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How do children learn what a word means when its uses are consistent with many possible meanings? One influential idea is that children rely on an inductive bias that ensures that novel words get assigned distinct meanings from known words— mutual exclusivity. Here, we explore the possibility that mutual-exclusivity phenomena do not reflect a bias but rather information encoded in the message. Learners might effectively be told when (and when not) to assume that word meanings are mutually exclusive. In three experiments (N = 106 from across the United States; ages 2 years, 0 months−2 years, 11 months), we show that 2-year-olds only assumed that novel words have distinct meanings if the words were spoken with focus, an information-structural marker of contrast. Without focus, we found no mutual exclusivity; novel words were understood to label familiar objects. These results provide a novel account of mutual exclusivity and demonstrate an early emerging understanding of focus and information structure. [ABSTRACT FROM AUTHOR]

Upon hearing a novel label, listeners tend to assume that it refers to a novel, rather than a familiar object. While this disambiguation or mutual exclusivity (ME) effect has been robustly shown across development, it is unclear what it involves. Do listeners use their pragmatic and lexical knowledge to exclude the familiar object and thus select the novel one? Or is the effect, at least in early childhood, simply based on an attraction to novelty and a direct mapping of the novel label to a novel object? In a preregistered online study with 2- to 3-year-olds (n = 75) and adults (n = 112), we examined (a) whether relative object novelty alone (without pragmatic or lexical information) could account for participants' disambiguation and (b) whether participants' decision processes involved reasoning by exclusion. Participants encountered either a known and an unknown object (classic ME condition) or two unknown objects, one completely novel and one preexposed (novelty condition) as potential referents of a novel label. Reasoning by exclusion was assessed by children's looking patterns and adults' explanations. In the classic ME condition, children and adults significantly chose the novel object and both used reasoning by exclusion. In contrast, in the novelty condition, children and adults chose randomly. Across conditions, a retention test revealed that adults remembered their prior selections, while children's performance was fragile. These results suggest that referent disambiguation is not based on relative object novelty alone. Instead, to resolve referential ambiguity, both young children and adults seem to make use of pragmatic and/or lexical sources of information and to engage in reasoning by exclusion strategies. Public Significance Statement: From early on, children seem to find the referents of novel words with relative ease. This study found that young toddlers' success is not driven by a simple attraction to the novelty of certain referents. Instead, they seem to use their pragmatic and lexical knowledge, in combination with their logical reasoning abilities, to exclude unlikely referents of novel words. [ABSTRACT FROM AUTHOR]

Linguistic interventions aim to change our linguistic practices. A commonly discussed type of linguistic intervention is meaning revision, which seeks to associate existing words with new or revised meanings. But why does retaining old words matter so much? Why not instead introduce new words to express the newly defined meanings? Drawing on relevant psycholinguistic research, this paper develops an empirically motivated, general, and practically useful pro tanto reason to retain rather than replace the original word during the process of conceptual improvement. [ABSTRACT FROM AUTHOR]

Although linguistic and nonlinguistic cues help young children infer meaning when presented with unfamiliar words, little is known about how syntactic information and early bilingual experience shape word learning. This study examined how monolingual and bilingual 24- to 30-month-olds' disambiguation of novel words during a mutual exclusivity task differs as a function of syntactic cues, age, and productive vocabulary. English monolinguals and Spanish-English bilinguals were presented with familiar and novel objects within a syntactic context (e.g., "Give me the blick!") or in isolation (e.g., "Blick!"). Results showed that monolinguals and bilinguals adhered to mutual exclusivity more often when provided with syntactic cues than when those cues were absent. Furthermore, bilinguals' mutually exclusive disambiguation of novel words increased with age, but only when syntactic cues were available. These results provide insight into factors that influence children's disambiguation of novel words. The theoretical implications of these findings are discussed.

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data. Cancer is known to arise after a cell experiences multiple driver mutations that allow it to grow uncontrollably and ultimately metastasize to distant anatomic sites. In this paper, we introduce a computational approach for identifying driver mutations by leveraging the mutual exclusivity of genes and variants within oncogenic pathways. [ABSTRACT FROM AUTHOR]

Influential theories and computational models suggest error-based learning plays an important role in language acquisition: Children learn new words by generating predictions about upcoming utterances and revising those predictions when they are erroneous. Critically, revising stronger (rather than weaker) predictions should further enhance learning. Although previously demonstrated in adults, such prediction error boost has not been conclusively shown in children. To close this gap, we tested 107 participants between the ages of 5 and 10. We found little evidence that word learning in this age group benefits from a prediction error boost. Moreover, we also failed to replicate previous evidence for such an effect in adults. Based on a detailed task analysis, we suggest the variation in adult findings may be partly explained by differences in encoding strategies and that, relatedly, the protracted development of the episodic memory system might explain why children do not experience robust benefits from having stronger (rather than weaker) predictions disconfirmed. [ABSTRACT FROM AUTHOR]

