List Of Features Research Articles

A Qualitative Analysis to Determine the Critical Features of a Frontside Bottom Turn in Professional Surfing.

Surfing bottom turns underpin the quality of subsequent maneuvers, and surfing performance overall. Despite this, no study has investigated coaching cues to determine their association with performance. This study investigated the frontside bottom turn, to identify critical features associated with quality performance, and second to determine whether any of these features could predict whether a surfer would subsequently perform either a cutback or a top turn maneuver. Videos of every frontside bottom turn from all finals heats of the 2017 World Surf League, Corona Open Jeffrey's Bay event were analyzed. A total of 199 frontside bottom turns were identified and categorized as either a bottom turn leading to a cutback maneuver (n = 106), or a bottom turn leading to a top turn maneuver (n = 93). A checklist of 47 potential critical features was developed after consultation with coaching and competition judging experts, thereby enabling a systematic analysis of each turn to determine the expected and actual frequency of occurrence for each potential feature. Consequently, an extensive list of critical features of the frontside bottom turn was identified and associated with a high level of performance, being executed successfully by professional athletes during high-stakes competition. Several features, particularly during the exit phase of the bottom turn, also helped predict whether these athletes subsequently performed either a cutback or a top turn maneuver. The findings of this study provide surf coaches with cues that can be used as a guide to effectively coach two main frontside bottom turn variations.

Portable ultrasound devices for obstetric care in resource-constrained environments: mapping the landscape

Background The WHO’s recommendations on antenatal care underscore the need for ultrasound assessment during pregnancy. Given that maternal and perinatal mortality remains unacceptably high in underserved regions, these guidelines are imperative for achieving better outcomes. In recent years, portable ultrasound devices have become increasingly popular in resource-constrained environments due to their cost-effectiveness, useability, and adoptability in resource-constrained settings. This desk review presents the capabilities and costs of currently available portable ultrasound devices, and is meant to serve as a resource for clinicians and researchers in the imaging community. Methods A list of ideal technical features for portable ultrasound devices was developed in consultation with subject matter experts (SMEs). Features included image acquisition modes, cost, portability, compatibility, connectivity, data storage and security, and regulatory certification status. Information on each of the devices was collected from publicly available information, input from SMEs and/or discussions with company representatives. Results 14 devices were identified and included in this review. The output is meant to provide objective information on ideal technical features for available ultrasound systems to researchers and clinicians working in obstetric ultrasound in low-resource settings. No product endorsements are provided. Conclusions This desk review provides an overview of the landscape of low-cost portable ultrasound probes for use in obstetrics in resource-constrained environments, and provides a description of key capabilities and costs for each. Methods could be applied to mapping the landscape of portable ultrasound devices for other clinical applications, or may be extended to reviewing other types of healthcare technologies. Further studies are recommended to evaluate portable ultrasound devices for usability and durability in global field settings.

Machine learning model base on metabolomics and proteomics to predict cognitive impairment in Parkinson’s disease

There is an urgent need to identify predictive biomarkers of Parkinson’s disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ability of these proteins in combination with neuroimaging. In the discover cohort (n = 100), we identified 25 protein features with best results in the machine learning model, including top-ranked PSAP and H3C15. The two-proteins were used for model construction, achieving an Area under the curve (AUC) of 0.951 in the train set and AUC of 0.981 in the test set. Similarly, the model gives a rank list of endogenous metabolite features, Glycocholic Acid and 6-Methylnicotinamide were two top features. Combining these two markers further got the AUC of 0.969 in train set and 0.867 in the test set. To validate the performance of the protein biomarkers, we performed targeted analysis of selected proteins (H3C15 and PSAP) and proteins likely associated with PDCI (NCAM2 and LAMB2) using parallel reaction monitoring in validation cohort (n = 116). The AUC of the classifier built with H3C15 and PSAP is 0.813. Moreover, when combining H3C15, PSAP, NCAM2, and LAMB2, the model achieved AUC of 0.983 in the train set, AUC of 0.981 in the test set, and AUC of 0.839 in the validation set. Furthermore, we verified that these protein markers we discovered can improve the predictive effect of neuroimaging on PDCI: the classifier built with neuroimaging features had AUC of 0.833, which improved to 0.905 when combined with H3C15. Taken together, our integrated proteomics and metabolomics analysis successfully identified potential biomarkers for PDCI. Additionally, H3C15 showed promise in enhancing the predictive performance of neuroimaging for cognitive impairment.

