There is a need to persuade public and private entities to share their currently unexposed bio-data banks by preserving ownership and secrecy. The reason is to make available results that can be obtained by massively exploiting the content of such data by modern machine learning approaches. Digital catalogues of data collections are being provided. However, they are not developed to protect private content that may be shared according to privileges assigned by the owners. Here, we present BIOCHAIN, a data-sharing module which will be the basis for a computational platform aimed at performing federated data analysis. The platform is intended to be used by a consortium of private and public institutions in the field of microbiology. BIOCHAIN makes use of blockchain technology to guarantee fairness among entities of the consortium by allowing them to securely share their data.

Abstract Automated segmentation of computed tomography (CT) scans is the first step in the pipeline for the interpretation and identification of potential pathologies in human organs. Several methods based on machine learning (ML) are currently available, even if their precision is still outperformed by medical doctors. In this field there are some intrinsic limitations to ML approaches, such as the following: cost and time to acquire high-quality annotated scans for training; and a remarkable high variability of organ morphology due to age, conditions, genetics and acquisition. This paper outlines a new methodology based on Answer Set Programming, which returns reliable, easy-to-program and explainable interpretations. In particular, we focus on the CT scan analysis and retrieval of tree-like structure, corresponding to main blood vessels (arteries) arrangement. The structure is compared to the knowledge base of vessels contained in anatomy textbooks. The mapping of vessel names is computed by an Answer Set Programming program. This preliminary step produces a robust input to a reasoner for the multi-organ labelling and localization problem.

This article presents a multi-GPU implementation of a Finite-Volume solver on a multi-resolution grid. The implementation completely offloads the computation to the GPUs and communications between different GPUs are implemented by means of the Message Passing Interface (MPI) API. Different domain decomposition techniques have been considered and the one based on the Hilbert Space Filling Curves (HSFC) showed optimal scalability. Several optimizations are introduced: One-to-one MPI communications among MPI ranks are completely masked by GPU computations on internal cells and a novel dynamic load balancing algorithm is introduced to minimize the waiting times at global MPI synchronization barriers. Such algorithm adapts the computational load of ranks in response to dynamical changes in the execution time of blocks and in network performances; Its capability to converge to a balanced computation has been empirically shown by numerical experiments. Tests exploit up to 64 GPUs and 83M cells and achieve an efficiency of 90 percent in weak scalability and 85 percent for strong scalability. The framework is general and the results of the article can be ported to a wide range of explicit 2D Partial Differential Equations solvers.

This special issue of Theory and Practice of Logic Programming (TPLP) contains the regular papers accepted for presentation at the 34-th International Conference on Logic Programming (ICLP 2018), held in Oxford, United Kingdom, from July 14th to July 17th, 2018.

The parallel computing power offered by graphic processing units (GPUs) has been recently exploited to support general purpose applications – by exploiting the availability of general API and the single-instruction multiple-thread-style parallelism present in several classes of problems (e.g. numerical simulations and matrix manipulations) – where relatively simple computations need to be applied to all items in large sets of data. This paper investigates the use of GPUs in parallelising a class of search problems, where the combinatorial nature leads to large parallel tasks and relatively less natural symmetries. Specifically, the investigation focuses on the well-known satisfiability testing (SAT) problem and on the use of the NVIDIA compute unified device architecture, one of the most popular platforms for GPU computing. The paper explores ways to identify strong sources of GPU-style parallelism from SAT solving. The paper describes experiments with different design choices and evaluates the results. The outcomes demonstrate the potential for this approach, leading to one order of magnitude of speedup using a simple NVIDIA platform.

Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this paper, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e., the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically plausible assessment of the structural connectivity of the brain.

