Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems.

BACKGROUND: The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. RESULTS: BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. CONCLUSIONS: BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

Why surveys are 'very hard': exploring challenges and insights for collection of authentic patient experience information with speakers of Australian First Nations languages.

INTRODUCTION: Health services collect patient experience data to monitor, evaluate and improve services and subsequently health outcomes. Obtaining authentic patient experience information to inform improvements relies on the quality of data collection processes and the responsiveness of these processes to the cultural and linguistic needs of diverse populations. This study explores the challenges and considerations in collecting authentic patient experience information through survey methods with Australians who primarily speak First Nations languages. METHODS: First Nations language experts, interpreters, health staff and researchers with expertise in intercultural communication engaged in an iterative process of critical review of two survey tools using qualitative methods. These included a collaborative process of repeated translation and back translation of survey items and collaborative analysis of video-recorded trial administration of surveys with languages experts (who were also receiving dialysis treatment) and survey administrators. All research activities were audio- or video-recorded, and data from all sources were translated, transcribed and inductively analysed to identify key elements influencing acceptability and relevance of both survey process and items as well as translatability. RESULTS: Serious challenges in achieving equivalence of meaning between English and translated versions of survey items were pervasive. Translatability of original survey items was extensively compromised by the use of metaphors specific to the cultural context within which surveys were developed, English words that are familiar but used with different meaning, English terms with no equivalent in First Nations languages and grammatical discordance between languages. Discordance between survey methods and First Nations cultural protocols and preferences for seeking and sharing information was also important: the lack of opportunity to share the 'full story', discomfort with direct questions and communication protocols that preclude negative or critical responses constrained the authenticity of the information obtained through survey methods. These limitations have serious implications for the quality of information collected and result in frustration and distress for those engaging with the survey. CONCLUSION: Profound implications for the acceptability of a survey tool as well as data quality arise from differences between First Nations cultural and communication contexts and the cultural context within which survey methods have evolved. When data collection processes are not linguistically and culturally congruent there is a risk that patient experience data are inaccurate, miss what is important to First Nations patients and have limited utility for informing relevant healthcare improvement. Engagement of First Nations cultural and language experts is essential in all stages of development, implementation and evaluation of culturally safe and effective approaches to support speakers of First Nations languages to share their experiences of health care and influence change.

Using altered auditory feedback to study pitch compensation and adaptation in tonal language speakers.

Human speech production is strongly influenced by the auditory feedback it generates. Auditory feedback-what we hear when we speak-enables us to learn and maintain speaking skills and to rapidly correct errors in our speech. Over the last three decades, the real-time altered auditory feedback (AAF) paradigm has gained popularity as a tool to study auditory feedback control during speech production. This method involves changing a speaker's speech and feeding it back to them in near real time. More than 50% of the world's population speak tonal languages, in which the pitch or tone used to pronounce a word can change its meaning. This review article aims to offer an overview of the progression of AAF paradigm as a method to study pitch motor control among speakers of tonal languages. Eighteen studies were included in the current mini review and were compared based on their methodologies and results. Overall, findings from these studies provide evidence that tonal language speakers can compensate and adapt when receiving inconsistent and consistent pitch perturbations. Response magnitude and latency are influenced by a range of factors. Moreover, by combining AAF with brain stimulation and neuroimaging techniques, the neural basis of pitch motor control in tonal language speakers has been investigated. To sum up, AAF has been demonstrated to be an emerging tool for studying pitch motor control in speakers of tonal languages.

Dataset for Siswati: Parallel textual data for English and Siswati and monolingual textual data for Siswati.

This data article presents a dataset for Siswati, a Bantu language of the Nguni group that is one of the eleven official South African languages and the official language of Eswatini (together with English). The dataset contains parallel textual data between English and Siswati as well as monolingual data for Siswati and was developed for use as training data for machine translation systems, specifically the Autshumato machine translation project. Both corpora can also be used for development and evaluation of Natural Language Processing (NLP) core technologies for Siswati. In addition, the data lends itself for corpus linguistic studies. The article describes how the data was collected, what type of texts it contains and what clean-up was done. It also provides an overview of the number of words contained in the datasets.

