Discrete-time dynamic modeling for software and services composition as an extension of the Markov chain approach

Discrete Time Markov Chains (DTMCs) and Continuous Time Markov Chains (CTMCs) are often used to model various types of phenomena, such as, for example, the behavior of software products. In that case, Markov chains are widely used to describe possible time-varying behavior of “self-adaptive” software systems, where the transition from one state to another represents alternative choices at the software code level, taken according to a certain probability distribution. From a control-theoretical standpoint, some of these probabilities can be interpreted as control signals and others can just be observed. However, the translation between a DTMC or CTMC model and a corresponding first principle model, that can be used to design a control system is not immediate. This paper investigates a possible solution for translating a CTMC model into a dynamic system, with focus on the control of computing systems components. Notice that DTMC models can be translated as well, providing additional information

Physiology of renal glucose handling via SGLT1, SGLT2 and GLUT2.

The concentration of glucose in plasma is held within narrow limits (4-10 mmol/l), primarily to ensure fuel supply to the brain. Kidneys play a role in glucose homeostasis in the body by ensuring that glucose is not lost in the urine. Three membrane proteins are responsible for glucose reabsorption from the glomerular filtrate in the proximal tubule: sodium-glucose cotransporters SGLT1 and SGLT2, in the apical membrane, and GLUT2, a uniporter in the basolateral membrane. 'Knockout' of these transporters in mice and men results in the excretion of filtered glucose in the urine. In humans, intravenous injection of the plant glucoside phlorizin also results in excretion of the full filtered glucose load. This outcome and the finding that, in an animal model, phlorizin reversed the symptoms of diabetes, has stimulated the development and successful introduction of SGLT2 inhibitors, gliflozins, in the treatment of type 2 diabetes mellitus. Here we summarise the current state of our knowledge about the physiology of renal glucose handling and provide background to the development of SGLT2 inhibitors for type 2 diabetes treatment

A high-throughput screen identifies that CDK7 activates glucose consumption in lung cancer cells.

Elevated glucose consumption is fundamental to cancer, but selectively targeting this pathway is challenging. We develop a high-throughput assay for measuring glucose consumption and use it to screen non-small-cell lung cancer cell lines against bioactive small molecules. We identify Milciclib that blocks glucose consumption in H460 and H1975, but not in HCC827 or A549 cells, by decreasing SLC2A1 (GLUT1) mRNA and protein levels and by inhibiting glucose transport. Milciclib blocks glucose consumption by targeting cyclin-dependent kinase 7 (CDK7) similar to other CDK7 inhibitors including THZ1 and LDC4297. Enhanced PIK3CA signaling leads to CDK7 phosphorylation, which promotes RNA Polymerase II phosphorylation and transcription. Milciclib, THZ1, and LDC4297 lead to a reduction in RNA Polymerase II phosphorylation on the SLC2A1 promoter. These data indicate that our high-throughput assay can identify compounds that regulate glucose consumption and that CDK7 is a key regulator of glucose consumption in cells with an activated PI3K pathway

Using Graph Transformation Systems to Specify and Verify Data Abstractions

This paper proposes an approach for the specification of the behavior of software components that implement data abstractions. By generalizing the approach of behavior models using graph transformation, we provide a concise specification for data abstractions that describes the relationship between the internal state, represented in a canonical form, and the observers of the component. Graph transformation also supports the generation of behavior models that are amenable to verification. To this end, we provide a translation approach into an LTL model on which we can express useful properties that can be model-checked with a SAT solver

Using machine learning for automatic identification of evidence-based health information on the web

Automatic assessment of the quality of online health information is a need especially with the massive growth of online content. In this paper, we present an approach to assessing the quality of health webpages based on their content rather than on purely technical features, by applying machine learning techniques to the automatic identification of evidence-based health information. Several machine learning approaches were applied to learn classifiers using different combinations of features. Three datasets were used in this study for three different diseases, namely shingles, flu and migraine. The results obtained using the classifiers were promising in terms of precision and recall especially with diseases with few different pathogenic mechanisms

Attractions between charged colloids at water interfaces

The effective potential between charged colloids trapped at water interfaces is analyzed. It consists of a repulsive electrostatic and an attractive capillary part which asymptotically both show dipole--like behavior. For sufficiently large colloid charges, the capillary attraction dominates at large separations. The total effective potential exhibits a minimum at intermediate separations if the Debye screening length of water and the colloid radius are of comparable size.Comment: 8 pages, 1 figure, revised version (one paragraph added) accepted in JPC

Self-efficacy configurations and wellbeing in the academic context: A person-centred approach