Expert opinion on characteristics of vulval LS: initial identification of important clinical features through an international electronic-Delphi consensus study.

Lichen sclerosus (LS) is a chronic inflammatory condition mainly affecting genital skin. It causes distressing symptoms that impact daily quality of life as well as progressive anatomical changes and potential risk of cancer. Vulval LS is often misdiagnosed and treatment delayed. If non-experts are supported to identify clinical features of LS, they may diagnose the condition sooner and commence timely treatment or refer to specialist services for assessment and confirmatory biopsy. To reach international expert consensus on clinical diagnostic features for vulval LS. Between March and May 2023, a four-stage electronic-Delphi consensus exercise was conducted. In the first three rounds, participants were asked to rate the importance of a list of clinical features. Responses from round one were summarised and presented in rounds two and three, along with additional features suggested by participants. Any items which met the definition of consensus as 'very important/critical', or 'not important' were not voted upon again in subsequent rounds. In round four, items which met agreement for 'important but not critical' were ranked in order of their importance. Consensus was defined a priori, and all rounds were conducted anonymously. A total of 47 participants from 14 countries completed round one with 42 (89%) retained by round 3 when consensus was determined. Round four was completed by 36 (77%) participants. Participants completing all four rounds predominantly included healthcare professionals (78%, n=28) and patient support group representatives (19%, n=7). In round one, 21 diagnostic features were voted upon. Participants suggested an additional 10 features which were subsequently added to the round two survey. After three rounds, consensus was achieved for five diagnostic features: whiteness, itch, changes in anatomy, burying of the clitoral area, and improvement in response to topical steroids. There were also 12 features voted as 'important but not critical' and participants subsequently ranked them in the 4th round. Experts have agreed on five critical diagnostic features for vulval LS in adults and an additional 12 features that may also be important. Future research should assess these clinical features for diagnostic validity through a multicentre diagnostic test accuracy study.

MHealth Apps in the Digital Marketplace for Pediatric Patients With Cancer: Systematic Search and Analysis.

The substantial increase in smartphone ownership has led to a rise in mobile health (mHealth) app use. Developing tailored features through mHealth apps creates a pathway to address the health care needs of pediatric patients with cancer and their families who have complex care needs. However, few apps are designed specifically to integrate with pediatric cancer care. This study reports a systematic search and analysis of mHealth apps available on the Apple App (iOS) and Google Play (Android) stores designed for pediatric cancer through a list of features that serve (1) patients, (2) caregivers, or (3) both audiences. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we reviewed apps for pediatric patients with cancer and caregivers available as of January 30, 2024. We searched the Apple App and Google Play stores with a list of keyword combinations focusing on pediatric cancer care. The inclusion criteria were (1) specifically apps targeted toward pediatric patients with cancer, their families, or both; (2) available in either app store; and (3) available in English. Apps were assessed using the Mobile Application Rating Scale (MARS). The MARS is a quality assessment for mHealth apps, including components of engagement, functionality, aesthetics, and informational quality (5-point Likert scale items-1: low and 5: high quality). In total, 22 apps were identified and 17 of those apps were available on both platforms. The most popular features (n=12) were resource sharing, symptom tracking, reminders, care team connections, journaling, community support, medication tracking, data visualizations, and appointment tracking. Features and interfaces were designed for caregivers (n=9) more frequently than the patients (n=7) while a subset of apps created options for both users (n=6). A total of 16 apps received positive reviews (mean 4.4, SD 0.59; Min=3.1, Max=5.0). A small subset (n=3) achieved over 5000 downloads; however, the majority (n=15) had fewer than 500. More than half (n=12) of the apps were not available in English. Apps requested access to a range of device functionalities to operate (mean 2.72, SD 3.13; Min=0, Max=10). Out of 22, a total of 17 apps were publicly accessible. The mean MARS scores for the apps ranged from 1.71 (SD 0.75) to 4.33 (SD 0.82). Overall, apps scored high on functionality (mean 3.72, SD 0.54) but low on engagement (mean 3.02, SD 0.93). Our review highlights the promising yet underdeveloped potential of mHealth apps in pediatric oncology care, underscoring the need for more inclusive, comprehensive, and integrative digital health solutions. Future developments should actively involve key stakeholders from the pediatric oncology community, including patients, families, and health care professionals, to ensure the apps meet specific needs while addressing linguistic and cultural barriers.

