Benchmarking ventricular arrhythmias in the mouse—revisiting the ‘Lambeth Conventions’ 20 years on

The isolated Langendorff-mode perfused heart has become a valuable experimental model, used extensively to examine cardiac function, pathophysiology and pharmacology. For the clinical cardiologist an ECG is often a simple practicality, however in experimental circumstances, particularly with ex vivo murine hearts it is not always possible to obtain an ECG due to experimental recording constraints. However, the mechanical record of ventricular contractile function can be highly informative in relation to electrical state. It is difficult though to achieve consistency in these evaluations of arrhythmia as a validated common reference framework is lacking. In 1988, a group of investigators developed the ‘Lambeth Conventions’—a standardised reference for the definition and classification of arrhythmias in animal experimental models of ischaemia, infarction and reperfusion in vivo. Now, two decades later it is timely to revisit the Lambeth Conventions, and to update the guidelines in the context of the marked increase in murine heart study in experimental cardiac pathophysiology. Here we describe an adjunct to the Lambeth Conventions for the reporting of ventricular arrhythmias post-ischaemia in ex vivo mouse hearts when ECG recordings are not employed. Of seven discrete and identifiable patterns of mechanical dysrhythmia observed in reperfusion, five could be classified using conventional ECG terminology: ventricular premature beat, bigeminy, trigeminy, ventricular tachycardia and ventricular fibrillation. Two additional rhythm variations detected from the pressure record are described (potentiated contraction and alternans).

Using recovery modalities between training sessions in elite athletes: does it help?

Achieving an appropriate balance between training and competition stresses and recovery is important in maximising the performance of athletes. A wide range of recovery modalities are now used as integral parts of the training programmes of elite athletes to help attain this balance. This review examined the evidence available as to the efficacy of these recovery modalities in enhancing between-training session recovery in elite athletes. Recovery modalities have largely been investigated with regard to their ability to enhance the rate of blood lactate removal following high-intensity exercise or to reduce the severity and duration of exercise-induced muscle injury and delayed onset muscle soreness (DOMS). Neither of these reflects the circumstances of between-training session recovery in elite athletes. After high-intensity exercise, rest alone will return blood lactate to baseline levels well within the normal time period between the training sessions of athletes. The majority of studies examining exercise-induced muscle injury and DOMS have used untrained subjects undertaking large amounts of unfamiliar eccentric exercise. This model is unlikely to closely reflect the circumstances of elite athletes. Even without considering the above limitations, there is no substantial scientific evidence to support the use of the recovery modalities reviewed to enhance the between-training session recovery of elite athletes. Modalities reviewed were massage, active recovery, cryotherapy, contrast temperature water immersion therapy, hyperbaric oxygen therapy, nonsteroidal anti-inflammatory drugs, compression garments, stretching, electromyostimulation and combination modalities. Experimental models designed to reflect the circumstances of elite athletes are needed to further investigate the efficacy of various recovery modalities for elite athletes. Other potentially important factors associated with recovery, such as the rate of post-exercise glycogen synthesis and the role of inflammation in the recovery and adaptation process, also need to be considered in this future assessment.

Maternal overnutrition programs changes in the expression of skeletal muscle genes that are associated with insulin resistance and defects of oxidative phosphorylation in adult male rat offspring.

Children of obese mothers have increased risk of metabolic syndrome as adults. Here we report the effects of a high-fat diet in the absence of maternal obesity at conception on skeletal muscle metabolic and transcriptional profiles of adult male offspring. Female Sprague Dawley rats were fed a diet rich in saturated fat and sucrose [high-fat diet (HFD): 23.5% total fat, 9.83% saturated fat, 20% sucrose wt:wt] or a normal control diet [(CD) 7% total fat, 0.5% saturated fat, 10% sucrose wt:wt] for the 3 wk prior to mating and throughout pregnancy and lactation. Maternal weights were not different at conception; however, HFD-fed dams were 22% heavier than controls during pregnancy. On a normal diet, the male offspring of HFD-fed dams were not heavier than controls but demonstrated features of insulin resistance, including elevated plasma insulin concentration [40.1 ± 2.5 (CD) vs 56.2 ± 6.1 (HFD) mU/L; P = 0.023]. Next-generation mRNA sequencing was used to identify differentially expressed genes in the offspring soleus muscle, and gene set enrichment analysis (GSEA) was used to detect coordinated changes that are characteristic of a biological function. GSEA identified 15 upregulated pathways, including cytokine signaling (P < 0.005), starch and sucrose metabolism (P < 0.017), inflammatory response (P < 0.024), and cytokine-cytokine receptor interaction (P < 0.037). A further 8 pathways were downregulated, including oxidative phosphorylation (P < 0.004), mitochondrial matrix (P < 0.006), and electron transport/uncoupling (P < 0.022). Phosphorylation of the insulin signaling protein kinase B was reduced [2.86 ± 0.63 (CD) vs 1.02 ± 0.27 (HFD); P = 0.027] and mitochondrial complexes I, II, and V protein were downregulated by 50-68% (P < 0.005). On a normal diet, the male offspring of HFD-fed dams did not become obese adults but developed insulin resistance, with transcriptional evidence of muscle cytokine activation, inflammation, and mitochondrial dysfunction. These data indicate that maternal overnutrition, even in the absence of prepregnancy obesity, can promote metabolic dysregulation and predispose offspring to type 2 diabetes.

