomical and functional connectivity of neural circuits, and factors that might alter neural circuits, such as trafficking of immune cells and brain precursor cells in the brain. Remarkably, a variety of work in human and animal brains has demonstrated that all these features of neural circuits can be visualized with MRI. In this Article we provide a brief summary of the new directions in neural imaging research, which should prove useful in future analyses of normal and pathological human brains and in studies of animal models of neurological and psychiatric disorders. At present, few MRI data characterizing the neural circuits in the heart are available, but in this Article we discuss the applicable present developments and the prospects for the future.     

A mutation in the human cardiac sodium channel (E161K) contributes to sick sinus syndrome, conduction disease and Brugada syndrome in two families

BACKGROUND: Mutations in the gene encoding the human cardiac sodium channel (SCN5A) have been associated with three distinct cardiac arrhythmia disorders: the long QT syndrome, the Brugada syndrome and cardiac conduction disease. Here we report the biophysical features of a novel sodium channel mutation, E161K, which we identified in individuals of two non-related families with symptoms of bradycardia, sinus node dysfunction, generalized conduction disease and Brugada syndrome, or combinations thereof. METHODS AND RESULTS: Wild-type (WT) or E161K sodium channel alpha-subunit and beta-subunit were cotransfected into tsA201 cells to study the functional consequences of mutant sodium channels. Characterization of whole-cell sodium current (I(Na)) using the whole cell patch-clamp technique revealed that the E161K mutation caused an almost threefold reduction in current density (P < 0.001), and an 11.9 mV positive shift of the voltage-dependence of activation (P < 0.0001). The inactivation properties of mutant and WT sodium channels were similar. These results suggest an overall reduction of E161K I(Na). Incorporation of the experimental findings into computational models demonstrate atrial and ventricular conduction slowing as well as a reduction in sinus rate by slowing of the diastolic depolarization rate and upstroke velocity of the sinus node action potential. This reduction in sinus rate was aggravated by application of acetylcholine, simulating the dominant vagal tone during night. CONCLUSION: Our experimental and computational analysis of the E161K mutation suggests that a loss of sodium channel function is not only associated with Brugada syndrome and conduction disease, but may also cause sinus node dysfunction in carriers of this mutation.     

Direct Estimation of General Pulmonary Vascular Models from Occlusion Experiments

Occlusion experiments yield time–pressure and time–flow curves which are related to the longitudinal distribution of compliances and resistances in the pulmonary circulation. The standard approach to the analysis of these curves involves the observation of relevant features of their graphs, which may directly reflect model parameter values. The present work considers five possible models of pulmonary vascular pressure dynamics and the relative (nonlinear) least-squares parameter estimation from experimental data, making simultaneous use of all available information. In situ isolated perfused and ventilated pig lung preparations were used, and pressure and flow changes during arterial, double, and venous occlusion maneuvers were measured. The five models considered included two linear models without inductance units, one linear model with inductance units, one nonlinear model with variable resistance, and one nonlinear model with variable compliance. In all cases parameter estimation for the numerically integrated model was performed by unweighted least squares, using a variable-metric minimization technique. Comparisons between competing models were based on parameter identifiability and on the Akaike Information Criterion (AIC), concluding that significant nonlinearity in the response of the pig lung resistance to variations in pressure is present for the analyzed data sets.     

Computational models reduce complexity and accelerate insight into cardiac signaling networks

Cardiac signaling networks exhibit considerable complexity in size and connectivity. The intrinsic complexity of these networks complicates the interpretation of experimental findings. This motivates new methods for investigating the mechanisms regulating cardiac signaling networks and the consequences these networks have on cardiac physiology and disease. Next-generation experimental techniques are also generating a wealth of genomic and proteomic data that can be difficult to analyze or interpret. Computational models are poised to play a key role in addressing these challenges. Computational models have a long history in contributing to the understanding of cardiac physiology and are useful for identifying biological mechanisms, inferring multiscale consequences to cell signaling activities and reducing the complexity of large data sets. Models also integrate well with experimental studies to explain experimental observations and generate new hypotheses. Here, we review the contributions computational modeling approaches have made to the analysis of cardiac signaling networks and forecast opportunities for computational models to accelerate cardiac signaling research.     

