Refining SCJ Mission Specifications into Parallel Handler Designs

Safety-Critical Java (SCJ) is a recent technology that restricts the execution and memory model of Java in such a way that applications can be statically analysed and certified for their real-time properties and safe use of memory. Our interest is in the development of comprehensive and sound techniques for the formal specification, refinement, design, and implementation of SCJ programs, using a correct-by-construction approach. As part of this work, we present here an account of laws and patterns that are of general use for the refinement of SCJ mission specifications into designs of parallel handlers used in the SCJ programming paradigm. Our notation is a combination of languages from the Circus family, supporting state-rich reactive models with the addition of class objects and real-time properties. Our work is a first step to elicit laws of programming for SCJ and fits into a refinement strategy that we have developed previously to derive SCJ programs.Comment: In Proceedings Refine 2013, arXiv:1305.563

SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling

For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towards what?". Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis we determine what works and what might be done to make further improvements for ORL.Comment: Published in NAACL 201

A Mention-Ranking Model for Abstract Anaphora Resolution

Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence--antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and -- if disregarding syntax -- discriminates candidates using deeper features.Comment: In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP). Copenhagen, Denmar

The Effect of Aerobic and Resistance Training on Glycemic Control in Type 2 Diabetes Mellitus: Meta-Analytic Study

Diabetes is a widely-spread disease among the whole population and can cause other complications in many organs if not treated correctly. Medications along with exercise and an appropriate diet are a key point in the management of type 2 diabetes. PURPOSE: to analyze the effects of aerobic, resistance, and combined training carefully on glycemic control in type 2 diabetes mellitus. METHODS: A meta-analysis review was conducted to investigate differences in glucose and HbA1c levels with aerobic and resistance training in type 2 diabetes patients. Data over training, HbA1c and daily glucose levels was collected from different studies for the investigation. The data collected was carefully analyzed to see whether aerobic or resistance training was the best option of training to have a better outcome on the control of glucose levels. All studies included adults from the ages 18-65 and of both genders. 905 inactive subjects were used for the study. The subjects met three times per week and exercised for an average of 49 minutes of moderate to high intensity training. RESULTS: All three types of training decreased the HbA1c levels but had a higher decrease in the combined training group. The Effect Size (ES) for all three groups were: aerobic (-0.47), resistance (-0.38), and combined (-0.77) which was a large ES. BMI (kg/m2), MVO2 (ml/min/kg), and FFA (%) had a Small-Moderate ES with combined training, while MVO2 showed a Large ES in the aerobic group. CONCLUSION: Even though the results showed positive effects in all three groups, the present study suggest type 2 diabetes patients to engage in combined training to see better results in their HbA1c levels

Developing bacterial microcompartments for the recombinant production of proteins

In prokaryotes, supramolecular self-assembling protein structures, known as bacterial microcompartments (BMCs), have evolved to encapsulate proteins associated with a number of different metabolic processes, providing a physical diffusion barrier whilst increasing local enzyme concentrations. The modular nature of these structures makes them promising biological platforms for the engineering of synthetic compartmentation within the bacterial cell that have potential to be used as novel nano-bioreactors [1-3]. We are interested in the development of robust synthetic BMC technologies that can be utilised for industrially relevant applications, specifically the spatial segregation of synthetic enzyme cascades for the enhanced production of valuable chemical compounds. BMCs may also be valuable in the production of recombinant proteins. Many therapeutic proteins and antibody fragments require disulphide-bonds for correct folding and activity. Here, we are exploring the potential of BMCs to serve as synthetic cellular organelles within the bacterial cytoplasm of E. coli that promote correct protein folding and disulphide bond formation of recombinant proteins, providing an alternative method to traditional approaches (folding in the eukaryotic endoplasmic reticulum and in the periplasm of prokaryotes). Previously, it has been shown that proteins of interest can be compartmentalised by fusing them to targeting peptides, which direct the proteins to the microcompartment shell [4, 5]. In this work, both, the shell (PduABJKNU) and the targeting peptides (P18/ D18) are derived from the 1,2- propanediol utilisation (Pdu) BMC from Citrobacter freundii [4]. We first determined the effect of fusing short targeting peptides onto the E. coli alkaline phosphatase PhoA, a protein widely used to examine disulfide bond formation in vivo, and the sulfhydryl oxidase Erv1p, a catalytic enzyme for the formation of disulphide bonds. The most active fusion proteins were selected for co-production with the BMC shell. For efficient recruitment of these proteins to the BMC, gene expression levels were controlled using tunable promoters and recombinantly produced BMC variants were analysed in vivo and in vitro using biochemical and biophysical methods. We demonstrated that both, PhoA and Erv1p, are targeted to recombinant BMCs and determined disulphide bond formation of PhoA in the presence and absence of Erv1p when targeted to the microcompartments. Using this approach, a range of other proteins of industrial interest will be tested and the potential for the production and purification of bio-therapeutic proteins and antibody fragments will be determined. References: [1] Lawrence, A. D., Frank, S., Newnham, S., Lee, M. J., Brown, I. R., Xue, W. F., Rowe, M.L. & Warren, M. J. (2014). Solution structure of a bacterial microcompartment targeting peptide and its application in the construction of an ethanol bioreactor. ACS synthetic biology, 3(7), 454-465. [2] Wagner, H. J., Capitain, C. C., Richter, K., Nessling, M., & Mampel, J. (2017). Engineering bacterial microcompartments with heterologous enzyme cargos. Engineering in Life Sciences, 17(1), 36-46. [3] Plegaria, J. S., & Kerfeld, C. A. (2018). Engineering nanoreactors using bacterial microcompartment architectures. Current opinion in biotechnology, 51, 1-7. [4] Parsons, J. B., Frank, S., Bhella, D., Liang, M., Prentice, M. B., Mulvihill, D. P., & Warren, M. J. (2010). Synthesis of empty bacterial microcompartments, directed organelle protein incorporation, and evidence of filament-associated organelle movement. Molecular cell, 38(2), 305-315. [5] Aussignargues, C., Paasch, B. C., Gonzalez-Esquer, R., Erbilgin, O., & Kerfeld, C. A. (2015). Bacterial microcompartment assembly: the key role of encapsulation peptides. Communicative & integrative biology, 8(3

Supporting ArcAngel in ProofPower

AbstractArcAngel is a specialised tactic language devised to facilitate and automate program developments using Morgan's refinement calculus. It is especially well-suited for the specification of high-level strategies to derive programs by construction, and equipped with a formal semantics that enables reasoning about tactics. In this paper, we present an implementation of ArcAngel for the ProofPower theorem prover. We discuss the underlying design, explain how it implements the semantics of ArcAngel, and examine differences in expressiveness and flexibility in comparison to ProofPower's in-built tactic language. ArcAngel supports backtracking through angelic choice; this is beyond the basic capabilities of ProofPower and many other main-stream theorem provers. The implementation is demonstrated with a non-trivial tactic example