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Due to the diffusion of IoT, modern software systems are often thought to control and coordinate smart devices in order to manage assets and resources, and to guarantee efficient behaviours. For this class of systems, which interact extensively with humans and with their environment, it is thus crucial to guarantee their correct behaviour in order to avoid unexpected and possibly dangerous situations. In this paper we will present a framework that allows us to measure the robustness of systems. This is the ability of a program to tolerate changes in the environmental conditions and preserving the original behaviour. In the proposed framework, the interaction of a program with its environment is represented as a sequence of random variables describing how both evolve in time. For this reason, the considered measures will be defined among probability distributions of observed data. The proposed framework will be then used to define the notions of adaptability and reliability. The former indicates the ability of a program to absorb perturbation on environmental conditions after a given amount of time. The latter expresses the ability of a program to maintain its intended behaviour (up-to some reasonable tolerance) despite the presence of perturbations in the environment. Moreover, an algorithm, based on statistical inference, is proposed to evaluate the proposed metric and the aforementioned properties. We use two case studies to the describe and evaluate the proposed approach.

This paper studies the existence of finite equational axiomatisations of the interleaving parallel composition operator modulo the behavioural equivalences in van Glabbeek's linear time-branching time spectrum. In the setting of the process algebra BCCSP over a finite set of actions, we provide finite, ground-complete axiomatisations for various simulation and (decorated) trace semantics. We also show that no congruence over BCCSP that includes bisimilarity and is included in possible futures equivalence has a finite, ground-complete axiomatisation; this negative result applies to all the nested trace and nested simulation semantics.

The combination of nondeterminism and probability in concurrent systems lead to the development of several interpretations of process behavior. If we restrict our attention to linear properties only, we can identify three main approaches to trace and testing semantics: the trace distributions, the trace-by-trace and the extremal probabilities approaches. In this paper, we propose novel notions of behavioral metrics that are based on the three classic approaches above, and that can be used to measure the disparities in the linear behavior of processes wrt trace and testing semantics. We study the properties of these metrics, like non-expansiveness, and we compare their expressive powers.

In this paper we continue our research line on logical characterizations of behavioral metrics obtained from the definition of a metric over the set of logical properties of interest. This time we provide a characterization of both strong and weak trace metric on nondeterministic probabilistic processes, based on a minimal boolean logic L which we prove to be powerful enough to characterize strong and weak probabilistic trace equivalence. Moreover, we also prove that our characterization approach can be restated in terms of a more classic probabilistic L-model checking problem.

Bisimulation metrics provide a robust and accurate approach to study the behavior of nondeterministic probabilistic processes. In this paper, we propose a logical characterization of bisimulation metrics based on a simple probabilistic variant of the Hennessy-Milner logic. Our approach is based on the novel notions of mimicking formulae and distance between formulae. The former are a weak version of the well known characteristic formulae and allow us to characterize also (ready) probabilistic simulation and probabilistic bisimilarity. The latter is a 1-bounded pseudometric on formulae that mirrors the Hausdorff and Kantorovich lifting the defining bisimilarity pseudometric. We show that the distance between two processes equals the distance between their own mimicking formulae.

We propose a method for the decomposition of modal formulae on processes with nondeterminism and probability with respect to Structural Operational Semantics. The purpose is to reduce the satisfaction problem of a formula for a process to verifying whether its subprocesses satisfy certain formulae obtained from the decomposition. To deal with the probabilistic behavior of processes, and thus with the decomposition of formulae characterizing it, we introduce a SOS-like machinery allowing for the specification of the behavior of open distribution terms. By our decomposition, we obtain (pre)congruence formats for probabilistic bisimilarity, ready similarity and similarity.

Neuronal disorders, like Huntington's disease (HD), are difficult to study, due to limited cell accessibility, late onset manifestations, and low availability of material. The establishment of an in vitro model that recapitulates features of the disease may help understanding the cellular and molecular events that trigger disease manifestations. Here, we describe the generation and characterization of a series of induced pluripotent stem (iPS) cells derived from patients with HD, including two rare homozygous genotypes and one heterozygous genotype. We used lentiviral technology to transfer key genes for inducing reprogramming. To confirm pluripotency and differentiation of iPS cells, we used PCR amplification and immunocytochemistry to measure the expression of marker genes in embryoid bodies and neurons. We also analyzed teratomas that formed in iPS cell-injected mice. We found that the length of the pathological CAG repeat did not increase during reprogramming, after long term growth in vitro, and after differentiation into neurons. In addition, we observed no differences between normal and mutant genotypes in reprogramming, growth rate, caspase activation or neuronal differentiation. However, we observed a significant increase in lysosomal activity in HD-iPS cells compared to control iPS cells, both during self-renewal and in iPS-derived neurons.In conclusion, we have established stable HD-iPS cell lines that can be used for investigating disease mechanisms that underlie HD. The CAG stability and lysosomal activity represent novel observations in HD-iPS cells. In the future, these cells may provide the basis for a powerful platform for drug screening and target identification in HD.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by an excessive expansion of a CAG trinucleotide repeat in the gene encoding the protein huntingtin, resulting in an elongated stretch of glutamines near the N-terminus of the protein. Here we report the derivation of a collection of 11 induced pluripotent stem (iPS) cell lines generated through somatic reprogramming of fibroblasts obtained from the R6/2 transgenic HD mouse line. We show that CAG expansion has no effect on reprogramming efficiency, cell proliferation rate, brain-derived neurotrophic factor level, or neurogenic potential. However, genes involved in the cholesterol biosynthesis pathway, which is altered in HD, are also affected in HD-iPS cell lines. Furthermore, we found a lysosomal gene upregulation and an increase in lysosome number in HD-iPS cell lines. These observations suggest that iPS cells from HD mice replicate some but not all of the molecular phenotypes typically observed in the disease; additionally, they do not manifest increased cell death propensity either under self-renewal or differentiated conditions. More studies will be necessary to transform a revolutionary technology into a powerful platform for drug screening approaches.