Computational and experimental analysis of DNA shuffling

We describe a computational model of DNA shuffling based on the thermodynamics and kinetics of this process. The model independently tracks a representative ensemble of DNA molecules and records their states at every stage of a shuffling reaction. These data can subsequently be analyzed to yield information on any relevant metric, including reassembly efficiency, crossover number, type and distribution, and DNA sequence length distributions. The predictive ability of the model was validated by comparison to three independent sets of experimental data, and analysis of the simulation results led to several unique insights into the DNA shuffling process. We examine a tradeoff between crossover frequency and reassembly efficiency and illustrate the effects of experimental parameters on this relationship. Furthermore, we discuss conditions that promote the formation of useless "junk" DNA sequences or multimeric sequences containing multiple copies of the reassembled product. This model will therefore aid in the design of optimal shuffling reaction conditions.     

Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language

We introduce an experimental paradigm for studying the cumulative cultural evolution of language. In doing so we provide the first experimental validation for the idea that cultural transmission can lead to the appearance of design without a designer. Our experiments involve the iterated learning of artificial languages by human participants. We show that languages transmitted culturally evolve in such a way as to maximize their own transmissibility: over time, the languages in our experiments become easier to learn and increasingly structured. Furthermore, this structure emerges purely as a consequence of the transmission of language over generations, without any intentional design on the part of individual language learners. Previous computational and mathematical models suggest that iterated learning provides an explanation for the structure of human language and link particular aspects of linguistic structure with particular constraints acting on language during its transmission. The experimental work presented here shows that the predictions of these models, and models of cultural evolution more generally, can be tested in the laboratory.     

Rough Surface Characterization Parameter Set and Redundant Parameter Set for Surface Modeling and Performance Research

Among the 26 roughness parameters described in ISO 25178 standard, the parameters used to characterize surface performance in characterization parameter set (CPS) lack scientificity and unity, resulting in application confusion. The current CPS comes from empirical selection or small sample experiments, thus featuring low generality. A new method for constructing CPS in rough surfaces is proposed to solve the above issues. Based on a data mining method, statistical theory, and roughness parameters definitions, the 26 roughness parameters are divided into CPS and redundant parameter sets (RPS) with the help of reconstructed surfaces and machining experiments, and the mapping relationships between CPS and RPS are established. The research shows that RPS accounts for 50%, and CPS, of great significance for surface performance, and has the ability to fully cover surface topography information. The birth of CPS provides an accurate parameter set for the subsequent study of different surface performance, and it provides more effective parameters for evaluating the workpiece surface performance from the same batch.     

Distinct neural signatures of threat learning in adolescents and adults

Most teenage fears subside with age, a change that may reflect brain maturation in the service of refined fear learning. Whereas adults clearly demarcate safe situations from real dangers, attenuating fear to the former but not the latter, adolescents' immaturity in prefrontal cortex function may limit their ability to form clear-cut threat categories, allowing pervasive fears to manifest. Here we developed a discrimination learning paradigm that assesses the ability to categorize threat from safety cues to test these hypotheses on age differences in neurodevelopment. In experiment 1, we first demonstrated the capacity of this paradigm to generate threat/safety discrimination learning in both adolescents and adults. Next, in experiment 2, we used this paradigm to compare the behavioral and neural correlates of threat/safety discrimination learning in adolescents and adults using functional MRI. This second experiment yielded three sets of findings. First, when labeling threats online, adolescents reported less discrimination between threat and safety cues than adults. Second, adolescents were more likely than adults to engage early-maturing subcortical structures during threat/safety discrimination learning. Third, adults' but not adolescents' engagement of late-maturing prefrontal cortex regions correlated positively with fear ratings during threat/safety discrimination learning. These data are consistent with the role of dorsolateral regions during category learning, particularly when differences between stimuli are subtle [Miller EK, Cohen JD (2001) Annu Rev Neurosci 24:167-202]. These findings suggest that maturational differences in subcortical and prefrontal regions between adolescent and adult brains may relate to age-related differences in threat/safety discrimination.     

