Int J Environ Res Public Health. 2022 Aug 4;19(15):9611. doi: 10.3390/ijerph19159611.

ABSTRACT

Exposure to green spaces contributes to residents' physical and mental health and well-being. The equitable allocation of green space has also become an increasingly important issue for society and the government. This study takes 3281 communities in Shenzhen as the analysis units. Using web crawlers, semantic segmentation based on deep learning, web map path planning and entropy weighting methods, four types of residents' daily green exposure indicators are calculated, including community green space ratio, green view index (GVI), park accessibility, and the weighted composite green exposure index. The results reveal inequalities in the level of green exposure in Shenzhen's communities across economic classes, mainly in GVI and comprehensive green exposure. We also found that the level of composite green exposure is relatively stable; however, green space ratio attainment levels for newer communities are increasing and GVI and park accessibility attainment levels are decreasing. Finally, among the newly built communities: compared to the low-income level communities, the high-income level communities have a significant advantage in green space, but the mid-income level communities do not have such an advantage. The main findings of this study can provide policy implications for urban green space planning, including the need to prioritize the addition of public green space near older communities with poor levels of green exposure, the addition of street greenery near communities with poor levels of composite green exposure, and ensuring that parks have entrances in all four directions as far as possible.

PMID:35954967 | DOI:10.3390/ijerph19159611

Brain Lang. 2022 Oct;233:105164. doi: 10.1016/j.bandl.2022.105164. Epub 2022 Aug 4.

ABSTRACT

People with aphasia often show partial impairments on a given task. This trial-to-trial variability offers a potential window into understanding how damaged language networks function. We test the hypothesis that successful word reading in participants with phonological system damage reflects semantic system recruitment. Residual semantic and phonological networks were defined with fMRI in 21 stroke participants with phonological damage using semantic- and rhyme-matching tasks. Participants performed an oral word reading task, and activation was compared between correct and incorrect trials within the semantic and phonological networks. The results showed a significant interaction between hemisphere, network activation, and reading success. Activation in the left hemisphere semantic network was higher when participants successfully read words. Residual phonological regions showed no difference in activation between correct and incorrect trials on the word reading task. The results provide evidence that semantic processing supports successful phonological retrieval in participants with phonological impairment.

PMID:35933744 | DOI:10.1016/j.bandl.2022.105164

J Med Internet Res. 2022 Aug 5;24(8):e39888. doi: 10.2196/39888.

ABSTRACT

BACKGROUND: Understanding how individuals think about a topic, known as the mental model, can significantly improve communication, especially in the medical domain where emotions and implications are high. Neurodevelopmental disorders (NDDs) represent a group of diagnoses, affecting up to 18% of the global population, involving differences in the development of cognitive or social functions. In this study, we focus on 2 NDDs, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), which involve multiple symptoms and interventions requiring interactions between 2 important stakeholders: parents and health professionals. There is a gap in our understanding of differences between mental models for each stakeholder, making communication between stakeholders more difficult than it could be.

OBJECTIVE: We aim to build knowledge graphs (KGs) from web-based information relevant to each stakeholder as proxies of mental models. These KGs will accelerate the identification of shared and divergent concerns between stakeholders. The developed KGs can help improve knowledge mobilization, communication, and care for individuals with ADHD and ASD.

METHODS: We created 2 data sets by collecting the posts from web-based forums and PubMed abstracts related to ADHD and ASD. We utilized the Unified Medical Language System (UMLS) to detect biomedical concepts and applied Positive Pointwise Mutual Information followed by truncated Singular Value Decomposition to obtain corpus-based concept embeddings for each data set. Each data set is represented as a KG using a property graph model. Semantic relatedness between concepts is calculated to rank the relation strength of concepts and stored in the KG as relation weights. UMLS disorder-relevant semantic types are used to provide additional categorical information about each concept's domain.

RESULTS: The developed KGs contain concepts from both data sets, with node sizes representing the co-occurrence frequency of concepts and edge sizes representing relevance between concepts. ADHD- and ASD-related concepts from different semantic types shows diverse areas of concerns and complex needs of the conditions. KG identifies converging and diverging concepts between health professionals literature (PubMed) and parental concerns (web-based forums), which may correspond to the differences between mental models for each stakeholder.