How much information do language users need to differentiate potentially absolute synonyms into near-synonyms? How consistent must the information be? We present two simple experiments designed to investigate this. After exposure to two novel verbs, participants generalized them to positive or negative contexts. In Experiment 1, there was a tendency across conditions for the verbs to become differentiated by context, even following inconsistent, random, or neutral information about context during exposure. While a subset of participants matched input probabilities, a high proportion did not. As a consequence, the overall pattern was of growth in differentiation that did not closely track input distributions. Rather, there were two main patterns: When each verb had been presented consistently in a positive or negative context, participants overwhelmingly specialized both verbs in their output. When this was not the case, the verbs tended to become partially differentiated, with one becoming specialized and the other remaining less specialized. Experiment 2 replicated and expanded on Experiment 1 with the addition of a pragmatic judgment task and neutral contexts at test. Its results were consistent with Experiment 1 in supporting the conclusion that quality of input may be more important than quantity in the differentiation of synonyms. [ABSTRACT FROM AUTHOR]

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

How do children succeed in learning a word? Research has shown robustly that, in ambiguous labeling situations, young children assume novel labels to refer to unfamiliar rather than familiar objects. However, ongoing debates center on the underlying mechanism: Is this behavior based on lexical constraints, guided by pragmatic reasoning, or simply driven by children's attraction to novelty? Additionally, recent research has questioned whether children's disambiguation leads to long‐term learning or rather indicates an attentional shift in the moment of the conversation. Thus, we conducted a pre‐registered online study with 2‐ and 3‐year‐olds and adults. Participants were presented with unknown objects as potential referents for a novel word. Across conditions, we manipulated whether the only difference between both objects was their relative novelty to the participant or whether, in addition, participants were provided with pragmatic information that indicated which object the speaker referred to. We tested participants' immediate referent selection and their retention after 5 min. Results revealed that when given common ground information both age groups inferred the correct referent with high success and enhanced behavioral certainty. Without this information, object novelty alone did not guide their selection. After 5 min, adults remembered their previous selections above chance in both conditions, while children only showed reliable learning in the pragmatic condition. The pattern of results indicates how pragmatics may aid referent disambiguation and learning in both adults and young children. From early ontogeny on, children's social‐cognitive understanding may guide their communicative interactions and support their language acquisition. Research Highlights: We tested how 2‐3‐year‐olds and adults resolve referential ambiguity without any lexical cues.In the pragmatic context both age groups disambiguated novel word‐object‐mappings, while object novelty alone did not guide their referent selection.In the pragmatic context, children also showed increased certainty in disambiguation and retained new word‐object‐mappings over time.These findings contribute to the ongoing debate on whether children learn words on the basis of domain‐specific constraints, lower‐level associative mechanisms, or pragmatic inferences. [ABSTRACT FROM AUTHOR]

We present a novel framework for building pragmatic artificialagents with explicit and trainable semantic representations, us-ing the Rational Speech Act model. We train our agents onsupervised and unsupervised communication games and com-pare their behavior to literal agents lacking pragmatic abilities.For both types of games pragmatic but not literal agents evolvea mutual exclusivity bias. This provides a computational prag-matic account of mutual exclusivity and points out a possi-ble direction for solving the mutual exclusivity bias challengeposed by Gandhi and Lake (2019). We find that pragmaticreasoning can cause the bias either by promoting lexical con-straints during learning, or by affecting online inference. In ad-dition we show that pragmatic abilities lead to faster learningand that this advantage is even stronger when meanings to becommunicated follow a more natural distribution as describedby Zipf’s law.

Children’s tendency to associate novel words with novel refer-ents has been taken to reflect a bias toward mutual exclusivity.This tendency may be advantageous both as (1) an ad-hoc ref-erent selection heuristic to single out referents lacking a labeland as (2) an organizing principle of lexical acquisition. Thispaper investigates under which circumstances cross-situationalneural models can come to exhibit analogous behavior to chil-dren, focusing on these two possibilities and their interaction.To this end, we evaluate neural networks’ on both symbolicdata and, as a first, on large-scale image data. We find thatconstraints in both learning and selection can foster mutual ex-clusivity, as long as they put words in competition for lexi-cal meaning. For computational models, these findings clarifythe role of available options for better performance in taskswhere mutual exclusivity is advantageous. For cognitive re-search, they highlight latent interactions between word learn-ing, referent selection mechanisms, and the structure of stimuliof varying complexity: symbolic and visual.