Sound Insulation of Corrugated-Core Sandwich Panels: Modeling,Optimization and Experiment

With the extension of the applications of sandwich panels with corrugated core, sound insulation performance has been a great concern for acoustic comfort design in many industrial fields. This paper presents a numerical and experimental study on the vibro-acoustic optimization of a finite size sandwich panel with corrugated core for maximizing the sound transmission loss. The numerical model is established by using the wave-based method, which shows a great improvement in the computational efficiency comparing to the finite element method. Constrained by the fundamental frequency and total mass, the optimization is performed by using a genetic algorithm in three different frequency bands. According to the optimization results, the frequency averaged sound transmission of the optimized models in the low, middle, and high-frequency ranges has increased, respectively, by 7.6 dB, 7.9 dB, and 11.7 dB compared to the baseline model. Benefiting from the vast number of the evolution samples, the correlation between the structural design parameters and the sound transmission characteristics is analyzed by introducing the coefficient of determination, which gives the variation of the importance of each design parameter in different frequency ranges. Finally, for validation purposes, a sound insulation test is conducted to validate the optimization results in the high-frequency range, which proves the feasibility of the optimization method in the practical engineering design of the sandwich panel.     

Probabilistic Assessment of the Dynamic Viscosity of Self-Compacting Steel-Fiber Reinforced Concrete through a Micromechanical Model

This article develops a probabilistic approach to a micromechanical model to calculate the dynamic viscosity in self-compacting steel-fiber reinforced concrete (SCSFRC), which implies a paradigm shift in the approach of the deterministic models used. It builds on a previous work by the authors in which Bayesian analysis is applied to rheological micromechanical models in cement paste, self-compacting mortar, and self-compacting concrete. As a consequence of the varied characteristics of the particles in these suspensions (in terms of materials, shapes, size distributions, etc.), as well as their random nature, it seems appropriate to study these systems with probabilistic models. The Bayesian analysis, thorough Markov Chain Monte Carlo and Gibbs Sampling methods, allows the conversion of parametric-deterministic models into parametric-probabilistic models, which results in enrichment in engineering and science. The incorporation of steel fibers requires a new term in the model to account for their effect on the dynamic viscosity of SCSFRC, and this new term is also treated here with the Bayesian approach. The paper uses an extensive collection of experimental data to obtain the probability density functions of the parameters for assessing the dynamic viscosity in SCSFRC. The results obtained with these parameters’ distributions are much better than those calculated with the theoretical values of the parameters, which indicates that Bayesian methods are appropriated to respond to questions in complex systems with complex models.     

Increased lipid peroxidation and mitochondrial dysfunction in aceruloplasminemia brains

Aceruloplasminemia is characterized by iron accumulation in the brain as well as in visceral organs, due to the absence of ceruloplasmin ferroxidase activity. The neurological symptoms, which include involuntary movements, ataxia, and dementia, reflect the sites of iron deposition. The unique involvement of the central nervous system distinguishes aceruloplasminemia from other inherited and acquired iron storage disorders. Excess iron functions as a potent catalyst of biologic oxidation. An increased iron concentration was associated with increased lipid peroxidation in the brains of three aceruloplasminemia patients. Positron emission tomography showed brain glucose and oxygen hypometabolism. Enzyme activities in the mitochondrial respiratory chain of the basal ganglia were reduced to about 50 and 43%, respectively, for complexes I and IV. Those of the cerebral and cerebellar cortices also were decreased approximately 62 and 65%. These findings suggest that iron-mediated free radicals may contribute to neuronal cell damage through increased lipid peroxidation and the impairment of mitochondrial energy metabolism in aceruloplasminemia brains.     

