Quantum Annealing in the NISQ Era: Railway Conflict Management.

We are in the noisy intermediate-scale quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrations of the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the train dispatching consequences of the arrival of an already delayed train to a given network segment. This problem is computationally hard and needs to be solved almost in real time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, which is compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including the conventional solution of a linear integer version of the model as well as the solution of the QUBO model using a tensor network-based algorithm. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) does not perform well on those instances, either.

Transcriptomic and Morphological Analysis of Cells Derived from Porcine Buccal Mucosa-Studies on an In Vitro Model.

Transcriptional analysis and live-cell imaging are a powerful tool to investigate the dynamics of complex biological systems. In vitro expanded porcine oral mucosal cells, consisting of populations of epithelial and connective lineages, are interesting and complex systems for study via microarray transcriptomic assays to analyze gene expression profile. The transcriptomic analysis included 56 ontological groups with particular focus on 7 gene ontology groups that are related to the processes of differentiation and development. Most analyzed genes were upregulated after 7 days and downregulated after 15 and 30 days of in vitro culture. The performed transcriptomic analysis was then extended to include automated analysis of differential interference contrast microscopy (DIC) images obtained during in vitro culture. The analysis of DIC imaging allowed to identify the different populations of keratinocytes and fibroblasts during seven days of in vitro culture, and it was possible to evaluate the proportion of these two populations of cells. Porcine mucosa may be a suitable model for reference research on human tissues. In addition, it can provide a reference point for research on the use of cells, scaffolds, or tissues derived from transgenic animals for applications in human tissues reconstruction.

Detecting life signatures with RNA sequence similarity measures.

The RNA World is currently the most plausible hypothesis for explaining the origins of life on Earth. The supporting body of evidence is growing and it comes from multiple areas, including astrobiology, chemistry, biology, mathematics, and, in particular, from computer simulations. Such methods frequently assume the existence of a hypothetical species on Earth, around three billion years ago, with a base sequence probably dissimilar from any in known genomes. However, it is often hard to verify whether or not a hypothetical sequence has the characteristics of biological sequences, and is thus likely to be functional. The primary objective of the presented research was to verify the possibility of building a computational 'life probe' for determining whether a given genetic sequence is biological, and assessing the sensitivity of such probes to the signatures of life present in known biological sequences. We have proposed decision algorithms based on the normalized compression distance (NCD) and Levenshtein distance (LD). We have validated the proposed method in the context of the RNA World hypothesis using short genetic sequences shorter than the error threshold value (i.e., 100 nucleotides). We have demonstrated that both measures can be successfully used to construct life probes that are significantly better than a random decision procedure, while varying from each other when it comes to detailed characteristics. We also observed that fragments of sequences related to replication have better discriminatory power than sequences having other molecular functions. In a broader context, this shows that the signatures of life in short RNA samples can be effectively detected using relatively simple means.

Counterexample-Driven Genetic Programming: Heuristic Program Synthesis from Formal Specifications.

Conventional genetic programming (GP) can guarantee only that synthesized programs pass tests given by the provided input-output examples. The alternative to such a test-based approach is synthesizing programs by formal specification, typically realized with exact, nonheuristic algorithms. In this article, we build on our earlier study on Counterexample-Based Genetic Programming (CDGP), an evolutionary heuristic that synthesizes programs from formal specifications. The candidate programs in CDGP undergo formal verification with a Satisfiability Modulo Theory (SMT) solver, which results in counterexamples that are subsequently turned into tests and used to calculate fitness. The original CDGP is extended here with a fitness threshold parameter that decides which programs should be verified, a more rigorous mechanism for turning counterexamples into tests, and other conceptual and technical improvements. We apply it to 24 benchmarks representing two domains: the linear integer arithmetic (LIA) and the string manipulation (SLIA) problems, showing that CDGP can reliably synthesize provably correct programs in both domains. We also confront it with two state-of-the art exact program synthesis methods and demonstrate that CDGP effectively trades longer synthesis time for smaller program size.

Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis.

