Long Noncoding RNAs: Molecular Modalities to Organismal Functions.

We have known for decades that long noncoding RNAs (lncRNAs) can play essential functions across most forms of life. The maintenance of chromosome length requires an lncRNA (e.g., hTERC) and two lncRNAs in the ribosome that are required for protein synthesis. Thus, lncRNAs can represent powerful RNA machines. More recently, it has become clear that mammalian genomes encode thousands more lncRNAs. Thus, we raise the question: Which, if any, of these lncRNAs could also represent RNA-based machines? Here we synthesize studies that are beginning to address this question by investigating fundamental properties of lncRNA genes, revealing new insights into the RNA structure-function relationship, determining cis- and trans-acting lncRNAs in vivo, and generating new developments in high-throughput screening used to identify functional lncRNAs. Overall, these findings provide a context toward understanding the molecular grammar underlying lncRNA biology.

Rapid infant learning of syntactic-semantic links.

In the second year of life, infants begin to rapidly acquire the lexicon of their native language. A key learning mechanism underlying this acceleration is syntactic bootstrapping: the use of hidden cues in grammar to facilitate vocabulary learning. How infants forge the syntactic-semantic links that underlie this mechanism, however, remains speculative. A hurdle for theories is identifying computationally light strategies that have high precision within the complexity of the linguistic signal. Here, we presented 20-mo-old infants with novel grammatical elements in a complex natural language environment and measured their resultant vocabulary expansion. We found that infants can learn and exploit a natural language syntactic-semantic link in less than 30 min. The rapid speed of acquisition of a new syntactic bootstrap indicates that even emergent syntactic-semantic links can accelerate language learning. The results suggest that infants employ a cognitive network of efficient learning strategies to self-supervise language development.

NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, the discovery of motifs and their combinatorial patterns from these CNN models has remained difficult. We show that the main difficulty is due to the problem of multifaceted neurons which respond to multiple types of sequence patterns. Since existing interpretation methods were mainly designed to visualize the class of sequences that can activate the neuron, the resulting visualization will correspond to a mixture of patterns. Such a mixture is usually difficult to interpret without resolving the mixed patterns. We propose the NeuronMotif algorithm to interpret such neurons. Given any convolutional neuron (CN) in the network, NeuronMotif first generates a large sample of sequences capable of activating the CN, which typically consists of a mixture of patterns. Then, the sequences are "demixed" in a layer-wise manner by backward clustering of the feature maps of the involved convolutional layers. NeuronMotif can output the sequence motifs, and the syntax rules governing their combinations are depicted by position weight matrices organized in tree structures. Compared to existing methods, the motifs found by NeuronMotif have more matches to known motifs in the JASPAR database. The higher-order patterns uncovered for deep CNs are supported by the literature and ATAC-seq footprinting. Overall, NeuronMotif enables the deciphering of cis-regulatory codes from deep CNs and enhances the utility of CNN in genome interpretation.

Neural consequences of binaural beat stimulation on auditory sentence comprehension: an EEG study.

A growing literature has shown that binaural beat (BB)-generated by dichotic presentation of slightly mismatched pure tones-improves cognition. We recently found that BB stimulation of either beta (18 Hz) or gamma (40 Hz) frequencies enhanced auditory sentence comprehension. Here, we used electroencephalography (EEG) to characterize neural oscillations pertaining to the enhanced linguistic operations following BB stimulation. Sixty healthy young adults were randomly assigned to one of three listening groups: 18-Hz BB, 40-Hz BB, or pure-tone baseline, all embedded in music. After listening to the sound for 10 min (stimulation phase), participants underwent an auditory sentence comprehension task involving spoken sentences that contained either an object or subject relative clause (task phase). During the stimulation phase, 18-Hz BB yielded increased EEG power in a beta frequency range, while 40-Hz BB did not. During the task phase, only the 18-Hz BB resulted in significantly higher accuracy and faster response times compared with the baseline, especially on syntactically more complex object-relative sentences. The behavioral improvement by 18-Hz BB was accompanied by attenuated beta power difference between object- and subject-relative sentences. Altogether, our findings demonstrate beta oscillations as a neural correlate of improved syntactic operation following BB stimulation.