非高密度脂蛋白胆固醇与载脂蛋白B在心血管疾病风险评估中的比较

非高密度脂蛋白胆固醇水平反映致动脉粥样硬化胆固醇总量,载脂蛋白B浓度反应致动脉粥样硬化颗粒总数。近期研究发现非高密度脂蛋白胆固醇和载脂蛋白B的心血管疾病风险预测价值优于传统指标低密度脂蛋白胆固醇。文章就这两个指标的心血管风险评估效能及临床操作性能做一综述。     

The experimental paradigm and observational studies of cause-effect relationships in clinical medicine

The paradigm of the randomized clinical trial (RCT) is proposed as a heuristic that can serve as a unified approach to guide the design not just of cause-effect studies of therapy, but also of studies of the etiology of disease. Three themes are developed in detail: that variability in the scientific paradigm of the randomized trial results in a wide range of techniques and methods being employed for the RCT, and that this extensive variability in clinical trial methods contributes substantially to the occurrence of conflicting trial results; that the scientific validity of observational surrogates for the RCT could be enhanced if investigators designed observational studies by incorporating the principles inherent in the RCT; and that there are two distinctive and competing strategies for designing case-control studies. The traditional strategy views case-control designs as statistical acts of sampling for cases and controls, but ignores the scientific reasoning that should guide the performance of case-control research. The alternative strategy requires that case-control studies adhere to the principles inherent in the RCT.     

Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally.     

Hypothesis: the chaos and complexity theory may help our understanding of fibromyalgia and similar maladies

BACKGROUND: Modern clinicians are often frustrated by their inability to understand fibromyalgia and similar maladies since these illnesses cannot be explained by the prevailing linear-reductionist medical paradigm. OBJECTIVE: This article proposes that new concepts derived from the Complexity Theory may help understand the pathogenesis of fibromyalgia, chronic fatigue syndrome, and Gulf War syndrome. METHODS: This hypothesis is based on the recent recognition of chaos fractals and complex systems in human physiology. RESULTS: These nonlinear dynamics concepts offer a different perspective to the notion of homeostasis and disease. They propose that the essence of disease is dysfunction and not structural damage. Studies using novel nonlinear instruments have shown that fibromyalgia and similar maladies may be caused by the degraded performance of our main complex adaptive system. This dysfunction explains the multifaceted manifestations of these entities. CONCLUSIONS: To understand and alleviate the suffering associated with these complex illnesses, a paradigm shift from reductionism to holism based on the Complexity Theory is suggested. This shift perceives health as resilient adaptation and some chronic illnesses as rigid dysfunction.     

Rejuvenation of aged hematopoietic stem cells

Until recently, there was broad consensus in the stem cell aging field that the phenotype of aged hematopoietic stem cells (HSCs) is fixed—dominated by cell-intrinsic regulatory mechanisms that cannot be altered by pharmacological or genetic means. The conventional thinking was that HSC aging could not be reverted by therapeutic intervention. This paradigm has started to shift dramatically, primarily because hallmarks of aged HSCs have been successfully reverted by distinct experimental approaches by multiple laboratories. We will discuss in this review these hallmarks of HSCs aging and the novel approaches that successfully ameliorated or even reverted aging-associated hallmarks of aged HSCs.     

Are the current attempts at standardization of antiphospholipid antibodies still useful? Emerging technologies signal a shift in direction

The pathogenic role of antiphospholipid antibodies (aPL) has been widely established over past years in several experimental models and clinical studies. Accordingly, the detection of aPL by immunoassays (anticardiolipin antibodies; anti-beta2 glycoprotein I antibodies) has become a routine practice in the clinical workup of patients with systemic autoimmune diseases. aPL are mostly assayed using commercial ELISA kits, whose performance has not been found to be sufficiently concordant among the different manufacturers. In the past years, collaborative groups have spent considerable effort to reach some form of standardization but this process is still ongoing. Such lack of standardization has recently become even more crucial, as manufacturers have had to face an increasing demand for fully automated tests for aPL, like those test systems that have been developed for other autoantibodies (e.g., antinuclear antibodies, anti-ENA antibodies). We therefore report our recent experience with two newly developed automated methods for anticardiolipin antibodies testing. In particular, we discuss the results obtained using routine samples, as we believe that these better reflect the "real-life" situation in which those automated methods will operate. We also mention other emerging technologies in the field of aPL detection.