CONCLUSIONS: We show for the first time that generating KGs from web-based data can capture the complex needs of families dealing with ADHD or ASD. Moreover, we showed points of convergence between families and health professionals' KGs. Natural language processing-based KG provides access to a large sample size, which is often a limiting factor for traditional in-person mental model mapping. Our work offers a high throughput access to mental model maps, which could be used for further in-person validation, knowledge mobilization projects, and basis for communication about potential blind spots from stakeholders in interactions about NDDs. Future research will be needed to identify how concepts could interact together differently for each stakeholder.

PMID:35930346 | DOI:10.2196/39888

Obstet Gynecol. 2022 Sep 1;140(3):450-452. doi: 10.1097/AOG.0000000000004902. Epub 2022 Aug 3.

ABSTRACT

Most studies of severe maternal morbidity (SMM) include only cases that occur during birth hospitalizations. We examined the increase in cases when including SMM during antenatal and postpartum (within 42 days of discharge) hospitalizations, using longitudinally linked data from 1,010,250 births in California from September 1, 2016, to December 31, 2018. For total SMM, expanding the definition resulted in 22.8% more cases; for nontransfusion SMM, 45.1% more cases were added. Sepsis accounted for 55.5% of the additional cases. The increase varied for specific indicators, for example, less than 2% for amniotic fluid embolism, 7.0% for transfusion, 112.9% for sepsis, and 155.6% for acute myocardial infarction. These findings reiterate the importance of considering SMM beyond just the birth hospitalization and facilitating access to longitudinally linked data to facilitate a more complete understanding of SMM.

PMID:35926198 | DOI:10.1097/AOG.0000000000004902

Brain Struct Funct. 2022 Sep;227(7):2513-2527. doi: 10.1007/s00429-022-02544-4. Epub 2022 Aug 4.

ABSTRACT

Language function in the brain, once thought to be highly localized, is now appreciated as relying on a connected but distributed network. The semantic system is of particular interest in the language domain because of its hypothesized integration of information across multiple cortical regions. Previous work in healthy individuals has focused on group-level functional connectivity (FC) analyses of the semantic system, which may obscure interindividual differences driving variance in performance. These studies also overlook the contributions of white matter networks to semantic function. Here, we identified semantic network nodes at the individual level with a semantic decision fMRI task in 53 typically aging adults, characterized network organization using structural connectivity (SC), and quantified the segregation and integration of the network using FC. Hub regions were identified in left inferior frontal gyrus. The individualized semantic network was composed of three interacting modules: (1) default-mode module characterized by bilateral medial prefrontal and posterior cingulate regions and also including right-hemisphere homotopes of language regions; (2) left frontal module extending dorsally from inferior frontal gyrus to pre-motor area; and (3) left temporoparietal module extending from temporal pole to inferior parietal lobule. FC within Module3 and integration of the entire network related to a semantic verbal fluency task, but not a matched phonological task. These results support and extend the tri-network semantic model (Xu in Front Psychol 8: 1538 1538, 2017) and the controlled semantic cognition model (Chiou in Cortex 103: 100 116, 2018) of semantic function.

PMID:35925418 | DOI:10.1007/s00429-022-02544-4

J Intell Inf Syst. 2022 Jul 29:1-26. doi: 10.1007/s10844-022-00731-7. Online ahead of print.