Program semantics is a promising recent research thread in Genetic Programming (GP). Over a dozen semantic-aware search, selection, and initialization operators for GP have been proposed to date. Some of these operators are designed to exploit the geometric properties of semantic space, while others focus on making offspring effective, that is, semantically different from their parents. Only a small fraction of previous works aimed at addressing both of these features simultaneously. In this article, we propose a suite of competent operators that combine effectiveness with geometry for population initialization, mate selection, mutation, and crossover. We present a theoretical rationale behind these operators and compare them experimentally to operators known from literature on symbolic regression and Boolean function synthesis benchmarks. We analyze each operator in isolation as well as verify how they fare together in an evolutionary run, concluding that the competent operators are superior on a wide range of performance indicators, including best-of-run fitness, test-set fitness, and program size.

Optical coherence microscopy as a novel, non-invasive method for the 4D live imaging of early mammalian embryos.

Imaging of living cells based on traditional fluorescence and confocal laser scanning microscopy has delivered an enormous amount of information critical for understanding biological processes in single cells. However, the requirement for a high numerical aperture and fluorescent markers still limits researchers' ability to visualize the cellular architecture without causing short- and long-term photodamage. Optical coherence microscopy (OCM) is a promising alternative that circumvents the technical limitations of fluorescence imaging techniques and provides unique access to fundamental aspects of early embryonic development, without the requirement for sample pre-processing or labeling. In the present paper, we utilized the internal motion of cytoplasm, as well as custom scanning and signal processing protocols, to effectively reduce the speckle noise typical for standard OCM and enable high-resolution intracellular time-lapse imaging. To test our imaging system we used mouse and pig oocytes and embryos and visualized them through fertilization and the first embryonic division, as well as at selected stages of oogenesis and preimplantation development. Because all morphological and morphokinetic properties recorded by OCM are believed to be biomarkers of oocyte/embryo quality, OCM may represent a new chapter in imaging-based preimplantation embryo diagnostics.

Online Discovery of Search Objectives for Test-Based Problems.

In test-based problems, commonly approached with competitive coevolutionary algorithms, the fitness of a candidate solution is determined by the outcomes of its interactions with multiple tests. Usually, fitness is a scalar aggregate of interaction outcomes, and as such imposes a complete order on the candidate solutions. However, passing different tests may require unrelated "skills," and candidate solutions may vary with respect to such capabilities. In this study, we provide theoretical evidence that scalar fitness, inherently incapable of capturing such differences, is likely to lead to premature convergence. To mitigate this problem, we propose disco, a method that automatically identifies the groups of tests for which the candidate solutions behave similarly and define the above skills. Each such group gives rise to a derived objective, and these objectives together guide the search algorithm in multi-objective fashion. When applied to several well-known test-based problems, the proposed approach significantly outperforms the conventional two-population coevolution. This opens the door to efficient and generic countermeasures to premature convergence for both coevolutionary and evolutionary algorithms applied to problems featuring aggregating fitness functions.

Cerebellar Volume in Children With Attention-Deficit Hyperactivity Disorder (ADHD).

Attention Deficit Hyperactivity Disorder (ADHD) is associated with altered cerebellar volume and cerebellum is associated with cognitive performance. However there are mixed results regarding the cerebellar volume in young patients with ADHD. To clarify the size and direction of this effect, we conducted the analysis on the large public database of brain images. The aim of this study was to confirm that cerebellar volume in ADHD is smaller than in control subjects in currently the largest publicly available cohort of ADHD subjects.We applied cross-sectional case control study design by comparing 286 ADHD patients (61 female) with age and gender matched control subjects. Volumetric measurements of cerebellum were obtained using automated segmentation with FreeSurfer 5.1. Statistical analysis was performed in R-CRAN statistical environment. Patients with ADHD had significantly smaller total cerebellar volumes (134.5±17.11cm3 vs.138.90±15.32 cm3). The effect was present in both females and males (males 136.9±14.37 cm3 vs. 141.20±14.75 cm3; females 125.7±12.34 cm3 vs. 131.20±15.03 cm3). Age was positively and significantly associated with the cerebellar volumes. These results indicate either delayed or disrupted cerebellar development possibly contributing to ADHD pathophysiology.

Segmenting Retinal Blood Vessels With Deep Neural Networks.

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400[Formula: see text]000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97 ). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87 ), and fares well on pathological cases.

OCT angiography by absolute intensity difference applied to normal and diseased human retinas.

We compare four optical coherence tomography techniques for noninvasive visualization of microcapillary network in the human retina and murine cortex. We perform phantom studies to investigate contrast-to-noise ratio for angiographic images obtained with each of the algorithm. We show that the computationally simplest absolute intensity difference angiographic OCT algorithm that bases only on two cross-sectional intensity images may be successfully used in clinical study of healthy eyes and eyes with diabetic maculopathy and branch retinal vein occlusion.

Development of novel molecular probes of the Rio1 atypical protein kinase.

The RIO kinases are essential protein factors required for the synthesis of new ribosomes in eukaryotes. Conserved in archaeal organisms as well, RIO kinases are among the most ancient of protein kinases. Their exact molecular mechanisms are under investigation and progress of this research would be significantly improved with the availability of suitable molecular probes that selectively block RIO kinases. RIO kinases contain a canonical eukaryotic protein kinase fold, but also display several unusual structural features that potentially create opportunity for the design of selective inhibitors. In an attempt to identify structural leads to target the RIO kinases, a series of pyridine caffeic acid benzyl amides (CABA) were tested for their ability to inhibit the autophosphorylation activity of Archeaoglobus fulgidus Rio1 (AfRio1). Screening of a small library of CABA molecules resulted in the identification of four compounds that measurably inhibited AfRio1 activity. Additional biochemical characterization of binding and inhibition activity of these compounds demonstrated an ATP competitive inhibition mode, and allowed identification of the functional groups that result in the highest binding affinity. In addition, docking of the compound to the structure of Rio1 and determination of the X-ray crystal structure of a model compound (WP1086) containing the desired functional groups allowed detailed analysis of the interactions between these compounds and the enzyme. Furthermore, the X-ray crystal structure demonstrated that these compounds stabilize an inactive form of the enzyme. Taken together, these results provide an important step in identification of a scaffold for the design of selective molecular probes to study molecular mechanisms of Rio1 kinases in vitro and in vivo. In addition, it provides a rationale for the future design of potent inhibitors with drug-like properties targeting an inactive form of the enzyme. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).

Formal analysis, hardness, and algorithms for extracting internal structure of test-based problems.

Problems in which some elementary entities interact with each other are common in computational intelligence. This scenario, typical for coevolving artificial life agents, learning strategies for games, and machine learning from examples, can be formalized as a test-based problem and conveniently embedded in the common conceptual framework of coevolution. In test-based problems, candidate solutions are evaluated on a number of test cases (agents, opponents, examples). It has been recently shown that every test of such problem can be regarded as a separate objective, and the whole problem as multi-objective optimization. Research on reducing the number of such objectives while preserving the relations between candidate solutions and tests led to the notions of underlying objectives and internal problem structure, which can be formalized as a coordinate system that spatially arranges candidate solutions and tests. The coordinate system that spans the minimal number of axes determines the so-called dimension of a problem and, being an inherent property of every problem, is of particular interest. In this study, we investigate in-depth the formalism of a coordinate system and its properties, relate them to properties of partially ordered sets, and design an exact algorithm for finding a minimal coordinate system. We also prove that this problem is NP-hard and come up with a heuristic which is superior to the best algorithm proposed so far. Finally, we apply the algorithms to three abstract problems and demonstrate that the dimension of the problem is typically much lower than the number of tests, and for some problems converges to the intrinsic parameter of the problem--its a priori dimension.

Multitask visual learning using genetic programming.

We propose a multitask learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining GP trees (subfunctions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort.

Visual learning by coevolutionary feature synthesis.

In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.

Ability of adenosine-2'(3')-deoxy-3'(2')-triphosphates and related analogues to replace ATP as phosphate donor for all human and Drosphila melanogaster deoxyribonucleoside kinases.