Determinantal Point Processes (DPPs) are probabilisticmodels of repulsion, capturing negative dependenciesbetween states. Here, we show that a DPP inrepresentation-space predicts inferential biases towardmutual exclusivity commonly observed in word learning(mutual exclusivity bias) and reasoning (disjunctivesyllogism) tasks. It does so without requiring explicitrule representations, without supervision, and withoutexplicit knowledge transfer. The DPP attempts tomaximize the total ”volume” spanned by the set ofinferred code-vectors. In a representational system inwhich combinatorial codes are constructed by re-usingcomponents, a DPP will naturally favor the combinationof previously un-used components. We suggest thatthis bias toward the selection of volume-maximizingcombinations may exist to promote the efficient retrievalof individuals from memory. In support of this, we showthe same algorithm implements efficient ”hashing”,minimizing collisions between key/value pairs withoutexpanding the required storage space. We suggestthat the mechanisms that promote efficient memorysearch may also underlie cognitive biases in structuredinference.

Children's ability to learn new words during their preschool years is crucial for further academic success. Previous research suggests that children rely on different learning mechanisms to acquire new words depending on the available context and linguistic information. To date, there is limited research integrating different paradigms to provide a cohesive view of the mechanisms and processes involved in preschool children's word learning. We presented 4 year-old children (n D 47) with one of three different novel word-learning scenarios to test their ability to connect novel words to their correspondent referents without explicit instruction to do so. The scenarios were tested with three exposure conditions of different nature: (i) mutual exclusivity-target novel word-referent pair presented with a familiar referent, prompting fast-mapping via disambiguation, (ii) cross-situational-target novel word-referent pair presented next to an unfamiliar referent prompting statistically tracking the target pairs across trials, and (iii) eBook - target word-referent pairs presented within an audio-visual electronic storybook (eBook), prompting inferring meaning incidentally. Results show children succeed at learning the new words above chance in all three scenarios, with higher performance in eBook and mutual exclusivity than in cross-situational word learning. This illustrates children's astounding ability to learn while coping with uncertainty and varying degrees of ambiguity, which are common in real-world situations. Findings extend our understanding of how preschoolers learn new words more or less successfully depending on specific word learning scenarios, which should be taken into account when working on vocabulary development for school readiness in the preschool years. [ABSTRACT FROM AUTHOR]

Children’s ability to learn new words during their preschool years is crucial for further academic success. Previous research suggests that children rely on different learning mechanisms to acquire new words depending on the available context and linguistic information. To date, there is limited research integrating different paradigms to provide a cohesive view of the mechanisms and processes involved in preschool children’s word learning. We presented 4 year-old children (n = 47) with one of three different novel word-learning scenarios to test their ability to connect novel words to their correspondent referents without explicit instruction to do so. The scenarios were tested with three exposure conditions of different nature: (i) mutual exclusivity–target novel word-referent pair presented with a familiar referent, prompting fast-mapping via disambiguation, (ii) cross-situational–target novel word-referent pair presented next to an unfamiliar referent prompting statistically tracking the target pairs across trials, and (iii) eBook - target word-referent pairs presented within an audio-visual electronic storybook (eBook), prompting inferring meaning incidentally. Results show children succeed at learning the new words above chance in all three scenarios, with higher performance in eBook and mutual exclusivity than in cross-situational word learning. This illustrates children’s astounding ability to learn while coping with uncertainty and varying degrees of ambiguity, which are common in real-world situations. Findings extend our understanding of how preschoolers learn new words more or less successfully depending on specific word learning scenarios, which should be taken into account when working on vocabulary development for school readiness in the preschool years.

To efficiently learn new words, children use constraints such as mutual exclusivity (ME) to narrow the search for potential referents. The current study investigated the use of ME in toddlers with autism spectrum disorder (ASD) and neurotypical (NT) peers matched on nonverbal cognition. Thirty-two toddlers with ASD and 26 NT toddlers participated in a looking-while-listening task. Images of novel and familiar objects were presented along with a novel or familiar label. Overall, toddlers with ASD showed less efficient looking toward a novel referent when a novel label was presented compared to NT toddlers, controlling for age and familiar word knowledge. However, toddlers with ASD and higher language ability demonstrated more robust use of ME than those with lower language ability. [ABSTRACT FROM AUTHOR]

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases. [ABSTRACT FROM AUTHOR]

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases.