ABSTRACT

Complex system development and maintenance face the challenge of dealing with different types of models due to language affordances, preferences, sizes, and so forth that involve interaction between users with different levels of proficiency. Current conceptual data modelling tools do not fully support these modes of working. It requires that the interaction between multiple models in multiple languages is clearly specified to ensure they keep their intended semantics, which is lacking in extant tools. The key objective is to devise a mechanism to support semantic interoperability in hybrid tools for multi-modal modelling in a plurality of paradigms, all within one system. We propose FaCIL, a framework for such hybrid modelling tools. We design and realise the framework FaCIL, which maps UML, ER and ORM2 into a common metamodel with rules that provide the central point for management among the models and that links to the formalisation and logic-based automated reasoning. FaCIL supports the ability to represent models in different formats while preserving their semantics, and several editing workflows are supported within the framework. It has a clear separation of concerns for typical conceptual modelling activities in an interoperable and extensible way. FaCIL structures and facilitates the interaction between visual and textual conceptual models, their formal specifications, and abstractions as well as tracking and propagating updates across all the representations. FaCIL is compared against the requirements, implemented in crowd 2.0, and assessed with a use case. The proof-of-concept implementation in the web-based modelling tool crowd 2.0 demonstrates its viability. The framework also meets the requirements and fully supports the use case.

PMID:35919102 | PMC:PMC9334976 | DOI:10.1007/s10844-022-00731-7

Zh Nevrol Psikhiatr Im S S Korsakova. 2022;122(7. Vyp. 2):78-83. doi: 10.17116/jnevro202212207278.

ABSTRACT

OBJECTIVE: To study the needs of patients suffering from multiple sclerosis (MS) in Russia.

MATERIAL AND METHODS: The technologies of Big Data analysis and intelligent processing of unstructured information (semantic analysis of natural language texts), developed by Semantic Hub were used. Semantic Hub platform scans digital environment to connect to the sources of interest and to collect data of potential interest (i.e. texts generated by patients and their caregivers, in anonymized form). As the next step, each text is analyzed using natural language understanding technologies to build the knowledge base with aggregated data.

RESULTS: The semantic analysis of natural language texts made it possible to describe virtual population of Russian patients with MS and their caregivers on the Web: age, gender, regions of residence, movements, key Web resources for getting information and communicating with each other, insights about medical care and the quality of life of patients with MS.

CONCLUSIONS: In addition to doctors' recommendations, today the patient can get information from various sources, including other patients with MS. This trend requires attention of medical community: it is necessary to help patients get reliable information about the disease, and methods of therapy. Doctor-to-patient communication on the Web should be widely discussed to develop effective and ethical approaches.

PMID:35912561 | DOI:10.17116/jnevro202212207278

Australas Emerg Care. 2023 Mar;26(1):13-23. doi: 10.1016/j.auec.2022.07.002. Epub 2022 Jul 29.

ABSTRACT

INTRODUCTION: Acute appendicitis is the most common cause of acute abdominal pain presentations to the ED and common air ambulance transfer.

AIMS: describe how linked data can be used to explore patients' journeys, referral pathways and request-to-activation responsiveness of patients' appendectomy outcomes (minor vs major complexity).

METHODS: Data sources were linked: aeromedical, hospital and death. Request-to-activation intervals showed strong right-tailed skewness. Quantile regression examined whether the longest request-to-activation intervals were associated with appendicitis complexity in patients who underwent an appendectomy.

RESULTS: There were 684 patients in three referral pathways based on hospital capability levels. In total, 5.6 % patients were discharged from ED. 83.3 % of all rural origins entered via the ED. 3.8 % of appendicitis patients were triaged to tertiary hospitals. Appendectomy patients with major complexity outcomes were less likely to have longer request-to-activation wait times & had longer lengths of stay than patients with minor complexity outcomes.

CONCLUSIONS: Linked data highlighted four aspects of a functioning referral system: appendectomy outcomes of major complexity were less likely to have longer request-to-activation intervals compared to minor (sicker patients were identified); few were discharged from EDs (validated transfer); few were triaged to tertiary hospitals (appropriate level for need), and no deaths relating to appendectomy.

PMID:35909043 | DOI:10.1016/j.auec.2022.07.002

Sensors (Basel). 2022 Jul 23;22(15):5509. doi: 10.3390/s22155509.

ABSTRACT

Humor is a special human expression style, an important "lubricant" for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular research domains; "discourse understanding" is also an important research direction, and how to make computers recognize and understand humorous expressions similar to humans has become one of the popular research domains for natural language processing researchers. In this paper, a humor recognition model (MLSN) based on current humor theory and popular deep learning techniques is proposed for the humor recognition task. The model automatically identifies whether a sentence contains humor expression by capturing the inconsistency, phonetic features, and ambiguity of a joke as semantic features. The model was experimented on three publicly available wisecrack datasets and compared with state-of-the-art language models, and the results demonstrate that the proposed model has better humor recognition accuracy and can contribute to the research on discourse understanding.