The aim of the present study was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centred approach. Results from a two-cohort sample (N = 1650) assessed at the beginning of their first year supported a 4-cluster solution: 1) Highly Self-Efficacious students, with high levels of self-efficacy in all domains; 2) Low Self-Efficacious students, with low levels of self-efficacy in all domains; 3) Learning and Socially Self-Efficacious students, with a medium-high level of self-regulated learning, medium level of social, and medium-low level of emotional self-efficacies; and 4) Emotionally Self-Efficacious students, with a medium-high level of emotional, medium-low level of social, and low level of self-regulated learning self-efficacies. The association of these configurations with wellbeing indicators, concurrently and one year later, provides support for the validity of the cluster solution. Specifically, by adopting the informative hypothesis testing approach, results showed that the first and second groups have the best and the worst wellbeing levels, respectively. Furthermore, whereas the other two groups did not differ with respect to depression, Learning and Socially Self-Efficacious students have higher life satisfaction than the last group. These results were confirmed both concurrently and over time

A Metric Encoding for Bounded Model Checking (extended version)

In Bounded Model Checking both the system model and the checked property are translated into a Boolean formula to be analyzed by a SAT-solver. We introduce a new encoding technique which is particularly optimized for managing quantitative future and past metric temporal operators, typically found in properties of hard real time systems. The encoding is simple and intuitive in principle, but it is made more complex by the presence, typical of the Bounded Model Checking technique, of backward and forward loops used to represent an ultimately periodic infinite domain by a finite structure. We report and comment on the new encoding technique and on an extensive set of experiments carried out to assess its feasibility and effectiveness

18F-FAC PET Visualizes Brain-Infiltrating Leukocytes in a Mouse Model of Multiple Sclerosis.

Brain-infiltrating leukocytes contribute to multiple sclerosis (MS) and autoimmune encephalomyelitis and likely play a role in traumatic brain injury, seizure, and stroke. Brain-infiltrating leukocytes are also primary targets for MS disease-modifying therapies. However, no method exists for noninvasively visualizing these cells in a living organism. 1-(2'-deoxy-2'-18F-fluoroarabinofuranosyl) cytosine (18F-FAC) is a PET radiotracer that measures deoxyribonucleoside salvage and accumulates preferentially in immune cells. We hypothesized that 18F-FAC PET could noninvasively image brain-infiltrating leukocytes. Methods: Healthy mice were imaged with 18F-FAC PET to quantify if this radiotracer crosses the blood-brain barrier (BBB). Experimental autoimmune encephalomyelitis (EAE) is a mouse disease model with brain-infiltrating leukocytes. To determine whether 18F-FAC accumulates in brain-infiltrating leukocytes, EAE mice were analyzed with 18F-FAC PET, digital autoradiography, and immunohistochemistry, and deoxyribonucleoside salvage activity in brain-infiltrating leukocytes was analyzed ex vivo. Fingolimod-treated EAE mice were imaged with 18F-FAC PET to assess if this approach can monitor the effect of an immunomodulatory drug on brain-infiltrating leukocytes. PET scans of individuals injected with 2-chloro-2'-deoxy-2'-18F-fluoro-9-β-d-arabinofuranosyl-adenine (18F-CFA), a PET radiotracer that measures deoxyribonucleoside salvage in humans, were analyzed to evaluate whether 18F-CFA crosses the human BBB. Results: 18F-FAC accumulates in the healthy mouse brain at levels similar to 18F-FAC in the blood (2.54 ± 0.2 and 3.04 ± 0.3 percentage injected dose per gram, respectively) indicating that 18F-FAC crosses the BBB. EAE mice accumulate 18F-FAC in the brain at 180% of the levels of control mice. Brain 18F-FAC accumulation localizes to periventricular regions with significant leukocyte infiltration, and deoxyribonucleoside salvage activity is present at similar levels in brain-infiltrating T and innate immune cells. These data suggest that 18F-FAC accumulates in brain-infiltrating leukocytes in this model. Fingolimod-treated EAE mice accumulate 18F-FAC in the brain at 37% lower levels than control-treated EAE mice, demonstrating that 18F-FAC PET can monitor therapeutic interventions in this mouse model. 18F-CFA accumulates in the human brain at 15% of blood levels (0.08 ± 0.01 and 0.54 ± 0.07 SUV, respectively), indicating that 18F-CFA does not cross the BBB in humans. Conclusion: 18F-FAC PET can visualize brain-infiltrating leukocytes in a mouse MS model and can monitor the response of these cells to an immunomodulatory drug. Translating this strategy into humans will require exploring additional radiotracers