From X to Y: Anatomy of a Constructional Pattern

Compositionality is undoubtedly one of the hardest problems in linguistics. In decoding theories, the speaker occupies a leading role, having to carefully choose the form that better encodes the meaning to be communicated. In contrast, in inferential theories, the burden is shifted from speaker to hearer: linguistic information typically underspecifies meaning and the hearer must make a number of inferences to bridge the gap between what is said and what is meant. In this article, I argue that constructional meaning can aid the process of sentence meaning formation by providing a scaffold that can help the hearer with the construal operations. Constructions, by providing an additional layer of meaning, constrain the range of possible meanings activated by words thereby reducing the combinatorial explosion when several words are joined together. This process is examined here by analysing the meanings associated with the grammatical construction [from X to Y], which is connected to a polysemy network of related senses, using examples extracted from a multimodal corpus. A preliminary analysis of the gesturing behaviour associated with the different senses proposed is also included, which can be seen to contribute to the characterisation of the different senses of the polysemy network.

A quantitative view of strategies to engineer cell-selective ligand binding

A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.

The complex build algorithm to set up starting structures of lanthanoid complexes with stereochemical control for molecular modeling

When handling metallic centers of higher coordination numbers, one is commonly deluded with the presumption that any assembled metal complex geometry (including a crystallographic one) is good enough as a starting structure for computational chemistry calculations; all oblivious to the fact that such a structure is nothing short of just one out of several, sometimes dozens, or even thousands of other stereoisomers. Moreover, coordination chirality, so frequently present in complexes of higher coordination numbers, is another often overlooked property, rarely recognized as such. The Complex Build algorithm advanced in this article has been designed with the purpose of generating starting structures for molecular modeling calculations with full stereochemical control, including stereoisomer complete identification and coordination chirality recognition. Besides being in the chosen correct stereochemistry, the ligands are positioned by the Complex Build algorithm in a very unobstructed and unclogged manner, so that their degrees of freedom do not hinder or even choke one another, something that would otherwise tend to lead to negative force constants after further geometry optimizations by more advanced computational model chemistries. The Complex Build algorithm has been conceived for any metallic center, but at present is targeting primarily lanthanoids whose coordination numbers range mostly from 5 to 12 and often lead to a combinatorial explosion of stereoisomers.

Hybrid Diagnosis Method Applied to Switched Mechatronic Systems

This article proposes a new diagnosis approach extended to switched mechatronic systems. The best tools of modeling and supervision, notably bond graph and observer, are used to move towards a high reliable fault detection and isolation approach. Researchers have always divided the hybrid observer into two blocks: a location observer that identifies the current mode and a continuous observer that detects faults. Applying the same logic to a system with a higher number of parameters from different energy domains increases the number of calculations and leads to a combinatorial explosion. The innovative interest of the present paper is the optimization of the observer's number using only one block to detect and, at the same time, locate faults. As a second objective, this paper presents an extension of the method to include complex industrial devices, which are in most cases switched mechatronic systems.

Bio inspired data mining for optimizing GPCR function identification

GPCR are the largest family of cell surface receptors; many of them still remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein, the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy (FES), data mining algorithm (DMA)). The authors propose also to use the BAT algorithm for extracting the pertinent features and the Genetic Algorithm to choose the best couple. They compared the results they we obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.

Oligogenic combinations of rare variants influence specific phenotypes in complex disorders

ABSTRACTGenetic studies of complex disorders such as autism and intellectual disability (ID) are often based on enrichment of individual rare variants or their aggregate burden in affected individuals compared to controls. However, these studies overlook the influence of combinations of rare variants that may not be deleterious on their own due to statistical challenges resulting from rarity and combinatorial explosion when enumerating variant combinations, limiting our ability to study oligogenic basis for these disorders. We present a framework that combines the apriori algorithm and statistical inference to identify specific combinations of mutated genes associated with complex phenotypes. Our approach overcomes computational barriers and exhaustively evaluates variant combinations to identify non-additive relationships between simultaneously mutated genes. Using this approach, we analyzed 6,189 individuals with autism and identified 718 combinations significantly associated with ID, and carriers of these combinations showed lower IQ than expected in an independent cohort of 1,878 individuals. These combinations were enriched for nervous system genes such as NIN and NGF, showed complex inheritance patterns, and were depleted in unaffected siblings. We found that an affected individual can carry many oligogenic combinations, each contributing to the same phenotype or distinct phenotypes at varying effect sizes. We also used this framework to identify combinations associated with multiple comorbid phenotypes, including mutations of COL28A1 and MFSD2B for ID and schizophrenia and ABCA4, DNAH10 and MC1R for ID and anxiety/depression. Our framework identifies a key component of missing heritability and provides a novel paradigm to untangle the genetic architecture of complex disorders.SIGNIFICANCEWhile rare mutations in single genes or their collective burden partially explain the genetic basis for complex disorders, the role of specific combinations of rare variants is not completely understood. This is because combinations of rare variants are rarer and evaluating all possible combinations would result in a combinatorial explosion, creating difficulties for statistical and computational analysis. We developed a data mining approach that overcomes these limitations to precisely quantify the influence of combinations of two or more mutated genes on a specific clinical feature or multiple co-occurring features. Our framework provides a new paradigm for dissecting the genetic causes of complex disorders and provides an impetus for its utility in clinical diagnosis.

Bounds in Tree-Based Approaches to Generate Project Portfolios in the Presence of Interactions

Portfolio decision models have become an important branch of decision analysis. Portfolio problems are inherently complex, because of the combinatorial explosion in the number of portfolios that can be constructed even from a small number of items. To efficiently construct a set of portfolios that provide good performance in multiple criteria, methods that guide the search process are needed. Such methods require the calculation of bounds to estimate the performance of portfolios that can be obtained from a given partial portfolio. The calculation of such bounds is particularly difficult if interactions between items in the portfolio are possible. In the paper, the authors introduce a method to represent such interactions and develop various bounds that can be used in the presence of interactions. These methods are then tested in a computational study, where they show that the bounds they propose frequently provide a good approximation of actual outcomes, and also analyze specific properties of the problem that influence the approximation quality of the proposed bounds.

Bio-Inspired Data Mining for Optimizing GPCR Function Identification

GPCR are the largest family of cell surface receptors; many of them still remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein, the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy (FES), data mining algorithm (DMA)). The authors propose also to use the BAT algorithm for extracting the pertinent features and the Genetic Algorithm to choose the best couple. They compared the results they we obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.

Discovery of complex oxides via automated experiments and data science

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

Eagle

Object sensitivity is widely used as a context abstraction for computing the points-to information context-sensitively for object-oriented programming languages such as Java. Due to the combinatorial explosion of contexts in large object-oriented programs, k -object-sensitive pointer analysis (under k -limiting), denoted k -obj , is often inefficient even when it is scalable for small values of k , where k ⩽ 2 holds typically. A recent popular approach for accelerating k -obj trades precision for efficiency by instructing k -obj to analyze only some methods in a program context-sensitively, determined heuristically by a pre-analysis. In this article, we investigate how to develop a fundamentally different approach, Eagle , for designing a pre-analysis that can make k -obj run significantly faster while maintaining its precision. The novelty of Eagle is to enable k -obj to analyze a method with partial context sensitivity (i.e., context-sensitively for only some of its selected variables/allocation sites) by solving a context-free-language (CFL) reachability problem based on a new CFL-reachability formulation of k -obj . By regularizing one CFL for specifying field accesses and using another CFL for specifying method calls, we have formulated Eagle as a fully context-sensitive taint analysis (without k -limiting) that is both effective (by selecting the variables/allocation sites to be analyzed by k -obj context-insensitively so as to reduce the number of context-sensitive facts inferred by k -obj in the program) and efficient (by running linearly in terms of the number of pointer assignment edges in the program). As Eagle represents the first precision-preserving pre-analysis, our evaluation focuses on demonstrating its significant performance benefits in accelerating k -obj for a set of popular Java benchmarks and applications, with call graph construction, may-fail-casting, and polymorphic call detection as three important client analyses.