PMID:35898012 | DOI:10.3390/s22155509

J Biomed Semantics. 2022 Jul 26;13(1):21. doi: 10.1186/s13326-022-00274-4.

ABSTRACT

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.

PMID:35883181 | DOI:10.1186/s13326-022-00274-4

J Biomed Inform. 2022 Sep;133:104144. doi: 10.1016/j.jbi.2022.104144. Epub 2022 Jul 22.

ABSTRACT

Medical named entity recognition (MNER) is a fundamental component of understanding the unstructured medical texts in electronic health records, and it has received widespread attention in both academia and industry. However, the previous approaches of MNER do not make full use of hierarchical semantics from morphology to syntactic relationships like word dependency. Furthermore, extracting entities from Chinese medical texts is a more complex task because it usually contains for example homophones or pictophonetic characters. In this paper, we propose a multi-level semantic fusion network for Chinese medical named entity recognition, which fuses semantic information on morphology, character, word and syntactic level. We take radical as morphology semantic, pinyin and character dictionary as character semantic, word dictionary as word semantic, and these semantic features are fused by BiLSTM to get the contextualized representation. Then we use a graph neural network to model word dependency as syntactic semantic to enhance the contextualized representation. The experimental results show the effectiveness of the proposed model on two public datasets and robustness in real-world scenarios.

PMID:35878823 | DOI:10.1016/j.jbi.2022.104144

Curr Oncol. 2022 Jun 27;29(7):4541-4557. doi: 10.3390/curroncol29070359.

ABSTRACT

BACKGROUND: The burden of out-of-pocket costs among cancer patients/survivors in Canada is not well understood. The objective of this study was to examine the health-related out-of-pocket cost burden experienced by households with a cancer patient/survivor compared to those without, examine the components of health-related costs and determine who experiences a greater burden.

DATA AND METHODS: This study used a data linkage between the Survey of Household Spending and the Canadian Cancer Registry to identify households with a cancer patient/survivor (cases) and those without (controls). The out-of-pocket burden (out-of-pocket costs measured relative to household income) and mean costs were described and regression analyses examined the characteristics associated with the household out-of-pocket burden and annual out-of-pocket costs.

RESULTS: The health-related out-of-pocket cost burden and annual costs measured in households with a cancer patient/survivor were 3.08% (95% CI: 2.55-3.62%) and CAD 1600 (95% CI: 1456-1759), respectively, compared to a burden of 2.84% (95% CI: 2.31-3.38) and annual costs of CAD 1511 (95% CI: 1377-1659) measured in control households, respectively. Households with a colorectal cancer patient/survivor had a significantly higher out-of-pocket burden compared to controls (mean difference: 1.0%, 95% CI: 0.18, 0.46). Among both cases and controls, the lowest income quintile households experienced the highest health-related out-of-pocket cost burden.

INTERPRETATION: Within a universal health care system, it is still relevant to monitor health-related out-of-pocket spending that is not covered by existing insurance mechanisms; however, this is not routinely assessed in Canada. We demonstrate the feasibility of measuring such costs in households with a cancer patient/survivor using routinely collected data. While the burden and annual health-related out-of-pocket costs of households with a cancer patient/survivor were not significantly higher than control households in this study, the routine measurement of out-of-pocket costs in Canada could be systemized, providing a novel, system-level, equity-informed performance indicator, which is relevant for monitoring inequities in the burden of out-of-pocket costs.

PMID:35877219 | PMC:PMC9322389 | DOI:10.3390/curroncol29070359

Methods Inf Med. 2022 Jul 20. doi: 10.1055/a-1905-5639. Online ahead of print.

ABSTRACT

BACKGROUND: In recent years, researchers have used many computerized interventions to reduce medical errors, the third cause of death in developed countries. One of such interventions is using differential diagnosis generators in primary care, where physicians may encounter initial symptoms without any diagnostic presuppositions. These systems generate multiple diagnoses, ranked by their likelihood. As such, these reports' accuracy can be determined by the location of the correct diagnosis in the list.

OBJECTIVE: This study aimed to design and evaluate a novel practical web-based differential diagnosis generator solution in primary care.

METHODS: In this research, a new online clinical decision support system, called DxGenerator, was designed to improve diagnostic accuracy; to this end, an attempt was made to converge a semantic database with the unified medical language system (UMLS) knowledge base, using MetaMap tool and natural language processing (NLP). In this regard, 120 diseases of gastrointestinal organs causing abdominal pain were modeled into the database. After designing an inference engine and a pseudo-free-text interactive interface, 172 patient vignettes were inputted into DxGenerator and ISABEL, the most accurate similar system. The Wilcoxon signed ranked test was used to compare the position of correct diagnoses in DxGenerator and ISABEL. The alpha level was defined as 0.05.

RESULTS: On a total of 172 vignettes, the mean and standard deviation of correct diagnosis positions improved from 4.2±5.3 in ISABEL to 3.2±3.9 in DxGenerator. This improvement was significant in the subgroup of uncommon diseases (P-value < 0.05).

CONCLUSION: Using UMLS knowledge base and MetaMap Tools can improve the accuracy of diagnostic systems in which terms are entered in a free text manner. Applying these new methods will help the medical community accept medical diagnostic systems better.

PMID:35858654 | DOI:10.1055/a-1905-5639

Database (Oxford). 2022 Jul 15;2022:baac059. doi: 10.1093/database/baac059.

ABSTRACT

The use of probiotics to improve health via the modulation of gut microbiota has gained wide attention. The growing volume of investigations of probiotic microorganisms and commercialized probiotic products has created the need for a database to organize the health-promoting functions driven by probiotics reported in academic articles, clinical trials and patents. We constructed ProBioQuest to collect up-to-date literature related to probiotics from PubMed.gov, ClinicalTrials.gov and PatentsView. More than 2.8 million articles have been collected. Automated information technology-assisted procedures enabled us to collect the data continuously, providing the most up-to-date information. Statistical functions and semantic analyses are provided on the website as an advanced search engine, which contributes to the semantic tool of this database for information search and analyses. The semantic analytical output provides categorized search results and functions to enhance further analysis. A keyword bank is included which can display multiple tables of contents. Users can select keywords from different displayed categories to achieve easily filtered searches. Additional information on the searched items can be browsed via the link-out function. ProBioQuest is not only useful to scientists and health professionals but also to dietary supplement manufacturers and the general public. In this paper, the method we used to build this database-web system is described. Applications of ProBioQuest for several literature-based analyses of probiotics are included as examples of the various uses of this search engine. ProBioQuest can be accessed free of charge at http://kwanlab.bio.cuhk.edu.hk/PBQ/. Database URL: http://kwanlab.bio.cuhk.edu.hk/PBQ/.

PMID:35849028 | DOI:10.1093/database/baac059

J Commun Disord. 2022 Sep-Oct;99:106247. doi: 10.1016/j.jcomdis.2022.106247. Epub 2022 Jul 8.

ABSTRACT

PURPOSE: Kenett et al. (2013) report that the sematic networks, measured by using an oral semantic fluency task, of children with cochlear implants (CI) are less structured compared to the sematic networks of children with typical hearing (TH). This study aims to evaluate if such differences are only evident if children with CI are compared to children with TH matched on chronological age, or also if they are compared to children with TH matched on hearing age.

METHOD: The performance of a group of children with CI on a verbal fluency task was compared to the performance of a group of chronological-age matched children with TH. Subsequently, computational network analysis was used to compare the semantic network structure of the groups. The same procedure was applied to compare a group of children with CI to a group of hearing-age matched children with TH.

RESULTS: The children with CI perform on the same level on an oral semantic verbal fluency task as the children with TH matched on hearing age. There are significant differences in terms of the structure of the semantic network between the groups. The magnitude of these differences is very small and they are non-significant for a proportion of nodes included in the bootstrap analysis. This indicates that there is no true difference between the networks. Hearing age, but not age at implantation was found to be significantly positively correlated with semantic verbal fluency performance for the children with CI.