How Grammar Conveys Meaning: Language-Specific Spatial Encoding Patterns and Cross-Language Commonality in Higher-Order Neural Space.

Languages come in different forms but have shared meanings to convey. Some meanings are expressed by sentence structure and morphologic inflections rather than content words, such as indicating time frame using tense. This fMRI study investigates whether there is cross-language common representation of grammatical meanings that can be identified from neural signatures in the bilingual human brain. Based on the representations in intersentence neural similarity space, identifying grammatical construction of a sentence in one language by models trained on the other language resulted in reliable accuracy. By contrast, cross-language identification of grammatical construction by spatially matched activation patterns was only marginally accurate. Brain locations representing grammatical meaning in the two languages were interleaved in common regions bilaterally. The locations of voxels representing grammatical features in the second language were more varied across individuals than voxels representing the first language. These findings suggest grammatical meaning is represented by language-specific activation patterns, which is different from lexical semantics. Commonality of grammatical meaning is neurally reflected only in the interstimulus similarity space.SIGNIFICANCE STATEMENT Whether human brain encodes sentence-level meanings beyond content words in different languages similarly has been a long-standing question. We characterize the neural representations of similar grammatical meanings in different languages. Using complementary analytic approaches on fMRI data, we show that the same grammatical meaning is neurally represented as the common pattern of neural distances between sentences. The results suggest the possibility of identifying specific grammatical meaning expressed by different morphologic and syntactic implementations of different languages. The neural realization of grammatical meanings is constrained by the specific language being used, but the relationships between the neural representations of sentences are preserved across languages. These findings have some theoretical implications on a distinction between grammar and lexical meanings.

Visual linguistic statistical learning is traceable through neural entrainment.

The human brain can detect statistical regularities in the environment across a wide variety of contexts. The importance of this process is well-established not just in language acquisition but across different modalities; in addition, several neural correlates of statistical learning have been identified. A current technique for tracking the emergence of regularity learning and localizing its neural background is frequency tagging (FT). FT can detect neural entrainment not only to the frequency of stimulus presentation but also to that of a hidden structure. Auditory learning paradigms with linguistic and nonlinguistic stimuli, along with a visual paradigm using nonlinguistic stimuli, have already been tested with FT. To complete the picture, we conducted an FT experiment using written syllables as stimuli and a hidden triplet structure. Both behavioral and neural entrainment data showed evidence of structure learning. In addition, we localized two electrode clusters related to the process, which spread across the frontal and parieto-occipital areas, similar to previous findings. Accordingly, we conclude that fast-paced visual linguistic regularities can be acquired and are traceable through neural entrainment. In comparison with the literature, our findings support the view that statistical learning involves a domain-general network.

Text analysis framework for identifying mutations among non-small cell lung cancer patients from laboratory data.

BACKGROUND: Laboratory data can provide great value to support research aimed at reducing the incidence, prolonging survival and enhancing outcomes of cancer. Data is characterized by the information it carries and the format it holds. Data captured in Alberta's biomarker laboratory repository is free text, cluttered and rouge. Such data format limits its utility and prohibits broader adoption and research development. Text analysis for information extraction of unstructured data can change this and lead to more complete analyses. Previous work on extracting relevant information from free text, unstructured data employed Natural Language Processing (NLP), Machine Learning (ML), rule-based Information Extraction (IE) methods, or a hybrid combination between them. METHODS: In our study, text analysis was performed on Alberta Precision Laboratories data which consisted of 95,854 entries from the Southern Alberta Dataset (SAD) and 6944 entries from the Northern Alberta Dataset (NAD). The data covers all of Alberta and is completely population-based. Our proposed framework is built around rule-based IE methods. It incorporates topics such as Syntax and Lexical analyses to achieve deterministic extraction of data from biomarker laboratory data (i.e., Epidermal Growth Factor Receptor (EGFR) test results). Lexical analysis compromises of data cleaning and pre-processing, Rich Text Format text conversion into readable plain text format, and normalization and tokenization of text. The framework then passes the text into the Syntax analysis stage which includes the rule-based method of extracting relevant data. Rule-based patterns of the test result are identified, and a Context Free Grammar then generates the rules of information extraction. Finally, the results are linked with the Alberta Cancer Registry to support real-world cancer research studies. RESULTS: Of the original 5512 entries in the SAD dataset and 5017 entries in the NAD dataset which were filtered for EGFR, the framework yielded 5129 and 3388 extracted EGFR test results from the SAD and NAD datasets, respectively. An accuracy of 97.5% was achieved on a random sample of 362 tests. CONCLUSIONS: We presented a text analysis framework to extract specific information from unstructured clinical data. Our proposed framework has shown that it can successfully extract relevant information from EGFR test results.