Abstract Background Lung adenocarcinoma (LUAD), as the most common subtype of lung cancer, is the leading cause of cancer deaths in the world. The accumulation of driver gene mutations enables cancer cells to gradually acquire growth advantage. Therefore, it is important to understand the functions and interactions of driver gene mutations in cancer progression. Methods We obtained gene mutation data and gene expression profile of 506 LUAD tumors from The Cancer Genome Atlas (TCGA). The subtypes of tumors with driver gene mutations were identified by consensus cluster analysis. Results We found 21 significantly mutually exclusive pairs consisting of 20 genes among 506 LUAD patients. Because of the increased transcriptomic heterogeneity of mutations, we identified subtypes among tumors with non‐silent mutations in driver genes. There were 494 mutually exclusive pairs found among driver gene mutations within different subtypes. Furthermore, we identified functions of mutually exclusive pairs based on the hypothesis of functional redundancy of mutual exclusivity. These mutually exclusive pairs were significantly enriched in nuclear division and humoral immune response, which played crucial roles in cancer initiation and progression. We also found 79 mutually exclusive triples among subtypes of tumors with driver gene mutations, which were key roles in cell motility and cellular chemical homeostasis. In addition, two mutually exclusive triples and one mutually exclusive triple were associated with the overall survival and disease‐specific survival of LUAD patients, respectively. Conclusions We revealed novel mutual exclusivity and generated a comprehensive functional landscape of driver gene mutations, which could offer a new perspective to understand the mechanisms of cancer development and identify potential biomarkers for LUAD therapy.

The main challenge in cancer genomics is to distinguish the driver genes from passenger or neutral genes. Cancer genomes exhibit extensive mutational heterogeneity that no two genomes contain exactly the same somatic mutations. Such mutual exclusivity (ME) of mutations has been observed in cancer data and is associated with functional pathways. Analysis of ME patterns may provide useful clues to driver genes or pathways and may suggest novel understandings of cancer progression. In this article, we consider a probabilistic, generative model of ME, and propose a powerful and greedy algorithm to select the mutual exclusivity gene sets. The greedy method includes a pre-selection procedure and a stepwise forward algorithm which can significantly reduce computation time. Power calculations suggest that the new method is efficient and powerful for one ME set or multiple ME sets with overlapping genes. We illustrate this approach by analysis of the whole-exome sequencing data of cancer types from TCGA. [ABSTRACT FROM AUTHOR]

Study of pairwise genetic interactions, such as mutually exclusive mutations, has led to understanding of underlying mechanisms in cancer. Investigation of various combinatorial motifs within networks of such interactions can lead to deeper insights into its mutational landscape and inform therapy development. One such motif called the Between-Pathway Model (BPM) represents redundant or compensatory pathways that can be therapeutically exploited. Finding such BPM motifs is challenging since most formulations require solving variants of the NP-complete maximum weight bipartite subgraph problem. In this paper we design an algorithm based on Integer Linear Programming (ILP) to solve this problem. In our experiments, our approach outperforms the best previous method to mine BPM motifs. Further, our ILP-based approach allows us to easily model additional application-specific constraints. We illustrate this advantage through a new application of BPM motifs that can potentially aid in finding combination therapies to combat cancer. [ABSTRACT FROM AUTHOR]

For decades, theories of early word learning have assumedthat infants are equipped with learning biases that help themlearn words at a fast pace. One of these biases, called MutualExclusivity, suggests that infants reject second labels forname-known objects. Our first two experiments, with childrenand with infants, suggest that novelty preference duringMutual Exclusivity tasks should not be taken as evidence thatassociations between novel labels and name-known objectshave not taken place. A third experiment, supplemented withcomputational modeling, ruled out cascaded activationpatterns as alternative explanations and, instead, confirmedthat word-object associations are non-selective throughoutinfancy and childhood.

Past mechanistic accounts of children’s word learningclaim that a simple type of cross-situational learning ispowerful enough to match observed rates of learning,even in quite ambiguous situations. However, a limita-tion in some of these analyses is their reliance on an un-realistic assumption that the learner only hears a word insituations containing the intended referent. This studyanalyzed a more general type of cross-situational learn-ing based on the relative frequency of word-object pairs,and found it to be slower than the simple mechanismanalyzed in prior work. We then analytically exploredwhether relative-frequency learning can be improved byincorporating the mutual exclusivity (ME) principle–an assumption that words map to objects 1-to-1. Ouranalyses show that with a certain type of correlation inword-to-word relationship, ME makes relative frequencylearning as efficient as fast-mapping, which can learn aword in one exposure.