Parallel texts dataset for Uzbek-Kazakh machine translation.

This paper presents a parallel corpus of raw texts between the Uzbek and Kazakh languages as a dataset for machine translation applications, focusing on the data collection process, dataset description, and its potential for reuse. The dataset-building process includes three separate stages, starting with a tiny portion of already available parallel data, then some more compiled from openly available resources like literature books, and web news texts, which were aligned using the sentence alignment method, encompassing a wide range of topics and genres. Finally, the majority of the dataset was taken from a raw text corpus in Uzbek and manually translated into Kazakh by a group of experts who are fluent in both languages. The resulting parallel corpus serves as a valuable resource for researchers and practitioners interested in Kazakh and Uzbek language processing tasks, particularly in the context of neural machine translation, where the presented data can be used for testing the rule-based machine translation models, or it can be used for both training statistical and neural machine translation models as well. The dataset has been made accessible through the widely recognized Hugging Face platform, a repository known for facilitating collaborative efforts and advancing Natural Language Processing (NLP) applications. This combination of methods to obtain a parallel corpus plays as a pivot for other languages among other low-resource Turkic languages.

"How Fluent Do I Need to Be to Say I'm Fluent?" Research Experiences of Communities that Speak Languages Other than English.

OBJECTIVE: The goal of this study was to partner with community organizations to understand the research experiences of communities who speak languages other than English (LOE). METHODS: We conducted semi-structured qualitative interviews in Spanish, Nepali, Mandarin, French, or Kizigua with LOE community members and community leaders who completed recruitment and data collection. Audio-recordings of the interviews were transcribed and translated. We conducted qualitative coding using a mixed deductive-inductive analysis approach and thematic analyses using three rounds of affinity clustering. This study occurred in partnership with an established community-academic collaboration. RESULTS: Thirty community members and six community leaders were interviewed. 83% of LOE participants were born outside of the US and most participants (63%) had never participated in a prior research study. Six themes emerged from this work. Many participants did not understand the concept of research, but those that did thought that inclusion of LOE communities is critical for equity. Even when research was understood as a concept, it was often inaccessible to LOE individuals, particularly because of the lack of language services. When LOE participants engaged in research, they did not always understand their participation. Participants thought that improving research trust was essential and recommended partnering with community organizations and disseminating research results to the community. CONCLUSION: This study's results can serve as an important foundation for researchers seeking to include LOE communities in future research to be more inclusive and scientifically rigorous.

Founder effects identify languages of the earliest Americans.

The known languages of the Americas comprise nearly half of the world's language families and a wide range of structural types, a level of diversity that required considerable time to develop. This paper proposes a model of settlement and expansion designed to integrate current linguistic analysis with other prehistoric research on the earliest episodes in the peopling of the Americas. Diagnostic structural features from phonology and morphology are compared across 60 North American languages chosen for coverage of geography and language families and adequacy of description. Frequency comparison and graphic cluster analysis are applied to assess the fit of linguistic types and families with late Pleistocene time windows when entry from Siberia to North America was possible. The linguistic evidence is consistent with two population strata defined by early coastal entries ~24,000 and ~15,000 years ago, then an inland entry stream beginning ~14,000 ff. and mixed coastal/inland ~12,000 ff. The dominant structural properties among the founder languages are still reflected in the modern linguistic populations. The modern linguistic geography is still shaped by the extent of glaciation during the entry windows. Structural profiles imply that two linguistically distinct and internally diverse ancient Siberian linguistic populations provided the founding American populations. OBJECTIVES: Describe early North American linguistic population structure and chronology; align distribution of structural types with archeological and paleoclimatological evidence on the earliest settlements. Propose an improved model of early settlement and expansion and pose some priority research questions. MATERIALS AND METHODS: Classification of languages based on a tripartite geolinguistic division based on geographical and linguistic evidence. Survey of phonological and morphological patterns of 60 languages representing the structural, geographical, and genealogical diversity of North America. Survey of 16 morphological and phonological features of known or likely high stability and family-identifying value across those languages. Frequency comparison and cluster analysis to elucidate the tripartite analysis and compare to the chronology and geolinguistics implied by paleoclimatological and archeological work. RESULTS: There is enough evidence (linguistic, archeological, genetic, and geological) to indicate four glacial-age openings allowing entries to North America: coastal c. 24,000 and 15,000 years ago; inland c. 14,000 years ago and continuing; and coastal c. 12,000 years ago and continuing. Geographical distribution of modern languages reflects the geography and chronology of the openings and the two human and linguistic population strata they formed, and plausibly also the structural types of the founding languages. DISCUSSION: Improved model of North American settlement (two chronological strata, four entries); comparison to other proposed models. Further questions and research issues for linguistic, genetic, and archeological research.

