One-pass Context-based Tableaux Systems for CTL and ECTL

When building tableau for temporal logic formulae, applying a two-pass construction, we first check the validity of the given tableaux input by creating a tableau graph, and then, in the second `pass', we check if all the eventualities are satisfied. In one-pass tableaux checking the validity of the input does not require these auxiliary constructions. This paper continues the development of one-pass tableau method for temporal logics introducing tree-style one-pass tableau systems for Computation Tree Logic (CTL) and shows how this can be extended to capture Extended CTL (ECTL). A distinctive feature here is the utilisation, for the core tableau construction, of the concept of a context of an eventuality which forces its earliest fulfilment. Relevant algorithms for obtaining a systematic tableau for these branching-time logics are also defined. We prove the soundness and completeness of the method. With these developments of a tree-shaped one-pass tableau for CTL and ECTL, we have formalisms which are well suited for the automation and are amenable for the implementation, and for the formulation of dual sequent calculi. This brings us one step closer to the application of one pass context based tableaux in certified model checking for a variety of CTL-type branching-time logics

Towards Certified Model Checking for PLTL using One-pass Tableaux

The standard model checking setup analyses whether the given system specification satisfies a dedicated temporal property of the system, providing a positive answer here or a counter-example. At the same time, it is often useful to have an explicit proof that certifies the satisfiability. This is exactly what the {\it certified model checking (CMC)} has been introduced for. The paper argues that one-pass (context-based) tableau for PLTL can be efficiently used in the CMC setting, emphasising the following two advantages of this technique. First, the use of the context in which the eventualities occur, forces them to fulfil as soon as possible. Second, a dual to the tableau sequent calculus can be used to formalise the certificates. The combination of the one-pass tableau and the dual sequent calculus enables us to provide not only counter-examples for unsatisfied properties, but also proofs for satisfied properties that can be checked in a proof assistant. In addition, the construction of the tableau is enriched by an embedded solver, to which we dedicate those (propositional) computational tasks that are costly for the tableaux rules applied solely. The combination of the above techniques is particularly helpful to reason about large (system) specifications

Magnetic behaviour of multisegmented FeCoCu/Cu electrodeposited nanowires

Understanding the magnetic behaviour of multisegmented nanowires (NWs) is a major key for the application of such structures in future devices. In this work, magnetic/non-magnetic arrays of FeCoCu/Cu multilayered NWs electrodeposited in nanoporous alumina templates are studied. Contrarily to most reports on multilayered NWs, the magnetic layer thickness was kept constant (30 nm) and only the non-magnetic layer thickness was changed (0 to 80 nm). This allowed us to tune the interwire and intrawire interactions between the magnetic layers in the NW array creating a three-dimensional (3D) magnetic system without the need to change the template characteristics. Magnetic hysteresis loops, measured with the applied field parallel and perpendicular to the NWs' long axis, showed the effect of the non-magnetic Cu layer on the overall magnetic properties of the NW arrays. In particular, introducing Cu layers along the magnetic NW axis creates domain wall nucleation sites that facilitate the magnetization reversal of the wires, as seen by the decrease in the parallel coercivity and the reduction of the perpendicular saturation field. By further increasing the Cu layer thickness, the interactions between the magnetic segments, both along the NW axis and of neighbouring NWs, decrease, thus rising again the parallel coercivity and the perpendicular saturation field. This work shows how one can easily tune the parallel and perpendicular magnetic properties of a 3D magnetic layer system by adjusting the non-magnetic layer thickness

A CD36 ectodomain mediates insect pheromone detection via a putative tunnelling mechanism.