Mutual Exclusivity analysis of genomic aberrations contributes to the exploration of potential synthetic lethal (SL) relationships thus guiding the nomination of specific cancer cells vulnerabilities. When multiple classes of genomic aberrations and large cohorts of patients are interrogated, exhaustive genome-wide analyses are not computationally feasible with commonly used approaches. Here we present Fast Mutual Exclusivity (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation of the Fisher’s exact test to achieve fast computation of genome-wide mutual exclusivity tests; we show that brute force testing for mutual exclusivity of hundreds of millions of aberrations combinations can be performed in few minutes. We applied FaME to allele-specific data from whole exome experiments of 27 TCGA studies cohorts, detecting both mutual exclusivity of point mutations, as well as allele-specific copy number signals that span sets of contiguous cytobands. We next focused on a case study involving the loss of tumor suppressors and druggable genes while exploiting an integrated analysis of both public cell lines loss of function screens data and patients’ transcriptomic profiles. FaME algorithm implementation as well as allele-specific analysis output are publicly available at https://github.com/demichelislab/FaME.

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as crosssituational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from cross-situational data, as needed to solve problems such as image captioning and visual question answering. These networks are potentially appealing as cognitive models because they can learn from raw visual and linguistic stimuli, something previous cognitive models have not addressed. In this paper, we examine whether recent machine learning approaches can help explain various behavioral phenomena from the psychological literature on crosssituational word learning. We consider two variants of a multimodal neural network architecture and look at seven different phenomena associated with cross-situational word learning and word learning more generally. Our results show that these networks can learn word-referent mappings from a single epoch of training, mimicking the amount of training commonly found in cross-situational word learning experiments. Additionally, these networks capture some, but not all of the phenomena we studied, with all of the failures related to reasoning via mutual exclusivity. These results provide insight into the kinds of phenomena that arise naturally from relatively generic neural network learning algorithms, and which word learning phenomena require additional inductive biases. [ABSTRACT FROM AUTHOR]

Abstract Background Idiopathic pulmonary fibrosis (IPF) is associated with an increased risk for lung cancer, but the underlying mechanisms driving malignant transformation remain largely unknown. This study aimed to identify differentially expressed genes (DEGs) distinguishing IPF and lung cancer from healthy individuals and common genes driving the transformation from healthy to IPF and lung cancer. Methods The gene expression data for IPF and non-small cell lung cancer (NSCLC) were retrieved from the Gene Expression Omnibus (GEO) database. The DEG signatures were identified via unsupervised two-way clustering (TWC) analysis, supervised support vector machine analysis, dimensional reduction, and mutual exclusivity analysis. Gene enrichment and pathway analyses were performed to identify common signaling pathways. The most significant signature genes in common among IPF and lung cancer were further verified by immunohistochemistry. Results The gene expression data from GSE24206 and GSE18842 were merged into a super array dataset comprising 86 patients with lung disorders (17 IPF and 46 NSCLC) and 51 healthy controls and measuring 23,494 unique genes. Seventy-nine signature DEGs were found among IPF and NSCLC. The peroxisome proliferator-activated receptor (PPAR) signaling pathway was the most enriched pathway associated with lung disorders, and matrix metalloproteinase-1 (MMP-1) in this pathway was mutually exclusive with several genes in IPF and NSCLC. Subsequent immunohistochemical analysis verified enhanced MMP1 expression in NSCLC associated with IPF. Conclusions For the first time, we defined common signature genes for IPF and NSCLC. The mutually exclusive sets of genes were potential drivers for IPF and NSCLC.

This study explored monolingual and multilingual two- to five-year-olds' reliance on a non-verbal and a verbal cue during word-referent mapping, in relation to vocabulary knowledge and, for the multilinguals, Dutch language exposure. Ninety monolingual and sixty-seven multilingual children performed a referential conflict experiment that pitted a non-verbal (pointing) cue and a verbal (mutual exclusivity) cue. Mixed-effect regressions showed no main effects of vocabulary and language exposure. An interaction between vocabulary and group showed that lower vocabulary scores were associated with a stronger reliance on pointing over mutual exclusivity for multilinguals (but not monolinguals). Furthermore, an interaction between vocabulary, language exposure, and cue word (novel vs. familiar label) indicated that multilinguals with lower exposure and lower vocabulary showed a stronger reliance on pointing over mutual exclusivity when a novel rather than familiar word was used. These findings suggest that multilingual and monolingual children go through different trajectories when learning to map words to referents.