Embracing different languages and local differences: Co-constructive patient simulation strengthens host countries' clinical training in psychiatry.

BACKGROUND: Global education in psychiatry is heavily influenced by knowledge from Western, high-income countries, which obscures local voices and expertise. AIM: To adapt a human simulation model to psychiatric education in a context that is specific to local languages and cultures. METHODS: We conducted an observational study consisting of six human simulation sessions with standardized patients from two host countries, speaking their native languages, and following an adaptation of the co-constructive patient simulation (CCPS) model. As local faculty became increasingly familiar with the CCPS approach, they took on the role of facilitators-in their country's native language. RESULTS: Fifty-three learners participated: 19 child and adolescent psychiatry trainees and 3 faculty members in Türkiye (as a group that met online during 3 consecutive months); and 24 trainees and 7 faculty in Israel (divided into 3 groups, in parallel in-person sessions during a single training day). Each of the six cases reflected local realities and clinical challenges, and was associated with specific learning goals identified by each case-writing trainee. CONCLUSION: Human simulation has not been fully incorporated into psychiatric education: The creation of immersive clinical experiences and the strengthening of reflective practice are two areas ripe for development. Our adaptations of CCPS can also strengthen local and regional networks and psychiatric communities of practice. Finally, the model can help question and press against hegemonies in psychiatric training that overshadow local expertise.

Mean Length of Utterance: A study of early language development in four Southern Bantu languages.

Mean Length of Utterance (MLU) has been widely used to measure children's early language development in a variety of languages. This study investigates the utility of MLU to measure language development in four agglutinative and morphologically complex Southern Bantu languages. Using a variant of MLU, MLU3, based on the three longest sentences children produced, we analysed the utterances of 448 toddlers (16-32 months) collected using the MacArthur-Bates Communicative Development Inventory, a parent-report tool. MLU3, measured in words (MLU3-w) and morphemes (MLU3-m), significantly correlated with age and other indices of language growth (e.g., grammar and vocabulary). MLU3 measures also accounted for significant variance in language development particular morphosyntactic development. Our results suggest that MLU3-m is a more sensitive measure than MLU3-w. We conclude that MLU measured in morphemes provides a useful addition to other indices of language development in these kinds of morphologically complex languages.

Lexicon dataset for the Hausa language.

This paper presents a comprehensive augmented lexicon sentiment analysis dataset for the Hausa language. The dataset was created by adopting words and phrases from a Hausa Language dictionary and then using the data augmentation method to expand the quantity of the dataset. The researchers manually annotated each phrase/sentence with positive, negative, or neutral polarity. The dataset consists of 14,663 rows, with 4,154 positives, 4,310 negatives, and 6,199 neutrals. The dataset is valuable because it contributes to the available resources for sentiment analysis, especially for Hausa, which is a low-resource language. The dataset will benefit researchers in sentiment analysis who want to develop a model to analyze Hausa posts on social media or product reviews in the Hausa language.

Fine-grained differences in gender-cue strength affect predictive processing in children: Cross-linguistic evidence from Russian and Bulgarian.

We tested predictive gender agreement processing in adjective-noun phrases by 45 4- to 6-year-old Russian- and Bulgarian-speaking children using the visual world eye-tracking paradigm. Russian and Bulgarian are closely related languages that have three genders but differ in the nature and number of gender cues on adjectives. Analysis of the proportion and time course of looks to the target noun showed that only Bulgarian children used gender cues to predict the upcoming noun. We argue that the cross-linguistic difference in the gender cue strength is revealed through the operation of economy, transparency, and interdependence in a gender complexity matrix. The documented advantage for Bulgarian children in gender agreement processing and acquisition underscores the need for a comparative language acquisition approach to typologically close languages.

The perils and promises of fact-checking with large language models.

Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large language models (LLMs) like GPT-4 are increasingly trusted to write academic papers, lawsuits, and news articles and to verify information, emphasizing their role in discerning truth from falsehood and the importance of being able to verify their outputs. Understanding the capacities and limitations of LLMs in fact-checking tasks is therefore essential for ensuring the health of our information ecosystem. Here, we evaluate the use of LLM agents in fact-checking by having them phrase queries, retrieve contextual data, and make decisions. Importantly, in our framework, agents explain their reasoning and cite the relevant sources from the retrieved context. Our results show the enhanced prowess of LLMs when equipped with contextual information. GPT-4 outperforms GPT-3, but accuracy varies based on query language and claim veracity. While LLMs show promise in fact-checking, caution is essential due to inconsistent accuracy. Our investigation calls for further research, fostering a deeper comprehension of when agents succeed and when they fail.

Characterizing language production across modalities.

ABSTRACTThis study investigates factors influencing lexical access in language production across modalities (signed and oral). Data from deaf and hearing signers were reanalyzed (Baus and Costa, 2015, On the temporal dynamics of sign production: An ERP study in Catalan Sign Language (LSC). Brain Research, 1609(1), 40-53. https://doi.org/10.1016/j.brainres.2015.03.013; Gimeno-Martínez and Baus, 2022, Iconicity in sign language production: Task matters. Neuropsychologia, 167, 108166. https://doi.org/10.1016/j.neuropsychologia.2022.108166) testing the influence of psycholinguistic variables and ERP mean amplitudes on signing and naming latencies. Deaf signers' signing latencies were influenced by sign iconicity in the picture signing task, and by spoken psycholinguistic variables in the word-to-sign translation task. Additionally, ERP amplitudes before response influenced signing but not translation latencies. Hearing signers' latencies, both signing and naming, were influenced by sign iconicity and word frequency, with early ERP amplitudes predicting only naming latencies. These findings highlight general and modality-specific determinants of lexical access in language production.

Real and synthetic Punjabi speech datasets for automatic speech recognition.

Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.

YembaTones: A syllable-tone annotated dataset for speech recognition and prosodic analysis of the Yemba language.

Prosody is a key area of linguistics that explores tonal and rhythmic variations in speech. In tonal languages such as Yemba, prosody plays a crucial role in distinguishing between words with different meanings or different grammatical forms. However, despite the large number of native speakers of this language in Cameroon, there are few resources for the speech recognition and synthesis. In this article, we present YembaTones, a syllabic and tonal annotated dataset, created from a dictionary we designed of 344 Yemba/French words coming from the most common phrases of the language, grouped according to their spellings that only differ by the tone. The dataset was originally designed for training and evaluating tone detection models for tonal and low resource languages. The recordings of the pronunciation of these words were made with 11 native speakers of Yemba, mainly specialists in linguistics with a good command of the sounds of the language. The recordings were made with a dictaphone in different places such as the homes of the speakers, the campuses and their workplaces. Then they have been cleaned and segmented into individual audio files corresponding to the pronunciations of isolated words, using the software Audacity. After cleaning and segmentation, we selected 3420 good quality audio files for annotation. Annotations were made at the syllabic and tonal level using Praat software. YembaTones is a valuable resource not only for the training and evaluation of automatic tone detection models but also for automatic speech recognition, speech synthesis of tonal and poorly endowed languages, as well as for the study of prosody and Yemba phonetics, research in speech acoustics and phonetic linguistics.

