SEPIA: Search for Proofs Using Inferred Automata

This paper describes SEPIA, a tool for automated proof generation in Coq. SEPIA combines model inference with interactive theorem proving. Existing proof corpora are modelled using state-based models inferred from tactic sequences. These can then be traversed automatically to identify proofs. The SEPIA system is described and its performance evaluated on three Coq datasets. Our results show that SEPIA provides a useful complement to existing automated tactics in Coq.Comment: To appear at 25th International Conference on Automated Deductio

Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals

Exponential increase in postprandial blood-glucose exposure with increasing carbohydrate loads using a linear carbohydrate-to-insulin ratio

Background. Postprandial glucose excursions contribute significantly to average blood glucose, glycaemic variability and cardiovascular risk. Carbohydrate counting is a method of insulin dosing that balances carbohydrate load to insulin dose using a fixed ratio. Many patients and current insulin pumps calculate insulin delivery for meals based on a linear carbohydrate-to-insulin relationship. It is our hypothesis that a non-linear relationship exists between the amounts of carbohydrate consumed and the insulin required to cover it.Aim. To document blood glucose exposure in response to increasing carbohydrate loads on fixed carbohydrate-to-insulin ratios.Methods. Five type 1 diabetic subjects receiving insulin pump therapy with good control were recruited. Morning basal rates and carbohydrateto-insulin ratios were optimised. A Medtronic glucose sensor was used for 5 days to collect data for area-under-the-curve (AUC) analysis, during which standardised meals of increasing carbohydrate loads were consumed.Results. Increasing carbohydrate loads using a fixed carbohydrate-to-insulin ratio resulted in increasing glucose AUC. The relationship was found to be exponential rather than linear. Late postprandial hypoglycaemia followed carbohydrate loads of >60 g and this was often followed by rebound hyperglycaemia that lasted >6 hours.Conclusion. A non-linear relationship exists between carbohydrates consumed and the insulin required to cover them. This has implications for control of postprandial blood sugars, especially when consuming large carbohydrate loads. Further studies are required to look at the optimal ratios, duration and type of insulin boluses required to cover increasing carbohydrate loads

What went wrong? The flawed concept of cerebrospinal venous insufficiency

In 2006, Zamboni reintroduced the concept that chronic impaired venous outflow of the central nervous system is associated with multiple sclerosis (MS), coining the term of chronic cerebrospinal venous insufficiency ('CCSVI'). The diagnosis of 'CCSVI' is based on sonographic criteria, which he found exclusively fulfilled in MS. The concept proposes that chronic venous outflow failure is associated with venous reflux and congestion and leads to iron deposition, thereby inducing neuroinflammation and degeneration. The revival of this concept has generated major interest in media and patient groups, mainly driven by the hope that endovascular treatment of 'CCSVI' could alleviate MS. Many investigators tried to replicate Zamboni's results with duplex sonography, magnetic resonance imaging, and catheter angiography. The data obtained here do generally not support the 'CCSVI' concept. Moreover, there are no methodologically adequate studies to prove or disprove beneficial effects of endovascular treatment in MS. This review not only gives a comprehensive overview of the methodological flaws and pathophysiologic implausibility of the 'CCSVI' concept, but also summarizes the multimodality diagnostic validation studies and open-label trials of endovascular treatment. In our view, there is currently no basis to diagnose or treat 'CCSVI' in the care of MS patients, outside of the setting of scientific research

The Complement Anaphylatoxin C5a Induces Apoptosis in Adrenomedullary Cells during Experimental Sepsis

Sepsis remains a poorly understood, enigmatic disease. One of the cascades crucially involved in its pathogenesis is the complement system. Especially the anaphylatoxin C5a has been shown to have numerous harmful effects during sepsis. We have investigated the impact of high levels of C5a on the adrenal medulla following cecal ligation and puncture (CLP)-induced sepsis in rats as well as the role of C5a on catecholamine production from pheochromocytoma-derived PC12 cells. There was significant apoptosis of adrenal medulla cells in rats 24 hrs after CLP, as assessed by the TUNEL technique. These effects could be reversed by dual-blockade of the C5a receptors, C5aR and C5L2. When rats were subjected to CLP, levels of C5a and norepinephrine were found to be antipodal as a function of time. PC12 cell production of norepinephrine and dopamine was significantly blunted following exposure to recombinant rat C5a in a time-dependent and dose-dependent manner. This impaired production could be related to C5a-induced initiation of apoptosis as defined by binding of Annexin V and Propidium Iodine to PC12 cells. Collectively, we describe a C5a-dependent induction of apoptotic events in cells of adrenal medulla in vivo and pheochromocytoma PC12 cells in vitro. These data suggest that experimental sepsis induces apoptosis of adrenomedullary cells, which are responsible for the bulk of endogenous catecholamines. Septic shock may be linked to these events. Since blockade of both C5a receptors virtually abolished adrenomedullary apoptosis in vivo, C5aR and C5L2 become promising targets with implications on future complement-blocking strategies in the clinical setting of sepsis

Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

Stroma Regulates Increased Epithelial Lateral Cell Adhesion in 3D Culture: A Role for Actin/Cadherin Dynamics

Cell shape and tissue architecture are controlled by changes to junctional proteins and the cytoskeleton. How tissues control the dynamics of adhesion and cytoskeletal tension is unclear. We have studied epithelial tissue architecture using 3D culture models and found that adult primary prostate epithelial cells grow into hollow acinus-like spheroids. Importantly, when co-cultured with stroma the epithelia show increased lateral cell adhesions. To investigate this mechanism further we aimed to: identify a cell line model to allow repeatable and robust experiments; determine whether or not epithelial adhesion molecules were affected by stromal culture; and determine which stromal signalling molecules may influence cell adhesion in 3D epithelial cell cultures.The prostate cell line, BPH-1, showed increased lateral cell adhesion in response to stroma, when grown as 3D spheroids. Electron microscopy showed that 9.4% of lateral membranes were within 20 nm of each other and that this increased to 54% in the presence of stroma, after 7 days in culture. Stromal signalling did not influence E-cadherin or desmosome RNA or protein expression, but increased E-cadherin/actin co-localisation on the basolateral membranes, and decreased paracellular permeability. Microarray analysis identified several growth factors and pathways that were differentially expressed in stroma in response to 3D epithelial culture. The upregulated growth factors TGFβ2, CXCL12 and FGF10 were selected for further analysis because of previous associations with morphology. Small molecule inhibition of TGFβ2 signalling but not of CXCL12 and FGF10 signalling led to a decrease in actin and E-cadherin co-localisation and increased paracellular permeability.In 3D culture models, paracrine stromal signals increase epithelial cell adhesion via adhesion/cytoskeleton interactions and TGFβ2-dependent mechanisms may play a key role. These findings indicate a role for stroma in maintaining adult epithelial tissue morphology and integrity

Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer

The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5–17.6), irregular mass shape (OR 10.0, CI 3.4–29.5), spiculated mass margin (OR 20.4, CI 1.9–222.8), and subject age (β = 0.09, p < 0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings