Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.

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Inertia Weight strategies in Particle Swarm Optimization

Stream processing is a term that is used widely in the literature to describe a variety of systems. We present an overview of the historical development of stream processing and a detailed discussion of the different languages and techniques for programming with streams that can be found in the literature. This includes an analysis of dataflow, specialized functional and logic programming with streams, reactive systems, signal processing systems, and the use of streams in the design and verification of hardware.

A survey of stream processing

Preclinical studies have shown that hypomethylating agents reverse platinum resistance in ovarian cancer. In this phase II clinical trial, based upon the results of our phase I dose defining study, we tested the clinical and biologic activity of low-dose decitabine administered before carboplatin in platinum-resistant ovarian cancer patients. Among 17 patients with heavily pretreated and platinum-resistant ovarian cancer, the regimen induced a 35% objective response rate (RR) and progression-free survival (PFS) of 10.2 months, with nine patients (53%) free of progression at 6 months. Global and gene-specific DNA demethylation was achieved in peripheral blood mononuclear cells and tumors. The number of demethylated genes was greater (P < 0.05) in tumor biopsies from patients with PFS more than 6 versus less than 6 months (311 vs. 244 genes). Pathways enriched at baseline in tumors from patients with PFS more than 6 months included cytokine-cytokine receptor interactions, drug transporters, and mitogen-activated protein kinase, toll-like receptor and Jak-STAT signaling pathways, whereas those enriched in demethylated genes after decitabine treatment included pathways involved in cancer, Wnt signaling, and apoptosis (P < 0.01). Demethylation of MLH1, RASSF1A, HOXA10, and HOXA11 in tumors positively correlated with PFS (P < 0.05). Together, the results of this study suggest that low-dose decitabine altered DNA methylation of genes and cancer pathways, restoring sensitivity to carboplatin in patients with heavily pretreated ovarian cancer and resulting in a high RR and prolonged PFS.

https://cancerres.aacrjournals.org/content/canres/72/9/2197.full.pdf

Epigenetic Resensitization to Platinum in Ovarian Cancer

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.

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Introductory programming: a systematic literature review

Receptors for extracellular nucleotides are widely expressed by mammalian cells. They mediate a large array of responses ranging from growth stimulation to apoptosis, from chemotaxis to cell differentiation and from nociception to cytokine release, as well as neurotransmission. Pharma industry is involved in the development and clinical testing of drugs selectively targeting the different P1 nucleoside and P2 nucleotide receptor subtypes. As described in detail in the present review, P2 receptors are expressed by all tumours, in some cases to a very high level. Activation or inhibition of selected P2 receptor subtypes brings about cancer cell death or growth inhibition. The field has been largely neglected by current research in oncology, yet the evidence presented in this review, most of which is based on in vitro studies, although with a limited amount from in vivo experiments and human studies, warrants further efforts to explore the therapeutic potential of purinoceptor targeting in cancer.

/pdf/purinergic-signalling-and-cancer-eibdgev4a8.pdf

Purinergic signalling and cancer

A coupled map lattice (CML) provides a mathematical model for both spatially extended physical and biological systems, and for a new type of parallel computing system. We analyse a general process to realize a multilayer structured CML by coupling m CMLs together with arbitrary coupling structure. As an application we use this coupling process in the study of a CML with multilayer planar architecture and non-symmetric hierarchical coupling between the layers. These CMLs model neural architectures and use chaotic maps as the basic elements in the lattice. We measure the complexity of the state of each layer and an increase in spatio-temporal coherence as one ascends a hierarchical feedforward layered network.

Coupling CMLs and the synchronization of a multilayer neural computing system

First, we study the general idea of a spatially extended system (SES) and argue that many mathematical models of systems in computing and natural science are examples of SESs. We examine the computability and the equational definability of SESs and show that, in the discrete case, there is a natural sense in which an SES is computable if, and only if, it is definable by equations. We look at a simple idea of hierarchical structure for SESs and, using respacings and retimings, we define how one SES abstracts, approximates, or is implemented by another SES. Secondly, we study a special kind of SES called a synchronous concurrent algorithm (SCA). We define the simplest kind of SCA with a global clock and unit delay which are computable and equationally definable by primitive recursive equations over time. We focus on two examples of SCAs: a systolic array for convolution and a non-linear model of cardiac tissue. We investigate the hierarchical structure of SCAs by applying the earlier general concepts for the hierarchical structure of SESs. We apply the resulting SCA hierarchy to the formal analysis of both the implementation of a systolic array and the approximation of a biologically detailed model of cardiac tissue.

Hierarchies of Spatially Extended Systems and Synchronous Concurrent Algorithms

In this paper, we address the problem of reverse-engineering a gene regulatory network from gene expression time series. We approach the problem by implementing an ant system to generate candidate network structures. The quality of a candidate structure is evaluated using a particle swarm optimization algorithm that tunes the parameters of the corresponding model, by minimizing the error between the actual time series and the trained model's output. We extend this approach by incorporating domain-specific heuristics to the ant system, as a mechanism that has the potential to bias the pheromone amplification effect towards biologically plausible relationships. We apply the method to a subset of genes from a real world data set and report on the results.

/pdf/incorporating-heuristics-in-a-swarm-intelligence-framework-5069tahm7f.pdf

Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series

We are currently developing PyBlocks, a blocks-based environment which allows novice programmers to construct and execute Python programs. In the initial design of PyBlocks [1], Python's basic data types and lists are represented using colors, every expression block is colored according to its type, and each unfilled slot contains color indicating all valid argument types. In this paper we extend the design to include Python's most common built-in composite types (lists, tuples, dictionaries and sets) and to allow nesting of these where appropriate. Using example types from a pedagogical media computation library, we also show how further types may be supported. Together, these extensions provide almost any type novice Python programmers are likely to use.

/pdf/extending-the-design-of-a-blocks-based-python-environment-to-19z8lao1qt.pdf

Extending the design of a blocks-based python environment to support complex types

This paper details the design of a visual blocks-based tool for editing Python programs Its purpose is to close the gap between programming using a simplified blocks-based language and textual programming in a mainstream language As well as helping to guarantee the syntactic validity of programs, the tool aims to reduce the occurrence of run-time errors, a source of learner frustration with dynamic languages, by ensuring that constructed programs will remain well-typed during execution The design promotes understanding of how data types are used in the language by representing them using colors: each expression block is colored according to its type, and each unfilled hole contains colors which indicate valid argument types Connected blocks preserve conventional use of whitespace, demonstrating good practice for novice programmers

/pdf/design-of-a-blocks-based-environment-for-introductory-38bmvk633n.pdf

Matthew Poole

Papers

Coupling CMLs and the synchronization of a multilayer neural computing system

Hierarchies of Spatially Extended Systems and Synchronous Concurrent Algorithms

Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series

Extending the design of a blocks-based python environment to support complex types

Design of a blocks-based environment for introductory programming in Python