Summary: Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

The goal of this review is twofold: first to explore whether mutual exclusivity and functional fixedness overlap and what might be their respective specificities and second, to investigate whether mutual exclusivity as an inferential principle could be applied in other domains than language and whether it can be found in non-human species. In order to do that, we first give an overview of the representative studies of each phenomenon. We then analyze papers on tool use learning in children that studied or observed one of these phenomena. We argue that, despite their common principle -one tool one function- mutual exclusivity and functional fixedness are two distinct phenomena and need to be addressed separately in order to fully understand the mechanisms underlying social learning and cognition. In addition, mutual exclusivity appears to be applicable in other domains than language learning, namely tool use learning and is also found in non-human species when learning symbols and tools.

Abstract High‐throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity‐coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers.

Children frequently apply a novel label to a novel object, a behavior known as the mutual exclusivity bias (MEB). This study examined how MEB affects children’s retention for word mappings. In Experiment 1, preschoolers (N = 39; Mage = 46.62 months) and adults (N = 24; Mage = 21.63 years) completed an immediate word mapping task and a delayed retention test. Both samples used MEB during referent selection, but neither group displayed higher retention for words mapped via MEB than words mapped via other referent selection strategies at test. Experiment 2 replicated Experiment 1 with preschoolers (N = 85; Mage = 47.78 months) and provided evidence against the possibility that interference from multiple words contributed to children’s faster forgetting of word mappings when using MEB. Experiment 3 presented children (N = 30; Mage = 51.13 months) with an abbreviated version of the task, providing evidence against the alternative hypothesis that cognitive load during learning caused the forgetting observed in Experiments 1 and 2. Taken together, these experiments suggest that MEB supports initial word mapping but may not provide an advantage for long-term retention.

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric. [ABSTRACT FROM AUTHOR]

The goal of this review is twofold: first to explore whether mutual exclusivity and functional fixedness overlap and what might be their respective specificities and second, to investigate whether mutual exclusivity as an inferential principle could be applied in other domains than language and whether it can be found in non-human species. In order to do that, we first give an overview of the representative studies of each phenomenon. We then analyze papers on tool use learning in children that studied or observed one of these phenomena. We argue that, despite their common principle -one tool one function- mutual exclusivity and functional fixedness are two distinct phenomena and need to be addressed separately in order to fully understand the mechanisms underlying social learning and cognition. In addition, mutual exclusivity appears to be applicable in other domains than language learning, namely tool use learning and is also found in non-human species when learning symbols and tools. [ABSTRACT FROM AUTHOR]

Children frequently apply a novel label to a novel object, a behavior known as the mutual exclusivity bias (MEB). This study examined how MEB affects children's retention for word mappings. In Experiment 1, preschoolers (N = 39; M age = 46.62 months) and adults (N = 24; M age = 21.63 years) completed an immediate word mapping task and a delayed retention test. Both samples used MEB during referent selection, but neither group displayed higher retention for words mapped via MEB than words mapped via other referent selection strategies at test. Experiment 2 replicated Experiment 1 with preschoolers (N = 85; M age = 47.78 months) and provided evidence against the possibility that interference from multiple words contributed to children's faster forgetting of word mappings when using MEB. Experiment 3 presented children (N = 30; M age = 51.13 months) with an abbreviated version of the task, providing evidence against the alternative hypothesis that cognitive load during learning caused the forgetting observed in Experiments 1 and 2. Taken together, these experiments suggest that MEB supports initial word mapping but may not provide an advantage for long-term retention. [ABSTRACT FROM AUTHOR]

High‐throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity‐coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers. [ABSTRACT FROM AUTHOR]

In English, many words can be used flexibly to label artifacts,as nouns, or functional uses of those artifacts, as verbs:We can shovel snow with a shovel and comb our hair with acomb. Here, we examine whether young children form generalizationsabout flexibility from early in life and use suchgeneralizations to predict new word meanings. When childrenlearn a new word for an artifact, do they also expect itto label its functional use, and vice versa? In Experiment 1,we show that when four- and five-year-olds are taught a firstnovel word to label a familiar action—e.g., that bucking meansshoveling—they exclude the artifact involved in this action—i.e., the shovel—as the meaning of a second novel word (e.g.,gork). This suggests that children spontaneously expected thefirst novel word—which referred to the action—to also refer tothe artifact. In Experiment 2, we show that this pattern extendsto words that label novel actions involving novel artifacts, suggestingthat children expect any word for an action to label theartifact that helps carry out that action. Experiment 3 traceshow such generalizations may arise in development. In particular,we show that while four- and five-year-olds each expectwords to label artifacts and their functional uses, three-yearoldsmay not.