Children extract a new linguistic rule more quickly than adults.

Children achieve better long-term language outcomes than adults. However, it remains unclear whether children actually learn language more quickly than adults during real-time exposure to input-indicative of true superior language learning abilities-or whether this advantage stems from other factors. To examine this issue, we compared the rate at which children (8-10 years) and adults extracted a novel, hidden linguistic rule, in which novel articles probabilistically predicted the animacy of associated nouns (e.g., "gi lion"). Participants categorized these two-word phrases according to a second, explicitly instructed rule over two sessions, separated by an overnight delay. Both children and adults successfully learned the hidden animacy rule through mere exposure to the phrases, showing slower response times and decreased accuracy to occasional phrases that violated the rule. Critically, sensitivity to the hidden rule emerged much more quickly in children than adults; children showed a processing cost for violation trials from very early on in learning, whereas adults did not show reliable sensitivity to the rule until the second session. Children also showed superior generalization of the hidden animacy rule when asked to classify nonword trials (e.g., "gi badupi") according to the hidden animacy rule. Children and adults showed similar retention of the hidden rule over the delay period. These results provide insight into the nature of the critical period for language, suggesting that children have a true advantage over adults in the rate of implicit language learning. Relative to adults, children more rapidly extract hidden linguistic structures during real-time language exposure. RESEARCH HIGHLIGHTS: Children and adults both succeeded in implicitly learning a novel, uninstructed linguistic rule, based solely on exposure to input. Children learned the novel linguistic rules much more quickly than adults. Children showed better generalization performance than adults when asked to apply the novel rule to nonsense words without semantic content. Results provide insight into the nature of critical period effects in language, indicating that children have an advantage over adults in real-time language learning.

Visual artificial grammar learning across 1 year in 7-year-olds and adults.

Acquiring sequential information is of utmost importance, for example, for language acquisition in children. Yet, the long-term storage of statistical learning in children is poorly understood. To address this question, 27 7-year-olds and 28 young adults completed four sessions of visual sequence learning (Year 1). From this sample, 16 7-year-olds and 20 young adults participated in another four equivalent sessions after a 12-month-delay (Year 2). The first three sessions of each year used Stimulus Set 1, and the last session used Stimulus Set 2 to investigate transfer effects. Each session consisted of alternating learning and test phases in a modified artificial grammar learning task. In Year 1, 7-year-olds and adults learned the regularities and showed transfer to Stimulus Set 2. Both groups retained their final performance level over the 1-year period. In Year 2, children and adults continued to improve with Stimulus Set 1 but did not show additional transfer gains. Adults overall outperformed children, but transfer effects were indistinguishable between both groups. The current results suggest that long-term memory traces are formed from repeated sequence learning that can be used to generalize sequence rules to new visual input. However, the current study did not provide evidence for a childhood advantage in learning and remembering sequence rules.

Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar.

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn.

Logical interdependencies in infrastructure: What are they, how to identify them, and what do they mean for infrastructure risk analysis?

