An empirical analysis of smart contracts: platforms, applications, and design patterns

Smart contracts are computer programs that can be consistently executed by a network of mutually distrusting nodes, without the arbitration of a trusted authority. Because of their resilience to tampering, smart contracts are appealing in many scenarios, especially in those which require transfers of money to respect certain agreed rules (like in financial services and in games). Over the last few years many platforms for smart contracts have been proposed, and some of them have been actually implemented and used. We study how the notion of smart contract is interpreted in some of these platforms. Focussing on the two most widespread ones, Bitcoin and Ethereum, we quantify the usage of smart contracts in relation to their application domain. We also analyse the most common programming patterns in Ethereum, where the source code of smart contracts is available.Comment: WTSC 201

Renegotiation and recursion in Bitcoin contracts

BitML is a process calculus to express smart contracts that can be run on Bitcoin. One of its current limitations is that, once a contract has been stipulated, the participants cannot renegotiate its terms: this prevents expressing common financial contracts, where funds have to be added by participants at run-time. In this paper, we extend BitML with a new primitive for contract renegotiation. At the same time, the new primitive can be used to write recursive contracts, which was not possible in the original BitML. We show that, despite the increased expressiveness, it is still possible to execute BitML on standard Bitcoin, preserving the security guarantees of BitML.Comment: Full version of the paper presented at COORDINATION 202

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis

Prognostic Integrated Image-Based Immune and Molecular Profiling in Early-Stage Endometrial Cancer

Optimum risk stratification in early-stage endometrial cancer (EC) combines clinicopathological factors and the molecular EC classification defined by The Cancer Genome Atlas (TCGA). It is unclear whether analysis of intratumoral immune infiltrate improves this. We developed a machine-learning image-based algorithm to quantify density of CD8+ and CD103+ immune cells in tumor epithelium and stroma in 695 stage I endometrioid ECs from the PORTEC-1&-2 trials. The relationship between immune cell density and clinicopathological/molecular factors was analyzed by hierarchical clustering and multiple regression. The prognostic value of immune infiltrate by cell type and location was analyzed by univariable and multivariable Cox regression, incorporating the molecular EC classification. Tumor-infiltrating immune cell density varied substantially between cases, and more modestly by immune cell type and location. Clustering revealed three groups with high, intermediate and low densities, with highly significant variation in the proportion of molecular EC subgroups between them. Univariable analysis revealed intraepithelial CD8+ cell density as the strongest predictor of EC recurrence; multivariable analysis confirmed this was independent of pathological factors and molecular subgroup. Exploratory analysis suggested this association was not uniform across molecular subgroups, but greatest in tumors with mutant p53 and absent in DNA mismatch repair deficient cancers. Thus, this work identified that quantification of intraepithelial CD8+ cells improved upon the prognostic utility of the molecular EC classification in early-stage EC

Regression spline bivariate probit models: A practical approach to testing for exogeneity

Bivariate probit models can deal with a problem usually known as endogeneity. This issue is likely to arise in observational studies when confounders are unobserved. We are concerned with testing the hypothesis of exogeneity (or absence of endogeneity) when using regression spline recursive and sample selection bivariate probit models. Likelihood ratio and gradient tests are discussed in this context and their empirical properties investigated and compared with those of the Lagrange multiplier and Wald tests through a Monte Carlo study. The tests are illustrated using two datasets in which the hypothesis of exogeneity needs to be tested

Non-Integrative Lentivirus Drives High-Frequency cre-Mediated Cassette Exchange in Human Cells

Recombinase mediated cassette exchange (RMCE) is a two-step process leading to genetic modification in a specific genomic target sequence. The process involves insertion of a docking genetic cassette in the genome followed by DNA transfer of a second cassette flanked by compatible recombination signals and expression of the recombinase. Major technical drawbacks are cell viability upon transfection, toxicity of the enzyme, and the ability to target efficiently cell types of different origins. To overcome such drawbacks, we developed an RMCE assay that uses an integrase-deficient lentivirus (IDLV) vector in the second step combined with promoterless trapping of double selectable markers. Additionally, recombinase expression is self-limiting as a result of the exchangeable reaction, thus avoiding toxicity. Our approach provides proof-of-principle of a simple and novel strategy with expected wide applicability modelled on a human cell line with randomly integrated copies of a genetic landing pad. This strategy does not present foreseeable limitations for application to other cell systems modified by homologous recombination. Safety, efficiency, and simplicity are the major advantages of our system, which can be applied in low-to-medium throughput strategies for screening of cDNAs, non-coding RNAs during functional genomic studies, and drug screening

Genome-wide association study of febrile seizures implicates fever response and neuronal excitability genes

Febrile seizures represent the most common type of pathological brain activity in young children and are influenced by genetic, environmental and developmental factors. In a minority of cases, febrile seizures precede later development of epilepsy. We conducted a genome-wide association study of febrile seizures in 7635 cases and 83 966 controls identifying and replicating seven new loci, all with P < 5 x 10(-10). Variants at two loci were functionally related to altered expression of the fever response genes PTGER3 and IL10, and four other loci harboured genes (BSN, ERC2, GABRG2, HERC1) influencing neuronal excitability by regulating neurotransmitter release and binding, vesicular transport or membrane trafficking at the synapse. Four previously reported loci (SCN1A, SCN2A, ANO3 and 12q21.33) were all confirmed. Collectively, the seven novel and four previously reported loci explained 2.8% of the variance in liability to febrile seizures, and the single nucleotide polymorphism heritability based on all common autosomal single nucleotide polymorphisms was 10.8%. GABRG2, SCN1A and SCN2A are well-established epilepsy genes and, overall, we found positive genetic correlations with epilepsies (r(g) = 0.39, P = 1.68 x 10(-4)). Further, we found that higher polygenic risk scores for febrile seizures were associated with epilepsy and with history of hospital admission for febrile seizures. Finally, we found that polygenic risk of febrile seizures was lower in febrile seizure patients with neuropsychiatric disease compared to febrile seizure patients in a general population sample. In conclusion, this largest genetic investigation of febrile seizures to date implicates central fever response genes as well as genes affecting neuronal excitability, including several known epilepsy genes. Further functional and genetic studies based on these findings will provide important insights into the complex pathophysiological processes of seizures with and without fever.Peer reviewe