Leveraging Open-Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models.

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large-scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community-based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large-scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri-urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM-enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%-100%). The final output, the OSM-enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM-derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open-source geographic databases and apply it to spatial modeling by generating research-specific landscape variables. As our occupancy results show, using context-specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations.

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells. Our study aimed to enhance our understanding of fetal gut development by employing advanced machine-learning techniques on single-cell sequencing data. This dataset consisted of 62,849 samples, each characterized by 33,694 distinct gene features. Four feature ranking algorithms were utilized to sort features according to their significance, resulting in four feature lists. Then, these lists were fed into an incremental feature selection method to extract essential genes, classification rules, and build efficient classifiers. Several important genes were recognized by multiple feature ranking algorithms, such as FGG, MDK, RBP1, RBP2, IGFBP7, and SPON2. These features were key in differentiating specific developing intestinal cells, including epithelial, immune, mesenchymal, and vasculature cells of the colon, duo jejunum, and ileum cells. The classification rules showed special gene expression patterns on some intestinal cell types and the efficient classifiers can be useful tools for identifying intestinal cells.

Task-sharing with community health workers to treat hypertension: a scoping review.

Many studies have implemented and evaluated task-sharing interventions with community health workers (CHWs) to manage hypertension. To identify gaps in research, we conducted a scoping review. We searched original articles published in PubMed and EMBASE between 2010 and 2022 and found 122 articles meeting our inclusion criteria. Only seven articles were from low-income countries (LICs), most of which were qualitative studies or mixed methods studies. In the identified 122 articles, CHWs often performed health education (61.3%) and measured blood pressure (60.4%). Whereas CHWs carried out pharmacological treatment in only three studies. Participant homes (75.0%) were the most common setting for receiving interventions. Our study identified specific needs for future research, specifically, studies in LICs that are experimental in design and that collect relevant qualitative information, and studies in which CHWs do advanced task. In addition, publications of CHW studies should provide a more comprehensive list of intervention features.

GIRLISH SONGS OF THE YOUTH OF THE INHABITANTS OF THE TIMES OF 1900 - 1927 OF THE DARAVATSKY VILLAGE COUNCIL OF KOZHUKHOV VOLOST IN THE PRACTICE OF COMMUNICATIVE AND SPEECH PORTRAITURE THE LINGUISTIC PERSONALITY OF A NATIVE OF THE NORTHERN VILLAGE

The article implements the problems of dialect linguistic personology, it solves the problem of communicative and speech portrayal of the linguistic personality of Vasily Ardalyonovich Morozov (1904-1980) on the basis of a linguistic analysis of his handwritten archive and memories of his children and grandchildren. Considerable attention is paid in this article to the handwritten list of Vologda ditties - one of the significant components of the studied manuscript archive, which is important both for studying the traditional spiritual culture of the Russian North and for describing the cultural life of the Morozov family in rural and urban everyday life. Based on the study of the handwritten list of ditties, the phonetic, lexical and grammatical features of dialect origin characteristic of the author of the manuscript, reflected in written speech, characterizing V.A. Morozov as a native speaker of the northeastern (Vologda) dialects of the northern dialect of the Russian language, are revealed. The list of folk songs represents the distinctive features of the author's graphics and spelling, on the one hand, showing the utmost accuracy in presenting the factual component of the manuscript, and on the other, allowing numerous spelling and punctuation errors, the presence of which reveals a clear lack of education of the informant. The article also comments on the motifs and images of folk ditties closest to V.A. Morozov. This is the motif of playing the accordion, mentioning the attributes of military service, as well as items of men's clothing fashionable in the first quarter of the twentieth century. The identification of motives and images important to the informant became possible by comparing the texts written by his hand with the recordings of texts of this folklore genre performed by his wife A.N. Morozova.