A useful theoretical lens that has emerged for understanding urban resilience is the four basic types of interdependencies in critical infrastructures: the physical, geographic, cyber, and logical types. This paper is motivated by a conceptual and methodological limitation-although logical interdependencies (where two infrastructures affect the state of each other via human decisions) are regarded as one of the basic types of interdependencies, the question of how to apply the notion and how to quantify logical relations remains under-explored. To overcome this limitation, this study focuses on institutions (rules), for example, rules and planned tasks guiding human interactions with one another and infrastructure. Such rule-mediated interactions, when linguistically expressed, have a syntactic form that can be translated into a network form. We provide a foundation to delineate these two forms to detect logical interdependence. Specifically, we propose an approach to quantify logical interdependence based on the idea that (1) there are certain network motifs indicating logical relations, (2) such network motifs can be discerned from the network form of rules, and that (3) the higher the frequency of these motifs between two infrastructures, the greater the extent of logical interdependency. We develop a set of such motifs and illustrate their usage using an example. We conclude by suggesting a revision to the original definition of logical interdependence. This rule-focused approach is relevant to understanding human error in risk analysis of socio-technical systems, as human error can be seen as deviations from constraints that lead to accidents.

Training morphosyntactic skills in Dutch preschoolers with (presumed) Developmental Language Disorder: A novel group-based intervention.

BACKGROUND: Morphosyntactic problems are a core symptom of Developmental Language Disorder (DLD). In the Netherlands, children with (presumed) DLD can receive special care in language-focused treatment groups. The focus of these groups mainly lies in improving communicative intentions, vocabulary and phonology. Morphosyntactic skills receive less attention. AIMS: The authors developed a scripted group-based intervention targeting morphosyntactic skills in young children with DLD. In this study, the effect of the intervention on the morphosyntactic skills of preschoolers with (presumed) DLD was tested and the usability for pedagogical practitioners (PPs) was evaluated. METHODS & PROCEDURES: Twenty-seven preschoolers with DLD (aged 2;10-3;10 [years;months]) participated in an A-B group study in which the development of grammatical structures was monitored with a morphosyntactic task and language sample analyses (LSA). Progression during 8 weeks usual care (UC) and 8 weeks morphosyntactic intervention was examined using Bayesian mixed effects models. In LSA, structures that were targeted were compared to control structures. The intervention consisted of a weekly script-based group session in which morphosyntactic structures were targeted, and daily activities in which these target structures were repeated. The intervention was provided by trained PPs, who were coached by a speech-language therapist. An early indication of usability and feasibility was evaluated using an online questionnaire. OUTCOMES & RESULTS: The analyses show that morphosyntactic skills improved during the intervention period, with strong evidence for growth in the production of target structures on the morphosyntactic task and target and control structures in LSA, while barely any evidence was found for growth in the use of these structures in UC. However, target structures and control structures seem to develop at the same rate. General measures of morphosyntactic ability showed improvement both during UC and the intervention phase. Evaluation among practitioners suggested that the intervention is regarded as usable and feasible. CONCLUSIONS & IMPLICATIONS: Growth in morphosyntactic skills of children in the intervention period was demonstrated, but this could not be proven to be related to the intervention because both target and control structures improved during the intervention. This growth might be due to maturation instead of the intervention. Nevertheless, our study demonstrates that the morphosyntactic skills of preschoolers with DLD can show considerable improvement over a period of 8 weeks. Furthermore, our study underlines the importance of using LSA measures when monitoring the morphosyntactic development of children, as they might be more sensitive to change than standardised tests. WHAT THIS PAPER ADDS: What is already known on the subject Most children with Developmental Language Disorder (DLD) experience difficulties in their morphosyntactic development. In the Netherlands, preschoolers with DLD can receive special care in language-focused treatment groups. These groups mainly focus on stimulating communicative intentions, vocabulary and phonology, but less so on stimulating morphosyntactic skills. What this study adds We designed a new group-based intervention targeting the expressive morphosyntactic skills of children with DLD, examined the effect and investigated the usability and feasibility. Although there is strong evidence for growth in morphological skills, intervention effects could not be demonstrated. Evaluations among practitioners suggested that the intervention is regarded as usable and feasible. Furthermore, this study shows that children's morphosyntactic skills can improve over relatively short periods of time and language sample analyses seem to be sensitive to detect these changes. What are the clinical implications of this work? Currently, very few group-based morphosyntactic interventions exist (in the Netherlands). Since practitioners were generally positive about the intervention and its usability and feasibility, the proposed intervention might benefit the treatment of morphosyntactic problems in children with DLD.