Text-to-movie authoring of anatomy lessons.

There is a need for a simple yet comprehensive tool to produce and edit pedagogical anatomy video courses, given the widespread usage of multimedia and 3D content in anatomy instruction. Anatomy teachers have minimal control over the present anatomical content generation pipeline. In this research, we provide an authoring tool for instructors that takes text written in the Anatomy Storyboard Language (ASL), a novel domain-specific language (DSL) and produces an animated video. ASL is a formal language that allows users to describe video shots as individual sentences while referencing anatomic structures from a large-scale ontology linked to 3D models. We describe an authoring tool that translates anatomy lessons written in ASL to finite state machines, which are then used to automatically generate 3D animation with the Unity 3D game engine. The proposed text-to-movie authoring tool was evaluated by four anatomy professors to create short lessons on the knee. Preliminary results demonstrate the ease of use and effectiveness of the tool for quickly drafting narrated video lessons in realistic medical anatomy teaching scenarios.

Pashto offensive language detection: a benchmark dataset and monolingual Pashto BERT.

Social media platforms have become inundated with offensive language. This issue must be addressed for the growth of online social networks (OSNs) and a healthy online environment. While significant research has been devoted to identifying toxic content in major languages like English, this remains an open area of research in the low-resource Pashto language. This study aims to develop an AI model for the automatic detection of offensive textual content in Pashto. To achieve this goal, we have developed a benchmark dataset called the Pashto Offensive Language Dataset (POLD), which comprises tweets collected from Twitter and manually classified into two categories: "offensive" and "not offensive". To discriminate these two categories, we investigated the classic deep learning classifiers based on neural networks, including CNNs and RNNs, using static word embeddings: Word2Vec, fastText, and GloVe as features. Furthermore, we examined two transfer learning approaches. In the first approach, we fine-tuned the pre-trained multilingual language model, XLM-R, using the POLD dataset, whereas, in the second approach, we trained a monolingual BERT model for Pashto from scratch using a custom-developed text corpus. Pashto BERT was then fine-tuned similarly to XLM-R. The performance of all the deep learning and transformer learning models was evaluated using the POLD dataset. The experimental results demonstrate that our pre-trained Pashto BERT model outperforms the other models, achieving an F1-score of 94.34% and an accuracy of 94.77%.

Towards a theory of morphosyntactic focus marking.

Based on six detailed case studies of languages in which focus is marked morphosyntactically, we propose a novel formal theory of focus marking, which can capture these as well as the familiar English-type prosodic focus marking. Special attention is paid to the patterns of focus syncretism, that is, when different size and/or location of focus are indistinguishably realized by the same form. The key ingredients to our approach are that complex constituents (not just words) may be directly focally marked, and that the choice of focal marking is governed by blocking.

Does Orkish Sound Evil? Perception of Fantasy Languages and Their Phonetic and Phonological Characteristics.

Constructed languages, frequently invented to support world-building in fantasy and science fiction genres, are often intended to sound similar to the characteristics of the people who speak them. The aims of this study are (1) to investigate whether some fictional languages, such as Orkish whose speakers are portrayed as villainous, are rated more negatively by listeners than, for example, the Elvish languages, even when they are all produced without emotional involvement in the voice; and (2) to investigate whether the rating results can be related to the sound structure of the languages under investigation. An online rating experiment with three 7-point semantic differential scales was conducted, in which three sentences from each of 12 fictional languages (Neo-Orkish, Quenya, Sindarin, Khuzdul, Adûnaic, Klingon, Vulcan, Atlantean, Dothraki, Na'vi, Kesh, ʕuiʕuid) were rated, spoken by a female and a male speaker. The results from 129 participants indicate that Klingon and Dothraki do indeed sound more unpleasant, evil, and aggressive than the Elvish languages Sindarin and Quenya. Furthermore, this difference in rating is predicted by certain characteristics of the sound structure, such as the percentage of non-German sounds and the percentage of voicing. The implications of these results are discussed in relation to theories of language attitude.