The paper investigates a novel approach, based on Constraint Logic Programming (CLP), to predict potential 3D conformations of a protein via fragments assembly. The fragments are extracted and clustered by a preprocessor from a database of known protein structures. Assembling fragments into a complete conformation is modeled as a constraint satisfaction problem solved using CLP. The approach makes use of a simplified Cα-side chain centroid protein model, that offers efficiency and a good approximation for space filling. The approach adapts existing energy models for protein representation and applies a large neighboring search (LNS) strategy. The results show the feasibility and efficiency of the method, and the declarative nature of the approach simplifies the introduction of additional knowledge and variations of the model.

Abstract The paper investigates a novel approach, based on Constraint Logic Programming (CLP), to predict the 3D conformation of a protein via fragments assembly. The fragments are extracted by a preprocessor—also developed for this work—from a database of known protein structures that clusters and classifies the fragments according to similarity and frequency. The problem of assembling fragments into a complete conformation is mapped to a constraint solving problem and solved using CLP. The constraint-based model uses a medium discretization degree Cα-side chain centroid protein model that offers efficiency and a good approximation for space filling. The approach and adapts existing energy models to the protein representation used and applies a large neighboring search strategy. The results shows the feasibility and efficiency of the method. The declarative nature of the solution allows to include future extensions, e.g., different size fragments for better accuracy.

This paper summarizes a constraint solving technique that is used to reason effectively in the scope of a set-based constraint language that supersedes existing finite domain languages. The first part of this paper motivates the presented work and introduces the constraint language, namely the language of Hereditarily Finite Sets (HFS). Then, the proposed constraint solver is detailed in terms of a set of rewrite rules that exploit finite domain reasoning within the HFS language. The proposed solution improves previous work on CLP (SET) [11] by integrating intervals into the constraint system and by providing a new layered architecture for the solver that supports more effective constraint solving strategies. On the other hand, the proposed approach provides enhanced expressivity and flexibility of domain representation than those usually found in existing finite domain constraint solvers.

In recent years, Answer Set Programming has gained popularity as a viable paradigm for applications in knowledge representation and reasoning. This paper presents a novel methodology to compute answer sets of an answer set program. The proposed methodology maintains a bottom-up approach to the computation of answer sets (as in existing systems), but it makes use of a novel structuring of the computation, that originates from the non-ground version of the program. Grounding is lazily performed during the computation of the answer sets. The implementation has been realized using Constraint Logic Programming over finite domains.

Abstract This paper presents the design, implementation and application of a constraint programming framework on 3D crystal lattices. The framework provides the flexibility to express and resolve constraints dealing with structural relationships of entities placed in a 3D lattice structure in space. Both sequential and parallel implementations of the framework are described, along with experiments that highlight its superior performance with respect to the use of more traditional frameworks (e.g. constraints on finite domains and integer programming) to model lattice constraints. The framework is motivated and applied to address the problem of solving the protein folding prediction problem, i.e. predicting the 3D structure of a protein from its primary amino acid sequence. Results and comparison with performance of other constraint‐based solutions to this problem are presented. Copyright © 2007 John Wiley & Sons, Ltd.

The paper describes a constraint-based solution to the protein folding problem on face-centered cubic lattices---a biologically meaningful approximation of the general protein folding problem. The paper improves the results presented in [15] and introduces new ideas for improving efficiency: (i) proper reorganization of the constraint structure; (ii) development of novel, both general and problem-specific, heuristics; (iii) exploitation of parallelism. Globally, we obtain a speed up in the order of 60 w.r.t. [15]. We show how these results can be employed to solve the folding problem for large proteins containing subsequences whose conformation is already known.

BACKGROUND: The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. RESULTS: Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. CONCLUSIONS: The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

In this paper we propose a semantically well-founded combination of the constraint solvers used in the constraint programming languages CLP(SET) and CLP(FD). This work demonstrates that it is possible to provide efficient executions (through CLP(FD) solvers) while maintaining the expressive power and flexibility of the CLP(SET) language. We develop a combined constraint solver and we show how static analysis can help in organizing the distribution of constraints to the two constraint solvers.