The Danish Nationwide osteoporosis cohort trials environment (NOCTE) - a DXA dataset for the 1900-1960 birth cohort.

Understanding the challenges of using digital health technologies for menopause information.

Developmental factors in forensic assessments of children over the minimum age of criminal responsibility: A study of forensic reports in Norway 2013-2024.

Analysis of the diagnostic value of pre-event intracardiac electrogram (pre-EGM) storage function in cardiac electronic implantable devices for arrhythmia diagnosis.

By the early 2000s, chromatographic methods applying chiral stationary phases (CSPs) became the most effective techniques for the resolution of chiral compounds on both analytical and preparative scales. Nowadays, high-performance liquid chromatography (HPLC) employing various types of chiral selectors covalently bonded to silica-based supports, leading to the respective CSPs and the packed chromatographic columns thereof, offers a state-of-the-art methodology for enantioselective "chiral analysis." Although a large number of CSPs are commercially available, the design and development of new "chiral columns" are still mandatory since it is obvious that in practice one needs an even more well-balanced portfolio of different "chiral columns" to face the challenging tasks of enantiomeric resolutions. The development of the unique chiral anion, cation, and zwitterion exchangers achieved by Lindner and his partners serves as an expansion of the range of efficiently applicable CSPs.In this chapter, we present an updated and, in parts, text-wise renewed overview of our earlier chapter of this book series (Ilisz et al. (2019) Cinchona alkaloid-based zwitterionic chiral stationary phases applied for liquid chromatographic enantiomer separations: an overview. In: Scriba GKE (ed) Chiral seperations. Methods and protocols. Humana Press, pp 251-277. https://doi.org/10.1007/978-1-4939-9438-0 ) in discussing and summarizing direct enantiomer separations achieved by zwitterionic ion exchangers derived from Cinchona alkaloids, focusing on the literature published between 2018 and 2023. This article aims to provide comprehensive information on practical solutions for challenging enantiomer separations while discussing more generally the molecular recognition and methodological variables of these particular types of CSPs.

The aim of this study is to examine the experiences of pre-service teachers regarding their use of artificial intelligence. The study employs a phenomenological approach within this context. A total of 57 pre-service teachers from different branches and grade levels participated in the study, within the context of snowball and maximum diversity techniques for purposeful sampling. The data collected using semi-structured interview forms were subjected to content analysis. According to the results of the study, it was concluded that the most used artificial intelligence application is "ChatGPT". It has been determined that the main reasons participants use artificial intelligence are for research, homework, and seeking ideas. In the context of the theme of "achievements", the results obtained were comprehensive information, ease of life, time savings, guidance, speed enhancement, and practicality. Within the context of the theme of the feelings it evokes, it was found that users felt fast, happy, good, relaxed, and confident. In the context of the theme of feelings created by not using it, the following results were reached: inadequacy, unhappiness, anxiety, difficulty, bad, doubt, laziness, and stress. Within the theme of whether it is harmful, nearly half of the participants mentioned its harmful aspects, such as laziness, complacency, killing creativity, and dulling the sense of curiosity. Therefore, in the context of the study's findings, it is recommended that artificial intelligence literacy training be provided alongside the widespread dissemination of artificial intelligence education in all fields.

We present a detailed description of the craniodental and postcranial remains of the enigmatic Magerictis imperialensis, a basal Ailuridae previously known only from a single m2 from the Middle Miocene of the Madrid Basin (Spain). The discovery of new specimens from eight Middle Miocene (MN5–MN6) localities in Madrid has yielded comprehensive information on the dentition, mandibular morphology, and postcranial skeleton of this species. These new fossils provide an excellent opportunity to revise the phylogenetic relationships of Magerictis and related musteloids from the Oligocene and Miocene of Eurasia and North America. Our phylogenetic analysis indicates that Magerictis and Rothictis gen. nov. are the most basal members of the Aiuluridae. Both genera, together with Alopecocyon, Actiocyon, and Protursus, underwent a limited adaptive radiation during the Middle and Late Miocene in Eurasia and North America. This radiation potentially includes the ancestors of the most specialized ailurids (Simocyon, and the ailurines Parailurus, Pristinailurus and Ailurus). The postcranial elements attributed to Magerictis, particularly the astragalus and calcaneus, exhibit a morphological pattern comparable to that of Ailurus, thereby distinguishing it from other Musteloidea. A systematic rearrangement of the primitive Holartic musteloids from the Oligocene and Miocene is proposed. The Ailuridae are divided into three subfamilies: (i) Ailurinae for Ailurus, Parailurus and Pristinailurus; (ii) Simocyoninae, which includes Simocyon, Protursus, Alopecocyon and Actiocyon; and (iii) Magerictinae nov. rank for Magerictis and Rothictis gen. nov. (type species Rothictis wintershofensis comb. nov.). The family Amphictidae Winge, 1895 is revalidated for Amphictis and Bonisictis gen. nov. (type species Bonisictis ambiguus comb. nov.). https://zoobank.org/urn:lsid:zoobank.org:pub:15D893D5-0C61-4652-8919-DAEF998F3953