The gut microbiota and inflammatory noncommunicable diseases: associations and potentials for gut microbiota therapies

Rapid environmental transition and modern lifestyles are likely driving changes in the biodiversity of the human gut microbiota. With clear effects on physiologic, immunologic, and metabolic processes in human health, aberrations in the gut microbiome and intestinal homeostasis have the capacity for multisystem effects. Changes in microbial composition are implicated in the increasing propensity for a broad range of inflammatory diseases, such as allergic disease, asthma, inflammatory bowel disease (IBD), obesity, and associated noncommunicable diseases (NCDs). There are also suggestive implications for neurodevelopment and mental health. These diverse multisystem influences have sparked interest in strategies that might favorably modulate the gut microbiota to reduce the risk of many NCDs. For example, specific prebiotics promote favorable intestinal colonization, and their fermented products have anti-inflammatory properties. Specific probiotics also have immunomodulatory and metabolic effects. However, when evaluated in clinical trials, the effects are variable, preliminary, or limited in magnitude. Fecal microbiota transplantation is another emerging therapy that regulates inflammation in experimental models. In human subjects it has been successfully used in cases of Clostridium difficile infection and IBD, although controlled trials are lacking for IBD. Here we discuss relationships between gut colonization and inflammatory NCDs and gut microbiota modulation strategies for their treatment and prevention. (J Allergy Clin Immunol 2015;135:3-13.)

Study of ensemble strategies in discovering linear casual models

Determining the causal structure of a domain is frequently a key task in the area of Data Mining and Knowledge Discovery. This paper introduces ensemble learning into linear causal model discovery, then examines several algorithms based on different ensemble strategies including Bagging, Adaboost and GASEN. Experimental results show that (1) Ensemble discovery algorithm can achieve an improved result compared with individual causal discovery algorithm in terms of accuracy; (2) Among all examined ensemble discovery algorithms, BWV algorithm which uses a simple Bagging strategy works excellently compared to other more sophisticated ensemble strategies; (3) Ensemble method can also improve the stability of parameter estimation. In addition, Ensemble discovery algorithm is amenable to parallel and distributed processing, which is important for data mining in large data sets.

An improved approach for the discovery of causal models via MML

Discovering a precise causal structure accurately reflecting the given data is one of the most essential tasks in the area of data mining and machine learning. One of the successful causal discovery approaches is the information-theoretic approach using the Minimum Message Length Principle[19]. This paper presents an improved and further experimental results of the MML discovery algorithm. We introduced a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. The experimental results of the current version of the discovery system show that: (1) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal models with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.

Ensemble parameter estimation for graphical models

Parameter Estimation is one of the key issues involved in the discovery of graphical models from data. Current state of the art methods have demonstrated their abilities in different kind of graphical models. In this paper, we introduce ensemble learning into the process of parameter estimation, and examine ensemble parameter estimation methods for different kind of graphical models under complete data set and incomplete data set. We provide experimental results which show that ensemble method can achieve an improved result over the base parameter estimation method in terms of accuracy. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.

The discovery of generalized causal models with mixed variables using MML criterion

One major difficulty frustrating the application of linear causal models is that they are not easily adapted to cope with discrete data. This is unfortunate since most real problems involve both continuous and discrete variables. In this paper, we consider a class of graphical models which allow both continuous and discrete variables, and propose the parameter estimation method and a structure discovery algorithm based on Minimum Message Length and parameter estimation. Experimental results are given to demonstrate the potential for the application of this method.