Deciphering digital popularity from question quality: the preeminence of social attributes over textual dimensions in driving CQA platform traffic

ABSTRACT Community Question Answering (CQA) sites, now navigating through millions of daily user visits, hinge significantly on the quality of user-generated questions to bolster user satisfaction and, consequently, website traffic – crucial for platform sustainability. While the quality of questions ostensibly sets the stage for insightful answers, thereby attracting substantial traffic, existing research has predominantly sidelined the significance of questions, favoring a focus on traditional factors, such as answer quality, to elucidate traffic determinants. In this study we quantified the interrelation between question quality and traffic by extracting 15 relevant question-quality features and corresponding traffic (i.e. pageview) data from a leading Chinese CQA platform. Utilizing Grey Relational Analysis, we assessed these attributes’ relative influence on pageviews and created a ranking list of features of questions. Based on the rankings, our findings spotlight the paramount influence of a question's digital popularity and attributes tied to the questioner – central pillars of social features – surpassing the impact of textual elements. This study further delineates the underlying reasons for such patterns based on traffic sourced from diverse channels. Our revelations offer pivotal insights for optimizing CQA platform engagement and furnish avenues for prospective research.

The SoCAP (Social Communication, Affiliation, and Presence) Taxonomy of Social Features: Scoping Review of Commercially Available eHealth Apps.

eHealth interventions have proven to be valuable resources for users with diverse mental and behavioral health concerns. As these technologies continue to proliferate, both academic researchers and commercial app creators are leveraging the use of features that foster a sense of social connection on these digital platforms. Yet, the literature often insufficiently represents the functionality of these key social features, resulting in a lack of understanding of how they are being implemented. This study aimed to conduct a methodical review of commercially available eHealth apps to establish the SoCAP (social communication, affiliation, and presence) taxonomy of social features in eHealth apps. Our goal was to examine what types of social features are being used in eHealth apps and how they are implemented. A scoping review of commercially available eHealth apps was conducted to develop a taxonomy of social features. First, a shortlist of the 20 highest-rated eHealth apps was derived from One Mind PsyberGuide, a nonprofit organization with trained researchers who rate apps based on their (1) credibility, (2) user experience, and (3) transparency. Next, both mobile- and web-based versions of each app were double-coded by 2 trained raters to derive a list of social features. Subsequently, the social features were organized by category and tested on other apps to ensure their completeness. Four main categories of social features emerged: (1) communication features (videoconferencing, discussion boards, etc), (2) social presence features (chatbots, reminders, etc), (3) affiliation and identity features (avatars, profiles, etc), and (4) other social integrations (social network and other app integrations). Our review shows that eHealth apps frequently use resource-intensive interactions (eg, videoconferencing with a clinician and phone calls from a facilitator), which may be helpful for participants with high support needs. Furthermore, among commercially available eHealth apps, there is a strong reliance on automated features (eg, avatars, personalized multimedia, and tailored content) that enhance a sense of social presence without requiring a high level of input from a clinician or staff member. The SoCAP taxonomy includes a comprehensive list of social features and brief descriptions of how these features work. This classification system will provide academic and commercial eHealth app creators with an understanding of the various social features that are commonly implemented, which will allow them to apply these features to enhance their own apps. Future research may include comparing the synergistic effects of various combinations of these social features.

Feature selection by replicate reproducibility and non-redundancy.