Although liquid crystalline materials are sometimes thought of as an odd state of matter, they have had a significant impact on contemporary technology. Liquid crystals, which have their origins in cholesterol derivatives, are a material that biomedical engineers and biologists deal with regularly. Since liquid crystal systems have distinct microstructures and physicochemical characteristics, they are garnering a lot of interest. Researchers and developers working in the formulation development field are increasingly choosing liquid crystals as their system of choice. A liquid crystal system can be used to efficiently deliver drugs to desired biological targets. Three factors contribute to the formation of liquid crystalline phases:chirality, micro segregation effects, and molecular shape. The purpose of this reviewwas to give an overview of the many types of liquid crystal systems and how they are prepared. Pharmaceutical liquid crystals and their significance in targeted drug delivery systems are to be scientifically highlighted in this review paper. This review's primary goal is to give comprehensive information on pharmaceutical liquid crystal technology, including the most recent and cutting-edge technological advancements.

Despite Indonesia's vast solar potential (207.8 GWp), its utilization remains far below the 2025 target (0.87 GW), hampered by unmapped industrial capabilities. This study addresses this gap by mapping the technological capabilities of Indonesia's photovoltaic (PV) industry and its supporting sectors using Technometric models. By adopting a quantitative survey methodology involving key industry players (n=3) representing the solar panel, cable, and mounting structure sectors , the study evaluated technoware (T), humanware (H), and infoware (I) components. The main results show that the solar panel industry has “excellent” technology content with a Technology Contribution Coefficient (TCC) of 0.7204 , driven by strong humanware and infoware contributions (both exceeding 70%). In contrast, cable companies, with a TCC of 0.5651 , are categorized as having “good” technology content, indicating a need to improve technoware, which only contributes 47%. Meanwhile, the structure (mounting) company also showed “very good” technological capability with a TCC of 0.7297. The TCC value, which ranges from 0 to 1, measures the contribution of technology in value-added creation, with values of 0.7-0.9 indicating an “excellent” category. The proposed recommendations include prioritizing technoware development through product design computing and test optimization, humanware improvement through innovation training, and infoware strengthening through comprehensive information dissemination. This study provides the first Technometric benchmark for the Indonesian PV sector, offering a clear diagnostic tool for policymakers and industry to enhance competitiveness and achieve sustainable growth targets.

Higher education plays a crucial role in determining one's future; however, many prospective students struggle to choose a major and university that align with their interests, talents, and future career prospects. A lack of comprehensive information and understanding of future job opportunities often leads to suboptimal choices. With the advancement of artificial intelligence (AI) and generative AI technology, decision-making in selecting majors and universities can be optimized using digital platforms that provide data-driven recommendations. Therefore, this community service program aims to assist prospective students in selecting the right major and university by using a web-based AI system. This community service program will be implemented over six months, with activities including application development, testing, outreach and implementation. The application will then be implemented among the target population, specifically students of grade X, SMA Negeri 17 Bandung. The program evaluation shows that the majority of students found the application engaging and easy to use, and that it significantly assisted them in determining suitable majors for their higher education.

Spectroscopic techniques are employed in analytical chemistry, biological analysis, and medicine. As technology has advanced, they have also been incorporated into other methods of analysis and estimation, such as elemental analysis, chromatography, and other identification tools. Microbes like cyanobacteria, the ancient oxygen-evolving photoautotrophs, have the ability to synthesize a wide variety of secondary metabolites and natural chemicals, according to advances in genome sequencing. When a secondary metabolite is produced in significant amounts, traditional structural elucidation and isolation procedures are reliable and effective; nevertheless, innovative methods are required for tiny quantities. High-performance liquid chromatography (HPLC), Fourier transform infrared spectroscopy (FTIR), electrospray ionization mass spectrometry (ESI-MS), Raman spectroscopy, and nuclear magnetic resonance spectroscopy (NMR) are the main methods used to investigate microbial and herbal secondary metabolites. Pre-separation of the metabolic components is necessary for MS and can be accomplished using analytical methods like GC or LC. Although specialized methods such arrayed electrochemical detection and FTIR have been employed in a number of situations, they also have distinct drawbacks. These days, metabolite identification from cell extracts or materials is typically done using HPLC and MS. For both pure chemicals and complicated combinations, NMR spectroscopy provides comprehensive information on molecular structure. Spectroscopy can identify these multifunctional molecules by providing additional information from the experimental data obtained for the characterization of secondary metabolites. Several recent developments in the characterization of microbial secondary metabolites using a variety of spectroscopic techniques, including UV-VIS spectroscopy, ESI-MS, FTIR, Raman spectroscopy, and NMR, are summarized in this work.

In Sciencefact perspective, the brain is not the origin or seat of consciousness, but rather the place where the effects of consciousness and awareness manifest. Given that T-Consciousness Fields can be tested practically, it is possible to design experiments to study the interaction between the brain and these non-physical fields. Unlike other mind-body influence methods, where individuals play an active role by performing techniques and exercises, the effect of T-Consciousness Fields begins with a brief and instantaneous attention. This crucial point has been experimentally confirmed in previous Sciencefact studies, which demonstrate the brain's role as a detector or revealer of consciousness. This will be explored further in upcoming issues of the Journal of Cosmointel, the first of which is now before you. In recent studies in this field, some of which are presented in this issue, more comprehensive information has been obtained regarding brain activity during interaction with the Faradarmani Consciousness Field, using EEG techniques and 128 electrodes, in the Faradarmangar population. In the first study, the total absolute power, or overall brain electrical activity, is analyzed. The second study compares absolute power across different frequency ranges, considering statistical parameters of the amplitude values. In the third study, the relative power of various frequency ranges is compared across different time segments. The fourth study examines changes in various types of entropy, previously explored in other T-Consciousness Field studies, with respect to brain electrical activity during the interaction. It is worth mentioning that the analysis of the activity levels and their correlation with different structural regions of the brain, the examination of functional connectivity between the involved brain regions, and the analysis of biological parameters such as heart rate, respiratory rate, and body temperature during the interaction with the Faradarmani Consciousness Field are still ongoing and will be addressed in a separate issue.

Long non-coding RNAs (lncRNAs) are non-coding transcripts that regulate various biological processes in many species, including insects. Some lncRNAs have been found to be associated with insecticide resistance. In this study, 4177 lncRNAs between the susceptive strain (RP-S) and the imidacloprid-resistant strain (RP-R) in Rhopalosiphum padi by transcriptome sequencing were recognized, including 2401 intergenic lncRNAs, 970 intron lncRNAs, and 806 antisense lncRNAs. Two hundred fifty-eight differentially expressed lncRNAs were observed, including 77 lncRNA transcripts that were upregulated, while 181 lncRNA transcripts were downregulated in RP-R compared to that in RP-S targeting 628 differentially expressed mRNAs. They could be involved in imidacloprid resistance by modulating the expression of 11 P450s, 2 CCEs, 6 UGTs, and 14 ABC transporters. Furthermore, quantitative PCR (qPCR), RNA interference (RNAi), and imidacloprid bioassay analyses demonstrated that overexpressed lnc8676, lnc36817, and lnc48853 were involved in imidacloprid resistance. This study provided comprehensive information on the lncRNAs profile and provided evidence that lncRNAs play a key role in conferring insecticide resistance to R. padi and have significant potential to be used as targets for pest control strategies.

Healthcare facilities at all levels of the system play a central role in the delivery of health services. However, comprehensive information on the capacity of healthcare facilities for surveillance, preparedness, and response at all system levels is lacking in Ethiopia. This study aims to bridge this gap and generate evidence to improve the PHEM system.

PurposeThis study aimed to evaluate the accuracy of ChatGPT’s responses to frequently asked questions (FAQs) about hamstring injuries and to determine if prompted, if ChatGPT could appropriately tailor the reading level to that suggested.MethodsA preliminary list of 15 questions was developed, from which the authors selected the 10 most encountered patient questions. Three queries were performed, inputting the questions into ChatGPT-4.0: 1) unprompted, naïve, 2) additional prompt specifying the response being tailored to a seventh grade reading level, 3) additional prompt specifying the response being tailored to a college graduate reading level. The responses from the unprompted query were independently evaluated by two of the authors. To assess the quality of the answers, a grading system was applied: (A) correct and sufficient response; (B) correct but insufficient response; (C) response containing both correct and incorrect information; and (D) incorrect response. Additionally, the readability of each response was measured using the Flesch-Kinkaid Reading Ease Score (FRES) and Grade Level (FKGL) scales.ResultsTen responses were evaluated. Inter-rater reliability was 0.6 regarding grading. Of the initial query, two out of ten responses received a grade of A, seven were graded as B, and one were graded as C. The average cumulative FRES and FKGL scores of the initial query was 61.64 and 10.28, respectively. The average cumulative FRES and FKGL scores of the secondary query were 75.2 and 6.1, respectively. Finally, the average FRES and FKGL scores of the third query were 12.08 and 17.23.ConclusionChatGPT demonstrated generally satisfactory accuracy in responding to questions regarding hamstring injuries, although certain responses lacked completeness or specificity. On initial, unprompted queries, the readability of responses aligned with a tenth-grade level. However, when explicitly prompted, ChatGPT reliably adjusted the complexity of its responses to both a seventh-grade and a graduate-level reading standard. These findings suggest that while ChatGPT may not consistently deliver fully comprehensive medical information, it possesses the capacity to adapt its output to meet specific readability targets.

Despite decades of research, cancer remains one of the biggest health challenges. Due to the intricate interplay between multiple factors and different cancer types, it is still impossible to pinpoint a common cause for all forms of cancer. Computational modeling can be helpful in integrating scattered information to derive comprehensive information about malignancy. We describe a discrete dynamic network model of a mitogen-activated protein kinase pathway consisting of 66 nodes and 95 edges. The network consists of five input signals (Fas ligand, DNA damage, insulin, tumor necrosis factor alpha and transforming growth factor beta) and three output nodes (proliferation, apoptosis and growth arrest). Using a random asynchronous update method andin siliconode perturbations, the accuracy of the model is ensured. The results of simulations and perturbations were in agreement with the gene knockout and constitutive expression studies reported in the literature, underscoring the high precision of the deduced comprehensive network. The fidelity of our model makes it useful to understand the etiology of malignancy. Both anti-cancer and pro-cancer roles have been attributed to DUSP1 in different forms of cancers and, in our model, DUSP1 knockout under insulin and DNA damage signaling was found to universally enhance the proportion of cells undergoing apoptosis (i.e. a pro-cancerous role), thus highlighting its potential in designing novel therapeutic interventions. Moreover, although MYC is a well-known oncogene, we found that MYC's overexpression can activate p53, a prominent anti-growth agent, through the p14 and MDM2 pathways.Implications:Our findings suggest a novel role of the DUSP1 and MYC genes in regulating cell proliferation.

RELEVANCE of the research lies in the need to improve the accuracy and reliability of information and measurement systems (AIS) in conditions of uncertainty and incompleteness of information. Modern AIS are being widely implemented in critical areas, where their effectiveness directly depends on their ability to cope with two types of uncertainty: stochastic (random) and epistemic (systemic). Classical methods of mathematical statistics have difficulty formalizing epistemic uncertainty, which makes it urgent to search for new approaches. THE PURPOSE of the work is to substantiate the effectiveness of using the Bayesian approach to solve problems of improving the accuracy of AIS in conditions of uncertainty. METHODS. The Bayesian approach to probability theory is applied, which treats probability as a measure of confidence. The key tool is Bayes' theorem, which allows combining a priori knowledge of magnitude with information from new experimental data to obtain a refined, a posteriori estimate. The article examines the application of the Bayesian approach to the typical problem of estimating a physical quantity, and discusses more complex models such as hierarchical Bayesian models and Bayesian networks. RESULTS. The Bayesian approach to probability theory is applied, which treats probability as a measure of confidence. The key tool is Bayes' theorem, which allows combining a priori knowledge of magnitude with information from new experimental data to obtain a refined, a posteriori estimate. The article examines the application of the Bayesian approach to the typical problem of estimating a physical quantity, and discusses more complex models such as hierarchical Bayesian models and Bayesian networks. results. Bayesian analysis allows us to obtain not just a point estimate, but a complete a posteriori probability distribution that contains comprehensive information about the measured value. This makes it possible to more adequately assess uncertainty and make decisions with minimal risk. Using the example of a typical task, it is shown that Bayesian information synthesis always leads to a decrease in uncertainty and an increase in estimation accuracy. The application of the Bayesian approach in the concept of Bayesian measurement intellectualization is discussed, which leads to the creation of adaptive AIS capable of continuously updating their internal models. CONCLUSION. Using the Bayesian approach is an effective and versatile strategy to improve the accuracy and reliability of an AIS. Despite the computational complexity and challenges associated with the choice of a priori distributions, this approach provides a unified theoretical framework for solving a wide range of problems. This opens up prospects for creating a new generation of AIS capable of efficiently working with heterogeneous data and providing the required quality of measurement information in difficult conditions.