Human infants learn meanings for words in interaction withtheir environment. Individual learning scenarios can beambiguous due to the presence of several words and possiblemeanings. One possible way to overcome ambiguity is calledcross-situational learning (XSL), where information isgathered over several learning trials. Experimental studies ofhuman XSL have shown that cognitive constraints, such asattention and memory limitations, decrease humanperformance when compared to computer models that canstore all available information. In this paper, we approachmodeling of human performance with a novel computationalXSL algorithm, FAMM (Familiarity preference, Associativelearning, Mutual exclusivity, Memory decay), equipped withthe four main components motivated by experimentalresearch. The model is evaluated based on a number of earlierXSL experiments that probe different aspects of learning.FAMM is shown to provide a better fit to the behavioral datathan the earlier proposed model of Kachergis et al. (2012

Identification of cancer driver modules or pathways is a key step in understanding cancer pathogenesis and exploring patient-specific treatments. Numerous studies have shown that some genes with low mutation frequency are also important for the cancer progression, while previous research have focused on identifying high-frequency mutation genes. In this study, we propose a new framework with a new metric to identify driver modules with low-frequency mutation genes, called iCDModule. Inspired by the gravity model, we integrate the coverage and mutual exclusivity in mutation information, define a new metric between gene pairs, called mutation impact distance, to help identifying potential driver genes sets, including those have extremely low mutation rates but play an important role in functional networks. A genetic network is constructed by combining the defined mutation impact distance and then the driver module identification problem is formalized as the maximum clique solution problem, and an improved ant colony optimization algorithm is used to solve it. iCDModule is applied to TCGA breast cancer, glioblastoma, ovarian cancer to test performance. Experiments show that it can accurately identify known cancer driver modules and pathways, and also detect driver modules containing low-frequency mutation genes. iCDModule is significantly better than other existing methods in identifying driver modules.

The present research aims at finding to what extent social-pragmatic cues that conflict with mutual exclusivity lead preschoolers to exclude a novel object as a referent for a novel word. Sixty early and late 3-year-old preschoolers randomly participated in one of the three conditions. In the first condition, preschoolers’ tendency to select an unfamiliar object for an unfamiliar word is investigated in the absence of social-pragmatic cues that contradict mutual exclusivity. The second condition is aimed to investigate if partial social-pragmatic cues, such as pointing towards a familiar object, interfere with mutual exclusivity. In the third condition, pointing towards a familiar object is accompanied by gazing alternately between the familiar object and preschoolers to investigate whether preschoolers abandon or still honor mutual exclusivity. The results indicate that in the absence of any social-pragmatic cues, preschoolers use a familiar object as a cue leading them to match a novel object with a novel word. Partial cues such as pointing towards familiar objects do not make any significant difference in preschoolers’ familiar/unfamiliar object selection for an unfamiliar word. If both of the social-pragmatic cues are available, preschoolers suspend mutual exclusivity in indirect word learning situations.

Biosemiosis is a process of choice-making between simultaneously alternative options. It is well-known that, when sufficiently young children encounter a new word, they tend to interpret it as pointing to a meaning that does not have a word yet in their lexicon rather than to a meaning that already has a word attached. In previous research, the strategy was shown to be optimal from an information theoretic standpoint. In that framework, interpretation is hypothesized to be driven by the minimization of a cost function: the option of least communication cost is chosen. However, the information theoretic model employed in that research neither explains the weakening of that vocabulary learning bias in older children or polylinguals nor reproduces Zipf's meaning-frequency law, namely the non-linear relationship between the number of meanings of a word and its frequency. Here we consider a generalization of the model that is channeled to reproduce that law. The analysis of the new model reveals regions of the phase space where the bias disappears consistently with the weakening or loss of the bias in older children or polylinguals. The model is abstract enough to support future research on other levels of life that are relevant to biosemiotics. In the deep learning era, the model is a transparent low-dimensional tool for future experimental research and illustrates the predictive power of a theoretical framework originally designed to shed light on the origins of Zipf's rank-frequency law. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD), as the most common subtype of lung cancer, is the leading cause of cancer deaths in the world. The accumulation of driver gene mutations enables cancer cells to gradually acquire growth advantage. Therefore, it is important to understand the functions and interactions of driver gene mutations in cancer progression. Methods: We obtained gene mutation data and gene expression profile of 506 LUAD tumors from The Cancer Genome Atlas (TCGA). The subtypes of tumors with driver gene mutations were identified by consensus cluster analysis. Results: We found 21 significantly mutually exclusive pairs consisting of 20 genes among 506 LUAD patients. Because of the increased transcriptomic heterogeneity of mutations, we identified subtypes among tumors with non‐silent mutations in driver genes. There were 494 mutually exclusive pairs found among driver gene mutations within different subtypes. Furthermore, we identified functions of mutually exclusive pairs based on the hypothesis of functional redundancy of mutual exclusivity. These mutually exclusive pairs were significantly enriched in nuclear division and humoral immune response, which played crucial roles in cancer initiation and progression. We also found 79 mutually exclusive triples among subtypes of tumors with driver gene mutations, which were key roles in cell motility and cellular chemical homeostasis. In addition, two mutually exclusive triples and one mutually exclusive triple were associated with the overall survival and disease‐specific survival of LUAD patients, respectively. Conclusions: We revealed novel mutual exclusivity and generated a comprehensive functional landscape of driver gene mutations, which could offer a new perspective to understand the mechanisms of cancer development and identify potential biomarkers for LUAD therapy. [ABSTRACT FROM AUTHOR]

Developmental research, like many fields, is plagued by low sample sizes and inconclusive findings. The problem is amplified by the difficulties associated with recruiting infant participants for research as well as the increased variability in infant responses. With sequential testing designs providing a viable alternative to paradigms facing such issues, the current study implemented a Sequential Bayes Factor (SBF) design on three findings in the developmental literature. In particular, using the framework described by Schönbrödt and colleagues (2017), we examined infants' sensitivity to mispronunciations of familiar words, their learning of novel word‐object associations from cross‐situational learning paradigms, and their assumption of mutual exclusivity in assigning novel labels to novel objects. We tested an initial sample of 20 participants in each study, incrementally increasing sample size by one and computing a Bayes Factor with each additional participant. In one study, we were able to obtain moderate evidence for the alternate hypotheses despite testing less than half the number of participants as in the original study. We did not replicate the findings of the cross‐situational learning study. Indeed, the data were five times more likely under the null hypothesis, allowing us to conclude that infants did not recognize the trained word‐object associations presented in the task. We discuss these findings in light of the advantages and disadvantages of using a SBF design in developmental research while also providing researchers with an account of how we implemented this design across multiple studies. [ABSTRACT FROM AUTHOR]

To test general and specific processes of symbol learning, 4- and 5- year-old children learned three kinds of abstract associates for novel objects: words, facts, and pictograms. To test fast mapping (i.e., one-trial learning) and subsequent learning, comprehension was tested after each of four exposures. Production was also tested, as was children’s tendency to generalize learned items to new objects in the same taxon. To test for a bias toward mutually exclu- sive associations, children learned either one-to-one or many-to- many mappings. In Experiment 1, children learned words, facts (with or without incidental novel words), or pictograms. In Experiment 2, children learned words or pictograms. In both of these experiments, children learned words slower than facts and pictograms. Pictograms and facts were generalized more systematically than words, but only in Experiment 1. Children learned one-to-one mappings faster only in Experiment 2, when cognitive load was increased. In Experiment 3, 3- and 4-year-olds were taught facts (with novel words), words, and pictograms. Children learned facts faster than words; however, they remembered all items equally well a week later. The results suggest that word learning follows non-specialized memory and associative learning processes.

How do learners gather new information during word learning? One possibility is that learners selectively sample items that help them reduce uncertainty about new word meanings. In a series of cross‐situational word learning tasks with adults and children, we manipulated the referential ambiguity of label‐object pairs experienced during training and subsequently investigated which words participants chose to sample additional information about. In the first experiment, adult learners chose to receive additional training on object‐label associations that reduce referential ambiguity during cross‐situational word learning. This ambiguity‐reduction strategy was related to improved test performance. In two subsequent experiments, we found that, at least in some contexts, children (3–8 years of age) show a similar preference to seek information about words experienced in ambiguous word learning situations. In Experiment 2, children did not preferentially select object‐label associations that remained ambiguous during cross‐situational word learning. However, in a third experiment that increased the relative ambiguity of two sets of novel object‐label associations, we found evidence that children preferentially make selections that reduce ambiguity about novel word meanings. These results carry implications for understanding how children actively contribute to their own language development by seeking information that supports learning. [ABSTRACT FROM AUTHOR]

Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.