A Calculus for Subjective Communication

In this paper we introduce Subjective Communication, a new interaction model for CAS and generalizing the attribute-based communication introduced in the AbC calculus. In this model, a message is broadcasted to every process, but each process can view the very same message in different ways, depending on its attributes. To formalize this model, we introduce SCC, the Subjective Communication Calculus, for which we propose two semantics: Direct SCC, particularly useful when dealing with an edge computing communication paradigm, and Indirect SCC, more suited to a cloud-centric model. We then introduce a stateless bisimilarity for our semantics, which we prove to be a congruence

Distributed Programming of Smart Systems with Event-Condition-Action Rules

In recent years, event-driven programming languages, e.g. those based on Event Condition Action (ECA) rules, have emerged as a promising paradigm for implementing smart systems, such as IoT devices. Still, actual implementations are bound to a centralized infrastructure, limiting scalability and security. In this work, we present attribute-based memory updates (AbU), a new interaction mechanism aiming to extend the ECA programming paradigm to distributed systems. It relies on attribute-based communication, that is similar to broadcast, but receivers are selected “on the fly” by means of predicates over their attributes. With AbU, smart devices can be easily programmed via ECA rules and, at the same time, they can be deployed to a distributed network. Hence, a centralized infrastructure is not needed anymore: the computation is moved on the edge, improving reliability, scalability, privacy and security

The AbU Language: IoT Distributed Programming Made Easy

Event-driven programming based on Event-Condition-Action (ECA) rules allows users to define complex automation routines in a simple, declarative way; for this reason, in recent years ECA rules have been adopted by the majority of companies in the Internet of Things (IoT) industry as a promising paradigm for implementing ubiquitous and pervasive systems. Unfortunately, programming simplicity comes to a price: most implementations of ECA rules are bound to a strongly centralized communication infrastructure, that poses serious limitations on the application scenarios for the IoT, due to scalability, security and availability issues. To mitigate these issues, recent works introduced abstractions for communication and coordination of ensembles of IoT devices in a decentralized setting, effectively moving the computation towards the edge of the network without sacrificing the programming simplicity prerogative of ECA rules. In particular, Attribute-based memory Updates is a communication model transparently enhancing ECA rules-based systems with an interaction mechanism where communication is similar to broadcast but actual receivers are selected on the spot, by means of predicates (i.e., properties) over devices attributes. In this paper, we introduce AbU-dsl, a Domain Specific Language for the IoT that compiles on top of an implementation of Attribute-based memory Updates. In this way, AbU-dsl provides a practical development interface, based on ECA rules, to effectively program IoT devices in a fully decentralized setting, by exploiting a full-fledged attribute-based interaction model. Thus, programmers can specify interactions between devices in a declarative way, abstracting from details such as devices identity, number, or even their existence, without the need for a central controlling service

Computing (optimal) embeddings of directed bigraphs

Bigraphs and bigraphical reactive systems are a well-known meta-model successfully used for formalizing a wide range of models and situations, such as process calculi, service oriented architectures, multi-agent systems, biological systems, etc. A key problem in the theory and the implementations of bigraphs is how to compute embeddings, i.e., structure-preserving mappings of a given bigraph (the pattern or guest) inside another (the target or host). In this paper, we present an algorithm for computing embeddings for directed bigraphs, an extension of Milner's bigraphs which take into account the request directions between controls and names. This algorithm solves the embedding problem by means of a reduction to a constraint satisfaction problem. We first prove soundness and completeness of this algorithm; then we present an implementation in jLibBig, a general Java library for manipulating bigraphical reactive systems. The effectiveness of this implementation is shown by several experimental results. Finally, we show that this algorithm can be readily adapted to find the optimal embeddings in a weighted variant of the embedding problem

A forward genetics approach integrating genome-wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays)

The characterization of the genetic basis of maize (Zea mays) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29\ua0573 gene models in RILs and to derive 373\ua0769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript\u2013trait correlations. Then, leaf traits were associated with SNPs in a genome-wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript\u2013trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co-expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize

Polymorphic Abstract Syntax via Grothendieck Construction

Abstract. Abstract syntax with variable binding is known to be characterised as an initial algebra in a presheaf category. This paper extends it to the case of poly-morphic typed abstract syntax with binding. We consider two variations, second-order and higher-order polymorphic syntax. The central idea is to apply Fiore’s initial algebra characterisation of typed abstract syntax with binding repeatedly, i.e. first to the type structure and secondly to the term structure of polymorphic system. In this process, we use the Grothendieck construction to combine differ-ently staged categories of polymorphic contexts.