Comparison of static and dynamic engine models on the transient performance of a passenger vehicle powertrain

This paper presents experimental and computational results obtained on the Ford Barra 190 4.0 litres I6 gasoline engine and on the Ford Falcon car equipped with this engine. Measurements of steady engine performance, fuel consumption and exhaust emissions were first collected using an automated test facility for a wide range of cam and spark timings vs. throttle position and engine speed. Simulations were performed for a significant number of measured operating points at full and part load by using a coupled Gamma Technologies GT-POWER/GT-COOL engine model for gas exchange, combustion and heat transfer. The fluid model was made up of intake and exhaust systems, oil circuit, coolant circuit and radiator cooling air circuit. The thermal model was made up of finite element components for cylinder head, cylinder, piston, valves and ports and wall thermal masses for pipes. The model was validated versus measured steady state air and fuel flow rates, cylinder pressure parameters, indicated and brake mean effective pressures, and temperature of metal, oil and coolant in selected locations. Computational results agree well with experiments, demonstrating the ability of the approach to produce fairly accurate steady state maps of BMEP and BSFC, as well as to optimize engine operation changing geometry, throttle position, cam and spark timing. Measurements of the transient performance and fuel consumption of the full vehicle were then collected over the NEDC cycle. Simulations were performed by using a coupled Gamma Technologies GT-POWER/GT-COOL/GT-DRIVE model for instantaneous engine gas exchange, combustion and heat transfer and vehicle motion. The full vehicle model is made up of transmission, driveshaft, axles, and car components and the previous engine model. The model was validated with measured fuel flow rates through the engine, engine throttle position, and engine speed and oil and coolant temperatures in selected locations. Instantaneous engine states following a time dependent demand for torque and speed differ from those obtained by interpolating steady state maps of BSFC vs. BMEP and speed. Computational results agree well with experiments, demonstrating the utility of the approach in providing a more accurate prediction of the fuel consumption over test cycles.

Experimental analysis of friction in forming automotive coated steels

The thesis addressed and answered a number of key issues in the experimental analysis of friction in sheet forming operations. Conventional friction theories were linked with the properties of sheet coatings and the process geometries. Newly derived mathematical models extended the analysis of friction in sheet metal forming applications.

Research contributions to development of particulate reinforced MMCs by theoretical and experimental studies

This thesis is a compilation of eighty publications on analytical, experimental and numerical studies on the mechanical, tribological and corrosion behaviour of metal matrix composites (MMCs). The models based on the interface between matrix and reinforcement behaviour help accurate prediction of density and locked up hysterysis-strain in the composites at elevated temperatures.

High rate filtration using buoyant medium : experiments and mathematical models

In this study, the high rate filtration system using buoyant medium of polypropylene or polystyrene was introduced with the study on its performance evaluation via: (i) specific surface coverage, (ii) ultimate specific deposit, (iii) blocking effect and (iv) particle detachment. The filter system was arranged in the downflow mode with an in-line flocculation. Experimental results obtained were analyzed using filtration models. This study showed that: (i) the specific surface coverage increased with the increase in the velocity; (ii) the ultimate specific deposit changed slightly at different filtration velocities and different flocculants; (iii) the blocking effect was significant in the transient stage removal of flocs; and (iv) the detachment model parameter was found to decrease with the increase in the adhesive force, Fad that acts on the flocs (above-micron size).

Suitability of chlorine bulk decay models for planning and management of water distribution systems

Effective disinfection planning and management in large, complex water distribution systems requires an accurate network water quality model. This model should be based on reaction kinetics, which describes disinfectant loss from bulk water over time, within experimental error. Models in the literature were reviewed for their ability to meet this requirement in real networks. Essential features were identified as accuracy, simplicity, computational efficiency, and ability to describe consistently the effects of initial chlorine dose, temperature variation, and successive rechlorinations. A reaction scheme of two organic constituents reacting with free chlorine was found to be necessary and sufficient to provide the required features. Recent release of the multispecies extension (MSX) to EPANET and MWH Soft's H2OMap Water MSX network software enables users to implement this and other multiple-reactant bulk decay models in real system simulations.

Learning discriminative sequence models from partially labelled data for activity recognition

Recognising daily activity patterns of people from low-level sensory data is an important problem. Traditional approaches typically rely on generative models such as the hidden Markov models and training on fully labelled data. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support fully supervised learning is often expensive. In this paper, we propose a new approach based on partially-supervised training of discriminative sequence models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that the approach can reduce labelling effort, and at the same time, provides us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart (i.e. the partially hidden Markov model), even when a substantial amount of labels are unavailable.

Human activity learning and segmentation using partially hidden discriminative models

Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised learning and generative models such as the hidden Markov models and its extensions. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support supervised training is often extremely expensive. In this paper, we propose a new approach based on semi-supervised training of partially hidden discriminative models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that these models allow us to incorporate both labeled and unlabeled data for learning, and at the same time, provide us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart, the partially hidden Markov model, even when a substantial amount of labels are unavailable.