CD36 transmembrane proteins have diverse roles in lipid uptake, cell adhesion and pathogen sensing. Despite numerous in vitro studies, how they act in native cellular contexts is poorly understood. A Drosophila CD36 homologue, sensory neuron membrane protein 1 (SNMP1), was previously shown to facilitate detection of lipid-derived pheromones by their cognate receptors in olfactory cilia. Here we investigate how SNMP1 functions in vivo. Structure-activity dissection demonstrates that SNMP1's ectodomain is essential, but intracellular and transmembrane domains dispensable, for cilia localization and pheromone-evoked responses. SNMP1 can be substituted by mammalian CD36, whose ectodomain can interact with insect pheromones. Homology modelling, using the mammalian LIMP-2 structure as template, reveals a putative tunnel in the SNMP1 ectodomain that is sufficiently large to accommodate pheromone molecules. Amino-acid substitutions predicted to block this tunnel diminish pheromone sensitivity. We propose a model in which SNMP1 funnels hydrophobic pheromones from the extracellular fluid to integral membrane receptors

Tailoring the magnetization states in 2D arrays of multiresponse ferromagnetic nanomagnets

We have fabricated Fe52-54Co46-48 nanomagnet arrays as a function of several geometrical parameters like the spacing between nanostructures, the aspect ratio and the layers thicknesses. The nanomagnets consist in two magnetic layers, separated by a non magnetic interlayer, that interact through magnetostatic coupling. They present a multiresponse hysteresis loops with two different switching fields. We have performed micromagnetic simulations to discern the role play by the different interactions. The spacing in the array strongly modifies the saturating field along the short axis and the magnetization reversal mechanisms from coherent rotation to domain wall nucleation. A small asymmetry between the two magnetic layers favors a magnetization reversal mechanism along the long axis with two different switching fields. These fields can be tailored through the thickness of the layers or the inter-element spacing in the array. In trilayers with the same magnetic layer thicknesses, the asymmetry can be induced by growing the two magnetic layers with a different anisotropy. The well-defined reversal fields make these nanomagnets potentially useful for magnetic tagging

CTCs expression profiling for advanced breast cancer monitoring

The study of circulating tumor cells (CTCs) has a huge clinical interest in advance and metastatic breast cancer patients. However, many approaches are biased by the use of epithelial markers, which underestimate non-epithelial CTCs phenotypes. CTCs enumeration provides valuable prognostic information; however, molecular characterization could be the best option to monitor patients throughout the disease since it may provide more relevant clinical information to the physicians. In this work, we aimed at enumerating and performing a molecular characterization of CTCs from a cohort of 20 patients with metastatic breast cancer (MBC), monitoring the disease at different time points of the therapy, and at progression when it occurred. To this end, we used a CTC negative enrichment protocol that allowed us to recover a higher variety of CTCs phenotypes. With this strategy, we were able to obtain gene expression data from CTCs from all the patients. In addition, we found that high expression levels of PALB2 and MYC were associated with a worse outcome. Interestingly, we identified that CTCs with an EpCAM(high)VIM(low)ALDH1A1(high) signature showed both shorter overall survival (OS) and progression-free survival (PFS), suggesting that CTCs with epithelial-stem features had the most aggressive phenotype

Large Genomic Imbalances in Brugada Syndrome

Purpose Brugada syndrome (BrS) is a form of cardiac arrhythmia which may lead to sudden cardiac death. The recommended genetic testing (direct sequencing of SCN5A) uncovers disease-causing SNVs and/or indels in ~20% of cases. Limited information exists about the frequency of copy number variants (CNVs) in SCN5A in BrS patients, and the role of CNVs in BrS-minor genes is a completely unexplored field. Methods 220 BrS patients with negative genetic results were studied to detect CNVs in SCN5A. 63 cases were also screened for CNVs in BrS-minor genes. Studies were performed by Multiplex ligation-dependent probe amplification or Next-Generation Sequencing (NGS). Results The detection rate for CNVs in SCN5A was 0.45% (1/220). The detected imbalance consisted of a duplication from exon 15 to exon 28, and could potentially explain the BrS phenotype. No CNVs were found in BrS-minor genes. Conclusion CNVs in current BrS-related genes are uncommon among BrS patients. However, as these rearrangements may underlie a portion of cases and they undergo unnoticed by traditional sequencing, an appealing alternative to conventional studies in these patients could be targeted NGS, including in a single experiment the study of SNVs, indels and CNVs in all the known BrS-related genes

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness