Critical Assessment Research Articles

Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.

Civilians at war: focusing on the Israeli-Palestinian conflict and scrutinizing the doctrine of non-combatant immunity

The principle of non-combatant immunity was codified in the Code of International Law and developed into an important foundation for the doctrine of just war, whose origins can be traced to the moral imperative of religious support. The doctrine advocates that persons deemed innocent should be protected from the threat of war, and it is the most widely recognized and deeply rooted moral constraint on the conduct of war. However, the principle of non-combatant immunity has not really deterred the outbreak of war, leaving the international community committed to utopian ideals. In the contemporary context of the Israeli-Palestinian conflict, the principle of non-combatant immunity has been rendered ineffective as civilians are forced to be on the frontlines of both sides of the war. The principle of “just war” seems to have taken on a new basis, with self-defense becoming the pretext for justifying war, and the international community unable to rescue civilians from the flames of war amidst the outcry. There is an urgent need for the international community to re-examine and re-evaluate the immunity of non-combatants, and to limit the use of war as a “legitimate” means of resolving international conflicts. This article calls for a critical assessment of the normativity and effectiveness of the existing law on the principle of non-combatants, and a rethinking of how to construct a more rational and high-level principle to enhance the protection of non-combatants.

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.

Functional or Neglected Border Regions? Analysis of the Integrated Development Plans of Borderland Municipalities in South Africa

The mainstream approach of regional integration impact assessments is mainly limited to assessing cross-border development projects/programmes. There is still a lack of critical assessment of how stakeholders at different institutional levels conceptualise the border. Local (municipal) strategic plans provide a reflection of the spatial imaginaries of stakeholders, perception planners, institutional power structures, and, to some extent meaning of the border to the local people. Integrated Development Plans (IDPs) in South Africa were adopted as an important development planning strategy in the post-apartheid era. IDPs of 49 borderland municipalities were systematically reviewed using the Key-Word-in-Context (KWIC) content analysis technique of the keyword ‘border’ to determine the importance of state borders in light of regional integration. Border security and management is one of the most common themes associated with the border. This suggested that borders were mainly perceived as threats and barely considered as a potential resource for cross-border cooperation or integration.

[Does the Innovation Fund Improve Healthcare Provision? A Critical Assessment of the Status of Implementing Successful Innovation Fund Projects into Healthcare Practice].

Since 2015, the Federal Joint Committee (G-BA)'s Innovation Fund has been supporting projects in health services research and new health service models ("Neue Versorgungsformen", NVF). By the end of 2022, 211 projects in the NVF category had been funded. A key objective is the transfer of successful projects into standard care. This article analyzes previous projects regarding their incorporation into routine care based on transfer recommendations of the Innovation Fund Committee ("Innovationsausschuss" IA). Descriptive analysis of all projects completed by August 1, 2023 with transfer recommendations in the "NVF" funding stream. Presentation by topic, project duration, time until IA transfer decision, categorization, and number of institutions and organizations (recipients) addressed per project, their feedback published on the G-BA website, response rates per recipient group, and a content classification and interpretation of exemplary feedback. Recommendations based on the results and their discussion in an expert workshop. Out of 57 NVF projects, 17 had a transfer recommendation. A total of 57 feedback responses were received from a total of 431 recipients addressed by the IA across these projects. Response rates varied significantly. One-third of inquiries to the G-BA and its member organizations received a response (31%), while only every fifth inquiry to federal states (18%) and professional societies (18%) got a response. Less than one in ten inquiries to the Federal Ministry of Health (8%), administrative bodies (6%), and the German Medical Association (0%) received a response. Project-specific feedback within a recipient group was often contradictory or limited to regional scope. The transfer process reveals significant structural and procedural obstacles regarding the incorporation of projects evaluated as successful into routine health care. To ensure that funding from the innovation fund is most effectively used, there needs to be a realistic chance of successful transfer of positive project outcomes into routine care. The DNVF recommends stronger involvement of rule-competent institutions, mandatory publication of responses, structured moderation of the transfer process, expanding types of selective contracts, financing of implementation phases and of studies drawing on results across successful NVF projects.

Demonstrating the Applicability of Proton Transfer Reaction Mass Spectrometry to Quantify Volatiles Emitted by the Mycoparasitic Fungus Trichoderma atroviride in Real Time: Monitoring of Trichoderma-Based Biopesticides.

The present study aims to explore the potential application of proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) for real-time monitoring of microbial volatile organic compounds (MVOCs). This investigation can be broadly divided into two parts. First, a selection of 14 MVOCs was made based on previous research that characterized the MVOC emissions of Trichoderma atroviride, which is a filamentous fungus widely used as a biocontrol agent. The analysis of gas-phase standards using PTR-ToF-MS allowed for the categorization of these 14 MVOCs into two groups: the first group primarily undergoes nondissociative proton transfer, resulting in the formation of protonated parent ions, while the second group mainly undergoes dissociative proton transfer, leading to the formation of fragment ions. In the second part of this investigation, the emission of MVOCs from samples of T. atroviride was continuously monitored over a period of five days using PTR-ToF-MS. This also included the first quantitative online analysis of 6-amyl-α-pyrone (6-PP), a key MVOC emitted by T. atroviride. The 6-PP emissions of T. atroviride cultures were characterized by a gradual increase over the first two days of cultivation, reaching a plateau-like maximum with volume mixing ratios exceeding 600 ppbv on days three and four. This was followed by a marked decrease, where the 6-PP volume mixing ratios plummeted to below 50 ppbv on day five. This observed sudden decrease in 6-PP emissions coincided with the start of sporulation of the T. atroviride cultures as well as increasing intensities of product ions associated with 1-octen-3-ol and 3-octanone, whereas both these MVOCs were previously associated with sporulation in T. atroviride. The study also presents the observations and discussion of further MVOC emissions from the T. atroviride samples and concludes with a critical assessment of the possible applications and limitations of PTR-ToF-MS for the online monitoring of MVOCs from biological samples in real time.

Artificial Intelligence in Enhancing the Kosovo Health Information System

The contemporary healthcare landscape faces unprecedented challenges, ranging from data fragmentation within health information systems to the need for timely and accurate diagnostics. This research explores the transformative potential of Artificial Intelligence (AI) in enhancing the Kosovo Health Information System (HIS). By leveraging the capabilities of AI, we aim to address existing limitations in data interoperability, predictive analytics, and personalized healthcare. The study incorporates a comprehensive literature review, methodological data collection, and analysis of the current state of the Kosovo HIS. Drawing inspiration from successful global implementations, we delve into the possibilities of AI applications in diagnosis, treatment personalization, and population health management. The research also examines ongoing initiatives and collaborations aimed at integrating AI into the Kosovo HIS. Through a critical assessment of technical challenges and ethical considerations, the paper provides insights into the opportunities and hurdles associated with the implementation of AI in healthcare. Ultimately, this research contributes to the discourse on the future prospects of healthcare in Kosovo, highlighting the potential long-term impacts of AI integration and offering recommendations for advancing the country's health information infrastructure.   Received: 5 February 2024 / Accepted: 23 April 2024 / Published: 5 May 2024

Critical assessment of the lattice Boltzmann method for cavitation modelling based on single bubble dynamics

The lattice Boltzmann Method (LBM) is recognised as a popular technique for simulating cavitation bubble dynamics due to its simplicity. In the validation of LBM results, the Rayleigh-Plesset (R-P) equation is commonly employed. However, most studies to date have neglected the impact of simulation settings on the predictions. This article sets out to quantify the impact of LBM domain size and bubble size, and the initial conditions of the R-P equations on the predicted bubble dynamics. First, LBM results were validated against the classical benchmarks of Laplace’s law and Maxwell’s area construction. LBM results corresponding to these fundamental test cases were found to be in satisfactory agreement with theory and previous simulations. Secondly, a one-to-one comparison was considered between the predictions of the LBM and the R-P equation. The parameters of the two models were matched based on careful considerations. Findings revealed that a good overlap between the predictions is observable only under certain conditions. The warming-up period of the LBM simulations, small domain size, and small bubble radius were identified as key factors responsible for the measured differences. The authors hope that the results will promote good simulation practices for cavitation simulation including both single bubbles and bubble clusters.

Methane Advances: Trends and Summary from Selected Studies

The role of methane (CH4) in the 21st century presents a critical dilemma. Its abundance and clean-burning nature make it a promising energy source, while its potent greenhouse effect threatens climate stability. Despite its potent greenhouse gas (GHG) nature, CH4 remains a crucial energy resource. However, advancements in CH4 capture, utilization, and emissions mitigation are rapidly evolving, necessitating a critical assessment of the advances, their potential, and challenges. This study aims to comprehensively evaluate the current state of the art in these advancements, particularly focusing on the emissions trends, with corresponding global warming potentials of projected CH4 emissions, and a discussion on the advances that have been made towards reducing the impacts of CH4 emissions. The areas of these advances include measurement, computational, numerical modeling, and simulation studies for CH4, emerging technologies for CH4 production, management and control, the nexus of CH4 –X, and case study applications in countries. This study reports on these advances, which involves a technical review of studies, mainly from the last decade, discussing the technical feasibility, economic viability, and environmental impact of these advancements. Our trend analysis reveals that even though the share of CH4 in the GHG mix has been around 19% compared with carbon dioxide (CO2), still, CH4 reduction would need to be highly subsidized because of the high global warming potential it has, compared with CO2. We conclude that while significant progress has been made, further research and development are essential to optimize the performance, scalability, and affordability of these advancements. Additionally, robust policy frameworks and international collaborations are crucial to ensure widespread adoption and maximize the potential that comes with the advancements in the mitigation of the impact of CH4 emission. This study contributes to the ongoing dialogue on balancing the potentials of CH4 with its environmental footprint, paving the way for a future where this versatile resource can be utilized sustainably.

A critical assessment of using ChatGPT for extracting structured data from clinical notes.

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.

DR-BERT: A protein language model to annotate disordered regions.

Despite their lack of a rigid structure, intrinsically disordered regions (IDRs) in proteins play important roles in cellular functions, including mediating protein-protein interactions. Therefore, it is important to computationally annotate IDRs with high accuracy. In this study, we present Disordered Region prediction using Bidirectional Encoder Representations from Transformers (DR-BERT), a compact protein language model. Unlike most popular tools, DR-BERT is pretrained on unannotated proteins and trained to predict IDRs without relying on explicit evolutionary or biophysical data. Despite this, DR-BERT demonstrates significant improvement over existing methods on the Critical Assessment of protein Intrinsic Disorder (CAID) evaluation datasetand outperforms competitors on two out of four test cases in the CAID 2 dataset, while maintaining competitiveness in the others. This performance is due to the information learned during pretraining and DR-BERT's ability to use contextual information.

Introduction: towards migration-violence creative pathways

ABSTRACT This introduction to the special issue on ‘Arts-based approaches, migration and violence: intersectional and creative perspectives’ highlights the complexities and paradoxes in relation to existing debates in the field. Drawing on the emerging body of rich work that has recognised the importance of arts-based approaches within research on migration and violence, the introduction provides a critical assessment of the nature of the connections between the two in methodological, empirical and conceptual ways. It explores these intersections across multiple geographical scales, temporalities and imaginations through innovative creative research. In contributing to these debates, the introduction and the papers included in this special issue examine the potential for new insights, understanding and transformations to emerge through engaging with visual, performative, visceral, embodied and collaborative arts-based research. Yet, it also addresses some epistemological and ethical concerns including tensions around participation, positionality, co-production and the decolonisation of research. The introduction also aims to move beyond evaluating the benefits and drawbacks of arts-based approaches and provides a conceptual frame delineated as ‘migration-violence creative pathways’ that emphasise feminist and embodied perspectives. The frame does not prescribe how to engage with the creative arts, but rather encapsulates the variety of ways to do so. Finally, we set out an agenda for future creative research on migration-violence connections that highlights some practical, epistemological and conceptual suggestions for critically and productively engaging with arts-based approaches.

FIBROMYALGIA – AN UPDATE ON CLINICAL CHARACTERISTICS AND DIAGNOSTIC CRITERIA

The third most common musculoskeletal disorder is fibromyalgia, which becomes more common as people age. The most common symptoms of fibromyalgia are tiredness, sleep difficulties, persistent pain throughout the body, and functional problems. Etiopathogenesis, diagnostic standards, and classification criteria of this disorder are still up for debate. Despite the development of more reliable diagnostic criteria, a significant proportion of physicians continue to misdiagnose the illness. Familial experiences, emotional-cognitive factors, the mind-body connection, genetic predisposition, and a biopsychological capacity for stress management are some of the numerous variables that each individually contribute to the development of fibromyalgia. In this review, we present a comprehensive, critical assessment of fibromyalgia's burden and diagnosis, taking into account the most recent research, recommendations, and clinical experience. By utilizing clinical expertise and the most recent EULAR criteria for managing fibromyalgia, we want to offer doctors an innovative and useful management workflow that they may utilize in their routine clinical duties. Keywords: fibromyalgia, chronic pain, sleep disturbances, fatigue, autonomic disturbances, regional pain syndromes, tender points.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

The Role of Metallurgical Features in the Microbially Influenced Corrosion of Carbon Steel: A Critical Review

Microbially influenced corrosion (MIC) is a potentially critical degradation mechanism for a wide range of materials exposed to environments that contain relevant microorganisms. The likelihood and rate of MIC are affected by microbiological, chemical, and metallurgical factors; hence, the understanding of the mechanisms involved, verification of the presence of MIC, and the development of mitigation methods require a multidisciplinary approach. Much of the recent focus in MIC research has been on the microbiological and chemical aspects, with less attention given to metallurgical attributes. Here, we address this knowledge gap by providing a critical synthesis of the literature on the metallurgical aspects of MIC of carbon steel, a material frequently associated with MIC failures and widely used in construction and infrastructure globally. The article begins by introducing the process of MIC, then progresses to explore the complexities of various metallurgical factors relevant to MIC in carbon steel. These factors include chemical composition, grain size, grain boundaries, microstructural phases, inclusions, and welds, highlighting their potential influence on MIC processes. This review systematically presents key discoveries, trends, and the limitations of prior research, offering some novel insights into the impact of metallurgical factors on MIC, particularly for the benefit of those already familiar with other aspects of MIC. The article concludes with recommendations for documenting metallurgical data in MIC research. An appreciation of relevant metallurgical attributes is essential for a critical assessment of a material’s vulnerability to MIC to advance research practices and to broaden the collective knowledge in this rapidly evolving area of study.

Development and validation of a photograph-based instrument to assess nutrition literacy: The NUTLY project

Most instruments measuring nutrition literacy evaluate theoretical knowledge, not necessarily reflecting skills relevant to food choices. We aimed to develop and validate a photograph-based instrument to assess nutrition literacy (NUTLY) among adults in Portugal. NUTLY assesses the ability to distinguish foods with different nutritional profiles; from each of several combinations of three photographs (two foods with similar contents and one with higher content) participants are asked to identify the food with the highest energy/sodium content. The NUTLY version with 79 combinations, obtained after experts/lay people evaluations, was applied to a sample representing different age, gender and education groups (n = 329). Dimensionality was evaluated through latent trait models. Combinations with negative or with positive small factor loadings were excluded after critical assessment. Internal consistency was measured using Cronbach's alpha and construct validity by comparing NUTLY scores with those obtained in the Medical Term Recognition Test and the Newest Vital Sign (NVS), and across education and training in nutrition/health groups. The cut-off to distinguish adequate/inadequate nutrition literacy was defined through ROC analysis using the Youden index criterion, after performing a Latent class analysis which identified a two-class model to have the best goodness of fit. Test-retest reliability was assessed after one month (n = 158). The final NUTLY scale was unidimensional and included 48 combinations (energy: 33; sodium: 15; α = 0.74). Mean scores (±standard deviation) were highest among nutritionists (39.9 ± 4.4), followed by health professionals (38.5 ± 4.1) and declined with decreasing education (p < 0.001). Those with adequate nutrition literacy according to NVS showed higher NUTLY scores (37.9 ± 4.3 vs. 33.9 ± 6.9, p < 0.001). Adequate nutrition literacy was defined as a NUTLY score≥35 (sensitivity: 89.3%; specificity: 93.7%). Test-retest reliability was high (ICC = 0.77). NUTLY is a valid and reliable nutrition literacy measurement tool.

Исследования в парадигме сокрытия информации: оценка фМРТ-данных на групповом уровне

&lt;p&gt;This work presents the results of studying the evaluation of fMRI data at the group level. Some factors influencing the formation of such estimates are shown. On the basis of experimental data obtained in forensic tests in the information concealment paradigm, attention is drawn to the need for a critical assessment of the results and methodology (design) of research, based on the results of which the outcome of the analysis of fMRI data at the group level are presented.&lt;/p&gt;

An ontology-driven model for hospital equipment maintenance management: a case study

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

Apoptosis in Postmortal Tissues of Goat Spinal Cords and Survival of Resident Neural Progenitors.

Growing demand for therapeutic tissue repair recurrently focusses scientists' attention on critical assessment of postmortal collection of live cells, especially stem cells. Our study aimed to assess the survival of neuronal progenitors in postmortal spinal cord and their differentiation potential. Postmortal samples of spinal cords were obtained from human-sized animals (goats) at 6, 12, 24, 36, and 54 h after slaughter. Samples were studied by immunohistology, differentiation assay, Western blot and flow cytometry for the presence and location of GD2-positive neural progenitors and their susceptibility to cell death. TUNEL staining of the goat spinal cord samples over 6-54 h postmortem revealed no difference in the number of positive cells per cross-section. Many TUNEL-positive cells were located in the gray commissure around the central canal of the spinal cord; no increase in TUNEL-positive cells was recorded in either posterior or anterior horns of the gray matter where many GD2-positive neural progenitors can be found. The active caspase 3 amount as measured by Western blot at the same intervals was moderately increasing over time. Neuronal cells were enriched by magnetic separation with antibodies against CD24; among them, the GD2-positive neural progenitor subpopulation did not overlap with apoptotic cells having high pan-caspase activity. Apoptotic cell death events are relatively rare in postmortal spinal cords and are not increased in areas of the neural progenitor cell's location, within measured postmortal intervals, or among the CD24/GD2-positive cells. Data from our study suggest postmortal spinal cords as a valuable source for harvesting highly viable allogenic neural progenitor cells.

Practical work as a means of implementing an activity-based approach to teaching biology in high school at the profile level

The article deals with the problem of forming subject competence in the application of an activity-based approach to the study of biology in high school. Therefore, the purpose of this article is to identify and substantiate specific examples of the application of the activity approach in the course of practical work in biology in the 10th grade of high school at the specialized level. It is shown that the current stage is preparatory to the implementation of the education reform in Ukraine (New Ukrainian School) in high school. Research methods: theoretical (analysis and synthesis, generalization, comparison, etc.), which were used to identify the peculiarities of using practical work in high school biology lessons; empirical (pedagogical observation) - to finalize the conclusions and prospects for further research. It is indicated that practical work in biology, provided in the school curriculum for grades 10-11 at the specialized level, provides ample opportunities for the formation of skills and abilities necessary for life in modern society. The article identifies the topics of specific practical works from the general list of them in the school curriculum in the topics "Introduction" and "Biodiversity". The possible ways and methodology of their implementation for the effective formation of subject competence in biology are shown. The implementation of these practical works involves the analytical activity of students, their identification of important components in the general array of biological information, the use of various sources and means to obtain information with the subsequent critical assessment of their reliability. It is recommended to use a number of modern mobile applications that simplify the process of plant identification - Plant Identifier &amp; Care, Planti: Plant Care, Lovely: plants care journal, etc. The article notes that the practical work "Calculation of basic statistical indicators for comparing the variability of biological objects" is the basis for the formation of critical thinking skills and the application of the method of scientific knowledge, which is defined in the school curriculum as the expected learning outcomes. The author emphasizes the expediency of using the group method of teaching during practical work not only for the effective implementation of the activity approach, but also for the formation of students' communicative competence. The examples and methodology of performing certain practical works given in the article will help teachers to effectively use the activity approach to achieve the expected results of teaching biology to high school students at the profile level.