Since the Genale Dawa River Basin has significant irrigation potential, a new project has been started to encourage irrigation extension. Nevertheless, these initiatives need accurate, site-specific data on past temperature and precipitation, two essential elements of climate variables. However, there is a lack of comprehensive site-specific information on these variables in the river basin. The main objective of this study was to investigate temperature and rainfall trends and variability during the past 30 years (1984–2014) at 15 selected stations. Accordingly, the observed historical rainfall data were assessed for the Coefficient of Variation (CV) and Precipitation Concentration Index (PCI). The trend and magnitude of change in rainfall and temperature were tested through the Mann-Kendall test and the Sen’s slope estimator, respectively. The results showed that the annual rainfall coefficient of variation is between 12.9% and 38.57%. The aggregate result of the stations’ Precipitation Concentration Index indicated that low concentration (5.1%), moderate concentration (45.3%), strong concentration (24.9%), and strong irregularity (24.7%) indicated spatial variability of precipitation. Annual and seasonal maximum and minimum temperatures have resulted in an increasing trend at all the stations. Generally, there was a spatially and temporally variable trend in rainfall, while the temperature trend was increasing irrespective of the temporal and spatial factors that have far-reaching consequences on the availability of water resources in the area, which requires the development of effective climate variability mitigation and adaptation measures in the study area.

ABSTRACT Tremendous amounts of generated municipal solid wastes (MSWs) and limited energy sources make utilization of sustainable waste management strategies mandatory. Lower efficiency of recycling practices especially in developing countries compels authorities to search for novel sustainable solutions. Biodrying is a promising technology for producing solid recovered fuel (SRF) from partially stabilized waste. However, accurate evaluation of biodrying performance remains challenging due to the complexity of microbial heat generation processes. On the other hand, basic biodrying indices are found to be insufficient for evaluating the overall efficiency. Multi-criteria decision-making techniques providing accurate and comprehensive information may serve as useful tools to interpret efficiency of such complex systems. The major aim of this work was to observe a meaningful ranking of biodrying efficiency by using AHP-PROMETHEE, considering all treatment and cost parameters. With this aim, first batch biodrying experiments were performed in a pilot-scale reactor with a volume of 0.8 m3 for a 7-day period. Eight trials including varying waste compositions weighing 70 ± 7 kg were tested under different air flow rates (AFR). Temperature and weight loss profiles together with moisture removal and leachate generation rates were determined for each of the trials. Energy contents (lower heating value (LHV)) were also analyzed after and before biodrying. The experimental results indicated that the higher food waste and lower AFR (Trial 4) increased volatile solids (VS) reductions along with the observed peak temperature (up to 60°C). In contrast, high AFR and low organic content (Trial 5), resulted in the lowest VS (6.86%) reduction and provided the highest biodrying index (6.76). The most crucial material in increasing the energy content of the waste matrix was plastic wastes with low biodegradability. As the proportion of plastic waste increased, the bioheat production capacity decreased and applied AFR became the most important factor in obtaining SRF (Trial 1). AHP results highlight energy content increase as the key factor (33% weight), while leachate production had minimal influence (6%). PROMETHEE ranked Trial 1 the first with the highest net flow value (Phi) of 0.32. Briefly, the study offers a novel approach to achieve an easy, informative, and accurate performance assessment for biodrying processes.

Introduction: Adolescence is a critical developmental stage often accompanied by psychological challenges, with anxiety being one of the most prevalent mental health issues among young people. Limited awareness and ineffective learning strategies contribute to poor understanding of anxiety, particularly among adolescent girls. This study aimed to examine the effect of e-book media on improving knowledge about anxiety and mental health among adolescent girls. Methods: A pre-experimental study with a one-group pretest–posttest design was conducted among 62 female students at Madrasah Aliyah Muhammadiyah Salaka, South Sulawesi, Indonesia. Data were collected using a validated questionnaire assessing knowledge of anxiety before and after the intervention. The educational e-book contained comprehensive information on the definition, causes, symptoms, prevention, and coping strategies related to anxiety. Data were analyzed using the Wilcoxon Signed Rank Test with SPSS version 29.0. Results: Before the intervention, 54.8% of respondents demonstrated low knowledge, while 4.8% had high knowledge. After exposure to the e-book, 74.2% reached the high knowledge level, and none remained in the low category. Statistical analysis showed a significant difference between pretest and posttest results (Z = -6.997, p = 0.000 < 0.05), confirming the effectiveness of e-book media in enhancing mental health knowledge. Conclusion: E-book media proved to be an effective and innovative educational tool for improving adolescents’ understanding of anxiety and mental health. Integrating digital learning materials into school-based programs can promote mental health literacy and support early prevention among youth populations.