Caste, mistrust and municipal inaction: The interwoven barriers for the integration of waste pickers in India.

Solid waste management in low- and middle-income countries like India faces significant challenges due to the increasing waste generation that surpasses the current capacity. Therefore, the informal waste sector (IWS) is more vital than ever in handling consumer waste alongside municipal solid waste management (SWM) systems. However, the integration of the IWS into formal waste management systems remains unresolved due to adverse social and economic conditions. This study focuses on identifying the root causes that hinder the integration of the IWS in India's waste management system, using the city of Chennai as a case study. Adopting an institutional perspective, we analyse the institutional landscape of the waste management system, considering both formal rules (in policy documents) and informal rules (i.e., social norms and routines). The institutional network analysis reveals a significant misalignment in perceptions among governance levels concerning the integration of the IWS. The study shows a considerable gap between rules-in-form and rules-in-use, leading to 1) Preclusion of waste pickers in collecting door-to-door source-segregated waste (i.e., recyclables). 2) Unfair pricing in transactions with small aggregators. 3) Lack of ID cards for waste pickers. These barriers are ultimately rooted in caste discrimination, misalignment between governance levels, and the exclusion of waste pickers in the policymaking process. In conclusion, understanding and rectifying the institutional gaps and discriminatory practices are essential steps towards effectively integrating the IWS in India's waste management system, promoting a more inclusive and sustainable approach to waste management.

Overuse of familiar phrases by individuals with Williams syndrome masks differences in language processing.

We investigated whether individuals with Williams Syndrome (WS) produce language with a bias towards statistical properties of word combinations rather than grammatical rules, resulting in an overuse of holistically stored, familiar phrases. We analysed continuous speech samples from English children with WS (n = 12), typically developing (TD) controls matched on chronological age (n = 15) and TD controls matched on language age (n = 14). Alongside word count, utterance length, grammatical complexity, and morphosyntactic errors, we measured familiarity of expressions by computing collocation strength of each word combination. The WS group produced stronger collocations than both control groups. Moreover, the WS group produced fewer complex sentences, shorter utterances, and more frequent function words than chronological-age matched controls. Language in WS may appear more typical than it is because familiar, holistically processed expressions mask grammatical and other difficulties.

To what extent do children's expressions of time actually refer to time? An investigation into the temporal and discursive usages of temporal adverbs in family interaction.

Many studies have explored children's acquisition of temporal adverbs. However, the extent to which children's early temporal language has discursive instead of solely temporal meanings has been largely ignored. We report two corpus-based studies that investigated temporal adverbs in Finnish child-parent interaction between the children's ages of 1;7 and 4;11. Study 1 shows that the two corpus children used temporal adverbs to construe both temporal and discursive meanings from their early adverb production and that the children's usage syntactically broadly reflected the input received. Study 2 shows that the discursive uses of adverbs appeared to be learned from contextually anchored caregiver constructions that convey discourse functions like urging and reassuring, and that the usage is related to the children's and caregivers' interactional roles. Our study adds to the literature on the acquisition of temporal adverbs by demonstrating that these items are learned also with additional discursive meanings in family interaction.

ENIGMA: A Web Application for Running Online Artificial Grammar Learning Experiments.

Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA ( https://enigma-lang.org ) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners' performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331-361, 2020) to illustrate possible experimental designs and examine the quality of the collected data.

Analyzing the Influence of Ambiguity Tolerance on Grammar Acquisition in EFL Learners Across Face-to-Face, Blended, and Flipped Learning Environments.