CONCLUSIONS: The results from the current study indicate that length of exposure to the tested language is an important factor for the structure of the semantic network and the performance on a semantic verbal fluency task for children with CI. Further studies are needed to explore the role of the accessibility of the language input for the development of semantic networks of children with CI.

PMID:35843069 | DOI:10.1016/j.jcomdis.2022.106247

J Acad Consult Liaison Psychiatry. 2022 Jul 12:S2667-2960(22)00294-4. doi: 10.1016/j.jaclp.2022.07.002. Online ahead of print.

ABSTRACT

INTRODUCTION: Since Kahlbaum's classic 19th-century description of catatonia our conceptualization of this syndrome, as well treatment options for it, have advanced considerably. However, little is known about the current state of the catatonia literature since a comprehensive bibliometric analysis of it has not yet been undertaken.

OBJECTIVE: The purpose of this study was to conduct a bibliometric analysis, along with a content analysis of articles reporting new findings, to better understand the catatonia literature and how catatonia research is changing.

METHODS: Using the search term "Title(catatoni*)" in Web of Science (WoS) Core Collection for all available years (1965-2020), all available publications (articles, proceeding papers, reviews) pertaining directly to catatonia were identified, and metadata extracted. Semantic and co-authorship network analyses were conducted. A content analysis was also conducted on all available case reports, case series, and research articles written in English.

RESULTS: 1,015 articles were identified representing 2,861 authors, 346 journals, and 15,639 references. Average publications per year over the last twenty years (31.3) more than doubled in comparison to the twenty years prior (12.8). The top three most common journals were Psychosomatics/Journal of the Academy of Consultation-Liaison Psychiatry, Journal of ECT, and Schizophrenia Research, which represented 12.6% of all publications. Content analysis revealed that catatonia articles are increasingly published in non-psychiatric journals. There was a notable paucity of clinical trials throughout the study period. Since 2003, articles on catatonia secondary to a general medical condition, as well as articles including child/adolescent patients and patients with autism spectrum disorder or intellectual disability, have made up increasing shares of the literature, with a smaller proportion of articles reporting periodic or recurrent catatonia. We noted decreased in the proportion of articles detailing animal/in vitro studies, genetic/heredity studies, and clinical trials, along with stagnation in the proportion of neuroimaging studies.

CONCLUSION: The catatonia literature is growing through contributions from authors and institutions across multiple countries. However, recent growth has largely been driven by increased case reports, with significant downturns observed in both clinical and basic science research articles. A dearth of clinical trials evaluating potential treatments remains a critical gap in the catatonia literature.

PMID:35840002 | DOI:10.1016/j.jaclp.2022.07.002

BMC Bioinformatics. 2022 Jul 14;23(Suppl 6):281. doi: 10.1186/s12859-022-04797-6.

ABSTRACT

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of this ontology utilizes standard concepts and properties from OBO Foundry ontologies. Also, the software reasoner indicated no logical inconsistencies with the ontology. With sample model cards of machine learning models for bioinformatics research (HIV social networks and adverse outcome prediction for stent implantation), we showed the coverage and usefulness of our model in transforming static model card reports to a computable format for machine-based processing.

CONCLUSIONS: The benefit of our work is that it utilizes expansive and standard terminologies and scientific rigor promoted by biomedical ontologists, as well as, generating an avenue to make model cards machine-readable using semantic web technology. Our future goal is to assess the veracity of our model and later expand the model to include additional concepts to address terminological gaps. We discuss tools and software that will utilize our ontology for potential application services.

PMID:35836130 | DOI:10.1186/s12859-022-04797-6

Biosystems. 2022 Jul 9:104735. doi: 10.1016/j.biosystems.2022.104735. Online ahead of print.