Six non-conventional adenosine-2'- and 3'-triphosphate analogues of ATP were tested as potential phosphate donors for all four human, and D. melanogaster, deoxyribonucleoside kinases. With dCK (only dAdo as acceptor), TK1, TK2 and dNK only 3'-deoxyadenosine-2'-triphosphate was an effective donor (5-60% that for ATP). With dCK (dCyd as acceptor) and dGK (dGuo as acceptor), sharing 45% sequence identity, donor activities ranged from 13 to 119% that for ATP. Products were 5'-phosphates. In some instances, kinetics are dependent on the nature of the acceptor, and donor and acceptors properties are mutually interdependent. Results are highly relevant to studies on the modes of interaction with the enzymes, and to interpretations of reported crystal structures of dCK and dNK with bound ligands.

Striking ability of adenosine-2'(3')-deoxy-3'(2')-triphosphates and related analogues to replace ATP as phosphate donor for all four human, and the Drosophila melanogaster, deoxyribonucleoside kinases.

In extension of an earlier report, six non-conventional analogues of ATP, three adenosine-2'-triphosphates (3'-deoxy, 3'-deoxy-3'-fluoro- and 3'-deoxy-3'-fluoroxylo-), and three adenosine-3'-triphosphates (2'-deoxy-, 2'-deoxy-2'-fluoro- and 2'-deoxy-2'-fluoroara-), were compared with ATP as potential phosphate donors for human deoxycytidine kinase (dCK), cytosolic thymidine kinase (TK1), mitochondrial TK2, deoxyguanosine kinase (dGK), and the deoxyribonucleoside kinase (dNK) from Drosophila melanogaster. With one group of enzymes, comprising TK1, TK2, dNK and dCK (with dAdo as acceptor), only 3'-deoxyadenosine-2'-triphosphate was an effective donor (5-60% that for ATP), and the other five analogues much less so, or inactive. With a second set, including dCK (dCyd, but not dAdo, as acceptor) and dGK (dGuo as acceptor), known to share high sequence similarity (approximately 45% sequence identity), all six analogues were good to excellent donors (13-119% that for ATP). With dCK and ATP1, products were shown to be 5'-phosphates. With dCK, donor properties of the analogues were dependent on the nature of the acceptor, as with natural 5'-triphosphate donors. With dCK (dCyd as acceptor), Km and Vmax for the two 2'(3')-deoxyadenosine-3'(2')-triphosphates are similar to those for ATP. With dGK, Km values are higher than for ATP, while Vmax values are comparable. Kinetic studies further demonstrated Michaelis-Menten (non-cooperative) or cooperative kinetics, dependent on the enzyme employed and the nature of the donor. The physiological significance, if any, of the foregoing remains to be elucidated. The overall results are, on the other hand, highly relevant to studies on the modes of interaction of nucleoside kinases with donors and acceptors; and, in particular, to interpretations of the recently reported crystal structures of dGK with bound ATP, of dNK with bound dCyd, and associated modeling studies.

Inorganic tripolyphosphate (PPP(i)) as a phosphate donor for human deoxyribonucleoside kinases.

Inorganic tripolyphosphate (PPP(i)) and pyrophosphate (PP(i)) were examined as potential phosphate donors for human deoxynucleoside kinase (dCK), deoxyguanosine kinase (dGK), cytosolic thymidine kinase (TK1), mitochondrial TK2, and the deoxynucleoside kinase (dNK) from Drosophila melanogaster. PPP(i) proved to be a good phosphate donor for dGK, as well as for dCK with dCyd, but not dAdo, as acceptor substrate, illustrating also the dependence of donor properties on acceptor. Products of phosphorylation were shown to be 5(')-phosphates. In striking contrast to ATP, the phosphorylation reaction follows strict Michaelis-Menten kinetics, with K(m) values of 74 and 92 microM for dCK and dGK, respectively, and V(max) values 40-50% that for ATP. With the other three enzymes, as well as for dCK with dAdo as acceptor, no, or only low levels (