In the realm of language education, the influence of learners' personality traits on their educational outcomes within novel instructional frameworks has gained prominence, prompting an exploration into the effects of ambiguity tolerance on grammar acquisition among English as a Foreign Language (EFL) students. This study investigates the impact of learners' personality traits on their learning outcomes in innovative instructional models, such as flipped and blended classes. A sample of 120 EFL students was divided into four comparative groups and two control groups based on their proficiency and ambiguity tolerance (AT) scores. The study utilized a Learning Management System (LMS) to deliver instruction to the different groups. The blended group received a combination of online and face-to-face instruction, while the flipped group received online instruction using the flipped approach. The control group received only face-to-face instruction. After a semester of instruction, a posttest on grammar learning was administered. The findings showed that the blended group performed better than the flipped and face-to-face groups in terms of grammar learning. The study also found no significant differences in grammar learning between high AT and low AT participants in the flipped and blended classes. However, high AT students in the face-to-face class demonstrated higher levels of success in grammar learning compared to low AT students.

Exploring grammatical development in children aged 2;6 to 7: a novel approach using elicited production.

The assessment of children's grammatical skills is a crucial component of diagnosing language disorders. Elicited production is a commonly used method for obtaining data on a child's productive language abilities. We introduce a new instrument developed as part of the third edition of a standardised test battery for German. This instrument utilises elicited production, wherein participants describe coloured pictures depicting everyday situations, in order to generate four test scores: mean length of utterances, completeness of utterances, and two grammar scores comprising relevant target structures. The construction of the grammar scores was inspired by the Index of Productive Syntax (IPSyn), modified for German and computerised. The detailed results provide a comprehensive profile of a child's syntactic and morphological strengths and weaknesses. Analysis of data collected from 348 monolingual German children who formed part of the norming sample, aged between 2;6 and 6;11 years of age, revealed age-related changes in these scores. Additionally, the age range was determined for both grammatical milestones and 'red flags', which may indicate potential problems in language development. In conclusion, the newly developed, time-efficient instrument allows for a detailed assessment of grammatical skills, identification of potential intervention targets, and facilitates various research objectives.

The relationship between vocabulary and grammar in two children with 5p deletion syndrome.

5p deletion syndrome is a rare genetic condition associated with severe speech and language problems. In general, research on speech and language skills is scarce, but there is more knowledge on phonetic and phonological skills than on lexical and grammatical skills. And till now no studies have addressed the relationship between grammar and vocabulary. Therefore, in this study, we address aspects of this relation based on longitudinal parent-reported data (MacArthur-Bates Communicative Development Inventories) from two children with this syndrome aged 2;0-7;3, and 1;11-7;1, respectively. We examine the development of the vocabulary size in each child, seen in relation to the development of grammar (inflections, combinations of words, complexity, and productivity), and see to what extent they can be compared to typically developing children. Results show that they follow a similar pattern to typically developing children but are delayed and have slightly different individual profiles.

Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming.

Genetic variation operators in grammar-guided genetic programming are fundamental to guide the evolutionary process in search and optimization problems. However, they show some limitations, mainly derived from an unbalanced exploration and local-search trade-off. This article presents an estimation of distribution algorithm for grammar-guided genetic programming to overcome this difficulty and thus increase the performance of the evolutionary algorithm. Our proposal employs an extended dynamic stochastic context-free grammar to encode and calculate the estimation of the distribution of the search space from some promising individuals in the population. Unlike traditional estimation of distribution algorithms, the proposed approach improves exploratory behavior by smoothing the estimated distribution model. Therefore, this algorithm is referred to as SEDA, smoothed estimation of distribution algorithm. Experiments have been conducted to compare overall performance using a typical genetic programming crossover operator, an incremental estimation of distribution algorithm, and the proposed approach after tuning their hyperparameters. These experiments involve challenging problems to test the local search and exploration features of the three evolutionary systems. The results show that grammar-guided genetic programming with SEDA achieves the most accurate solutions with an intermediate convergence speed.