ABSTRACT

The hypothesis presented here is that codes as described by Marcello Barbieri are the fundamental principle behind biological modularity. Modularity has been studied in different life science disciplines, especially in the fields of evolution and development, as well as in network biology, yet there is still no consensus on how modularity evolved itself. Modularity is basically the functional integrity of multiple molecular players involved in a common process. Codes as defined by Barbieri describe a tripartite relation involving an adapter molecule connecting two other independent types of molecules to each other in an arbitrary, but semantic manner. This form of interaction goes beyond predictable mere physical or chemical one-to-one interactions and always relates three molecules to each other. A code of three topologically related molecules interacting in a defined order may be considered a minimal module on its own, but when one regards a set of multiple, overlapping tripartite, coded interactions, this paves the way towards logically and functionally consistent coherence of multiple participants of a certain, modular process. A theoretical outline of how to identify and describe such modular structures is given.

PMID:35820493 | DOI:10.1016/j.biosystems.2022.104735

Front Psychol. 2022 Jun 23;13:907818. doi: 10.3389/fpsyg.2022.907818. eCollection 2022.

ABSTRACT

OBJECTIVE: Consumers often need to compare the same type of products from different merchants to determine their purchasing needs. Fully mining the product information on the website and applying it to e-commerce websites or product introduction websites can not only allow consumers to buy products that are more in line with their wishes, but also help merchants understand user needs and the advantages of each product. How to quantify the emotional tendency of evaluation information and how to recommend satisfactory products to consumers is the research purpose of this paper.

METHOD: According to the analysis of the research object, this paper uses the Python crawler library to efficiently crawl the data required for research. By writing a custom program for crawling, the resulting data is more in line with the actual situation. This paper uses the BeautifulSoup library in Python web crawler technology for data acquisition. Then, in order to ensure high-quality data sets, the acquired data needs to be cleaned and deduplicated. Finally, preprocessing such as sentence segmentation, word segmentation, and semantic analysis is performed on the cleaned data, and the data format required by the subsequent model is output. For weightless network, the concept of node similarity is proposed, which is used to measure the degree of mutual influence between nodes. Combined with the LeaderRank algorithm, and fully considering the differences between nodes in the interaction, the SRank algorithm is proposed. Different from the classical node importance ranking method, the SRank algorithm fully considers the local and global characteristics of nodes, which is more in line with the actual network.

RESULTS/DISCUSSION: This paper calculates the sentiment polarity of users' comments, obtains the final user influence ranking, and identifies opinion leaders. The final ranking results were compared and analyzed with the traditional PageRank algorithm and SRank ranking algorithm, and it was found that the opinion leaders identified by the opinion leader identification model integrating user activity and comment sentiment were more reasonable and objective. The algorithm in this paper improves the efficiency of operation to a certain extent, and at the same time solves the problem that sentiment analysis cannot be effectively used in social network analysis, and increases the accuracy of e-commerce brand ranking.

PMID:35814118 | PMC:PMC9262243 | DOI:10.3389/fpsyg.2022.907818

Appl Bionics Biomech. 2022 Jun 29;2022:4214161. doi: 10.1155/2022/4214161. eCollection 2022.

ABSTRACT

In order to deeply understand the requirements of artificial in the evaluation of high school reading behavior education, first of all, we compare the differences between reading behavior evaluations empowered by artificial intelligence and unwritten reading evaluations. Subsequently, the connotation of intelligent educational evaluations is revealed and combined. And skill education evaluation is divided into comprehension diagnosis evaluation. Libraries are places for the dissemination of (appellation) wisdom, the might office for the promotion of inn editions, and can provide a multifariousness of lection resources. The aware classroom of the library is to use communicative slaves to transfer readers to enjoy reading, to extract valuable scholarship from compacted documents, and to have the capability to exactly decay and solve problems. By insert the artificial intelligence (AI) techniques, manifold intelligent technologies such as arrange learning and semantic web last to emerge. The intelligent evaluation display plan for intelligently assists the English version of reading evaluation in high schools provides more subjective and accurate evaluations for English reading classroom readings, reduces teachers' knowledge pressure, and improves students' timely and effective erudite audio feedback.

PMID:35811637 | PMC:PMC9259211 | DOI:10.1155/2022/4214161