A dependent nominal type theory

Nominal abstract syntax is an approach to representing names and binding pioneered by Gabbay and Pitts. So far nominal techniques have mostly been studied using classical logic or model theory, not type theory. Nominal extensions to simple, dependent and ML-like polymorphic languages have been studied, but decidability and normalization results have only been established for simple nominal type theories. We present a LF-style dependent type theory extended with name-abstraction types, prove soundness and decidability of beta-eta-equivalence checking, discuss adequacy and canonical forms via an example, and discuss extensions such as dependently-typed recursion and induction principles

Assessing the genetic and molecular basis of resistance to Fusarium verticillioides in maize

Maize (Zea mays L.) is a major cereal crop, the second most cultivated crop in the world. Maize is used for human consumption, livestock feed, and biofuel. In addition to its economic importance, maize has been a widely used model species for genetics and plant biology. Among the limitations to maize production and seed quality, the several diseases caused by Fusarium are severe and largely diffused. Maize research has been oriented towards distinguishing the levels of resistance to ear rot caused by Fusarium verticillioides, however, it has not yet been possible to clarify the model of genetic action of the resistance that could guide the selection of resistant genotypes. The Multi-parent Advance Generation Intercross (MAGIC) maize population was previously used to identify quantitative trait loci (QTL) for Fusarium seedling rot resistance using the rolled towel assay (RTA) that allows fast and reliable phenotyping at early developmental stages. Production of transcriptomic data specific to the infection phase may increase the precision by which candidate genes are identified. RNA-Seq approach was used to compare the genome-wide gene expression patterns in maize scutella and early germinating shoots in the eight MAGIC maize founder lines in mock and F. verticilloides treated seeds. The RTAs were performed at 48, 72, 96, 120, 168 hours post inoculation (hpi) under two conditions control and treated to identify the appropriate time point for the investigation of MAGIC maize founder lines transcriptome profiles. Twenty seeds were used for each RTA in both treatments, in the treated, the seeds were inoculated with 100 \u3bcl of a 3.5 x 106 ml-1 spore suspension of F. verticillioides ITEM10027 (MPVP 294). Real-time PCR was applied on plant and pathogen specific genes to identify the best time point for RNA extraction, which turned out to be 72 hpi. RNA was extracted from the scutella and early germinating shoots and a total of 48 cDNA libraries (8 genotypes x 2 conditions x 3 biological replicates) have been produced and subjected to sequencing. Transcriptomic data on the parental lines will be projected onto recombinant inbred lines reconstructed genomes and used to narrow down QTL intervals to their genetic determinants. The defense-related transcriptional changes will shed light on and related them to the specific genomic regions identified by QTL mapping

TRANSCRIPTIONAL ANALYSIS OF EIGHT MAGIC MAIZE PARENTAL LINES INFECTED WITH FUSARIUM VERTICILLIOIDES

Maize (Zea mays L.) is among the most important crops worldwide for food, feed, biofuels, and industrial applications. Its cultivation faces significant constraints due to Fusarium species that affect the quality and quantity of maize products. Among these, Fusarium verticillioides is responsible for severe diseases including seedling blights, stalk rot, and ear rot. The impact of the fungus is worsened by the fact that chemical and agronomic measures used to control Fusarium infection are often inefficient. Hence, genetic resistance is considered the most reliable resource to reduce damages caused by F. verticillioides. This study aims to elucidate the genetic basis of resistance to this fungus in maize. Young seedlings of eight divergent maize lines, founder of the MAGIC population, were artificially inoculated with a F. verticillioides strain using the rolled towel assay method. Total RNA was extracted from both control and treated samples after 72 hours of artificial inoculation and underwent paired-end sequenced with Illumina technology. Here we report the use this large transcriptomic dataset to identify the early transcriptional changes and the differentially expressed genes (DEGs) involved in fungal infection. The analysis identified several hundred DEGs, whose functions were explored through Gene Ontology enrichment analysis. A co-expression network analysis further refined the set of genes with potential implications in disease response. The results identify a limited set of genes that might play an important roles in maize resistance to F. verticillioides providing new insights into the molecular resistance mechanisms against the pathogen