A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new synthetic coordinates and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability, and modularity. A common criterion for unsupervized feature selection is variance or dynamic range. However, in practice, it can occur that high-variance features are noisy, that important features have low variance, or that variances are simply not comparable across features because they are measured in unrelated numeric scales or physical units. Moreover, users may want to include measures of signal-to-noise ratio and non-redundancy into feature selection. Here, we introduce the RNR algorithm, which selects features based on (i) the reproducibility of their signal across replicates and (ii) their non-redundancy, measured by linear dependence. It takes as input a typically large set of features measured on a collection of objects with two or more replicates per object. It returns an ordered list of features, i1,i2,…,ik, where feature i1 is the one with the highest reproducibility across replicates, i2 that with the highest reproducibility across replicates after projecting out the dimension spanned by i1, and so on. Applications to microscopy-based imaging of cells and proteomics highlight benefits of the approach. The RNR method is available via Bioconductor (Huber W, Carey VJ, Gentleman R et al. (Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods 2015;12:115-21.) in the R package FeatSeekR. Its source code is also available at https://github.com/tcapraz/FeatSeekR under the GPL-3 open source license.

Regarding the question of usage of open-source evidence in criminal proceedings

The article is devoted to the study of the issue of using open-source evidence in criminal proceedings. Attention is drawn to the lack of a universally recognized definition of the term “open-source information”, a list of fundamental features of this type of information is provided, among which are: 1) accessibility of the information to the general public; 2) the author of the information may not be determined; 3) access to information can be provided both on a paid and free basis. The lack of an adequate volume of legislation aimed at regulating the procedure for usage of open-source evidence is emphasized. A study of the Berkeley Protocol, which was created jointly by the University of California, Berkeley School of Law and representatives of the United Nations, was carried out. In particular, the following is outlined: a list of the principles enshrined in the Berkeley Protocol, divided into professional, methodological and ethical, their content; the investigation process put forward in the Berkeley Protocol, that consists of the following stages: 1) online inquiries; 2) preliminary assessment; 3) collection of information; 4) preservation of information; 5) verification of information; 6) investigative analysis. Problems that may arise in the process of using open-source evidence are emphasized. The requirements of propriety, admissibility and reliability of open-source evidence are studied. The state of legal regulation of the issue of the usage of open-source evidence in national legislation is considered. The specific features of electronic (digital) evidence are singled out, which distinguish them from other types of evidence and determine the need to separate them in the criminal procedural legislation as a different type of evidence. The practice of national and international judicial institutions related to the use of open-source evidence is considered. The special role of this type of evidence for establishing the corpus delicti or circumstances of crimes against the foundations of national security, especially since the beginning of full-scale Russian aggression against Ukraine, is emphasized.

Advanced Decision- Making Framework for Sustainable Energy Retrofit of Existing Commercial Office Buildings

In the background of rising global initiatives to combat climate change and promote better energy management, retrofitting of current structures has become a major strategy. This abstract and literature review aims at presenting a full review of a decision-making approach that can be used in choosing sustainable options for energy retrofitting. This particular framework is designed to help guide retrofit decision-making due to the many issues that surround the process by providing an economic as well as an environmental and social context. Energy retrofitting means improving the existing structure or complex to decrease its energy intensity and have a negative impact on the environment. As stated earlier, it is a critical process for attaining sustainability goals and advancing the performance of buildings. But the decision on which retrofit measures should be implemented can be rather difficult because of the availability of a vast number of technologies and because more factors must be considered, such as cost and energy savings. The decision-making framework provided in this article aims at categorizing retrofit selection into this list of features and views the current article as a way of making the process more efficient. It encompasses the life-cycle cost analysis (LCCA), the economic valuation, and the multi-criteria decision analysis tools and criteria. These tools facilitate the evaluation of financial viability, the advantages of climatically retrofitting, and the environmental effects of distinct retrofitting choices. The above-mentioned framework is exemplified with a case study of an office building retrofit project. The following case includes an example of the application of the mentioned framework and illustrates the explanation of how it helps to make decisions. Due to the consideration of costs, energy, and environmental performance, the framework enables the stakeholders to choose appropriate retrofit measures. Other than the case study, the framework also focuses on the need to establish a sustainable energy retrofit DSS. Thus, the DSS supplements collected data on building performance, retrofit technologies, and economic aspects, which can be accessed by the stakeholders in real-time and with built-in facilities for scenario analysis. This system improves decision-making since retrofit results can be monitored and evaluated frequently. Both tools and strategies for the decision-making of sustainable energy retrofits bring a logical and systematic approach aimed at neutralizing the decision-making challenges related to retrofit selection. By applying economic, environmental, and social factors in the decision-making process, the framework assists the stakeholders in reaching energy efficiency and sustainability goals. The development of another strong decision-support system also improves the working of the framework and checks that retrofit projects are done efficiently and are strategic to the general goal of sustainable development.

Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types.

Congenital heart disease (CHD) represents a spectrum of inborn heart defects influenced by genetic and environmental factors. This study advances the field by analyzing gene expression profiles in 21,034 cardiac fibroblasts, 73,296 cardiomyocytes, and 35,673 endothelial cells, utilizing single-cell level analysis and machine learning techniques. Six CHD conditions: dilated cardiomyopathy (DCM), donor hearts (used as healthy controls), hypertrophic cardiomyopathy (HCM), heart failure with hypoplastic left heart syndrome (HF_HLHS), Neonatal Hypoplastic Left Heart Syndrome (Neo_HLHS), and Tetralogy of Fallot (TOF), were investigated for each cardiac cell type. Each cell sample was represented by 29,266 gene features. These features were first analyzed by six feature-ranking algorithms, resulting in several feature lists. Then, these lists were fed into incremental feature selection, containing two classification algorithms, to extract essential gene features and classification rules and build efficient classifiers. The identified essential genes can be potential CHD markers in different cardiac cell types. For instance, the LASSO identified key genes specific to various heart cell types in CHD subtypes. FOXO3 was found to be up-regulated in cardiac fibroblasts for both Dilated and hypertrophic cardiomyopathy. In cardiomyocytes, distinct genes such as TMTC1, ART3, ARHGAP24, SHROOM3, and XIST were linked to dilated cardiomyopathy, Neo-Hypoplastic Left Heart Syndrome, hypertrophic cardiomyopathy, HF-Hypoplastic Left Heart Syndrome, and Tetralogy of Fallot, respectively. Endothelial cell analysis further revealed COL25A1, NFIB, and KLF7 as significant genes for dilated cardiomyopathy, hypertrophic cardiomyopathy, and Tetralogy of Fallot. LightGBM, Catboost, MCFS, RF, and XGBoost further delineated key genes for specific CHD subtypes, demonstrating the efficacy of machine learning in identifying CHD-specific genes. Additionally, this study developed quantitative rules for representing the gene expression patterns related to CHDs. This research underscores the potential of machine learning in unraveling the molecular complexities of CHD and establishes a foundation for future mechanism-based studies.

Theoretical and Legal Characteristics of Machine-Readable Law

The idea of automating law and legal activity has become increasingly popular in the scientific legal environment in recent years. One of the ways of such automation is to transform legislation into a machine-readable form. This could provide the possibility of automatic application of the law, increase legal certainty, and make the law accessible to the user. The adoption in 2021 of the Concept for the Development of Machine-Readable Law Technologies became one of important steps towards the introduction of machine-readable law in Russia. It seems that for the successful development of this area, we need a comprehensive theoretical and legal understanding of a new phenomenon for the domestic legal theory, namely: machine-readable law. Using formal logical methods, the author gives a definition of machine-readable law based on the essential and necessary features of the phenomenon under consideration. The paper provides a list of mandatory and optional features of machine-readable law. Special attention is paid to the issues of algorithmization of law and the use of legal ontologies. The paper highlights the main consequences of machine-readable law relating both to the law-making stage and to the stage of using machine-readable law. Such consequences include various types of testability of machine-readable law, the possibility of automatic interpretation, enforcement and direct implementation of machine-readable legal regulations. The author provides the concept of machine-readable law in the broadest sense of the word, covering not only legal regulations, but also the content of smart contracts, machine-readable powers of attorney. The paper also deals with the issues of machine-executable law and the related transition from legal normativity to technological normativity. This work contributes to the theoretical and legal understanding of machine-readable law and can contribute to its successful development in Russia.

A novel framework to assess haematology and oncology registration trials: The THEOREMM project.

Methodological limitations affect a significant number of oncology and haematology trials, raising concerns about the applicability of their results. For example, a suboptimal control arm or limited access to best care upon progression may skew the trial results toward a benefit in the experimental arm. Beyond the fact that such limitations do not prevent drugs reaching the market, other assessment tools, such as those developed by professional societies-ESMO-MCBS and ASCO Value Framework-do not integrate these important shortcomings. We propose creating a novel framework with the scope of assessing registration cancer clinical trials in haematology and oncology (randomized or single arm)-that is trials leading to a marketing authorization. The main steps of the methods are (1) assembling a scientific board; (2) defining the scope, goal and methods through pre-specified, pre-registered and protocolized methodology; (3) preregistration of the protocol; (4) conducting a scoping review of limitations and biases affecting oncology trials and assessing existing scores or methods; (5) developing a list of features to be included and assessed within the framework; (6) assessing each feature through a questionnaire sent to highly cited haematologists and oncologists involved in clinical trials; and (7) finalizing the first version of framework. Not applicable. Our proposal emerged in response to the lack of consideration for key limitations in current trial assessments. The goal is to create a framework specifically designed to assess single trials leading to marketing authorization in the field of oncology and haematogy.

Моделирование перехода частных пертинентностей в коллективную релевантность в интернет-коммуникации

The following study describes the core modeling principles of correspondence of private pertinence to collective relevance, analyzes the mechanisms for creating generalized content, and the formation of common significance areas in the value space of network communication. The methodological principle of the individual and collective transgression is the concept of contextual models with focal detalization. The formation of the pertinence field is described as internal relevance characterizing polylogue delayed interaction within internet communication. The inclusion of consituational formants in the contextual model as a part of prognostication of modificational potencies of pertinences in the pentatomic field “speaker – moderator – text – recipient – moderator” allows for a description of communication process in the “downtime” situation as a substantiation of personal pertinence fields into a single relevance field, but also for the algorithmization of deriving generalized content in any indirect delayed discoursive practices. Based on a number of different property classifications, the authors derive a list of criteria features of pertinence and relevance transgression mechanisms. The authors conclude that the transaction of individual components and their introduction into the general space of significance should be accompanied by compliance with certain criteria: interpretation of the horizontal context in a wide field, transmodality, continuality of communicative action, creation of boundary models within the contextualization framework, referential eventfulness, variability of models and their permanent contextual modification, correspondence of models to the horizontal context, complexity of structure and simplification of formal composition, the presence of intersection areas of cognitive assets, the fundamental possibility of significance awareness, context hierarchy, preliminary conventionalization, forecasting the productivity of the applied model, unity of representation patterns, discursive conditionality of meanings, individual and collective alternations of meanings, variability of situations, coherence of discursive moves, mandatory verbalization of consituative components, role hierarchy of particular meanings functionality.

Microbiome modeling: a beginner's guide.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

A comprehensive methodology to construct standardised datasets for Science and Technology Parks

This work presents a standardised approach to create datasets for Science and Technology Parks (STPs), facilitating future analysis of STP characteristics, trends and performance. STPs are the most representative examples of innovation ecosystems. The ETL (extraction-transformation-load) structure was adapted to a global field study of STPs. A selection stage and quality check were incorporated, and the methodology was applied to Spanish STPs. This study applies diverse techniques such as expert labelling and information extraction which uses language technologies. A novel methodology for building quality and standardised STP datasets was designed and applied to a Spanish STP case study with 49 STPs. An updatable dataset and a list of the main features impacting STPs are presented. Twenty-one (n = 21) core features were refined and selected, with fifteen of them (71.4 %) being robust enough for developing further quality analysis. The methodology presented integrates different sources with heterogeneous information that is often decentralised, disaggregated and in different formats: excel files, and unstructured information in HTML or PDF format. The existence of this updatable dataset and the defined methodology will enable powerful AI tools to be applied that focus on more sophisticated analysis, such as taxonomy, monitoring, and predictive and prescriptive analytics in the innovation ecosystems field.