BACKGROUND AND OBJECTIVE: The Grading of Recommendations, Assessment, Development and Evaluations (GRADE)-ADOLOPMENT methodology has been widely used to adopt, adapt, or de novo develop recommendations from existing or new guideline and evidence synthesis efforts. The objective of this guidance is to refine the operationalization for applying GRADE-ADOLOPMENT. METHODS: Through iterative discussions, online meetings, and email communications, the GRADE-ADOLOPMENT project group drafted the updated guidance. We then conducted a review of handbooks of guideline-producing organizations, and a scoping review of published and planned adolopment guideline projects. The lead authors refined the existing approach based on the scoping review findings and feedback from members of the GRADE working group. We presented the revised approach to the group in November 2022 (approximately 115 people), in May 2023 (approximately 100 people), and twice in September 2023 (approximately 60 and 90 people) for approval. RESULTS: This GRADE guidance shows how to effectively and efficiently contextualize recommendations using the GRADE-ADOLOPMENT approach by doing the following: (1) showcasing alternative pathways for starting an adolopment effort; (2) elaborating on the different essential steps of this approach, such as building on existing evidence-to-decision (EtDs), when available or developing new EtDs, if necessary; and (3) providing examples from adolopment case studies to facilitate the application of the approach. We demonstrate how to use contextual evidence to make judgments about EtD criteria, and highlight the importance of making the resulting EtDs available to facilitate adolopment efforts by others. CONCLUSION: This updated GRADE guidance further operationalizes the application of GRADE-ADOLOPMENT based on over 6 years of experience. It serves to support uptake and application by end users interested in contextualizing recommendations to a local setting or specific reality in a short period of time or with limited resources.

• Klíčová slova
• Adaptation, Adolopment, Adoption, Contextualization, GRADE, Global health, Guidelines, Research waste, Systematic reviews,
• Publikační typ
• časopisecké články MeSH

This article reviews recent literature focused on the factors associated with the reactions of youth bystanders to cyberaggression. The studies examined factors from three topical areas. The first area concerns the role of individual factors, with specific focus on the role of moral disengagement. The second area concerns the contextual factors and the applicability of the bystander intervention model, the bystander effect in the online environment, and the severity of the incident. The third area comprises social factors, with focus on the effect of class and school, and the interactions between individual and social levels. Taking into the reviewed literature, recommendations are formulated regarding the measurement of cyberbystander reactions, the need to capture contextual factors, and the examination of social factors.

• MeSH
• kyberšikana * MeSH
• lidé MeSH
• mladiství MeSH
• mravy MeSH
• šikana * MeSH
• školy MeSH
• sociální faktory MeSH
• Check Tag
• lidé MeSH
• mladiství MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

BACKGROUND: Unsafe management of human faecal waste represents a major risk for public health, particularly in low- and middle-income countries. Efforts to improve sanitation conditions are considerably sensitive to contextual specifics of natural and social environments. This review operationalises, analyses, and synthesises evidence of how contextual factors and motivations affect different sanitation outcomes with a specific focus on community approaches to rural sanitation. METHODS AND FINDINGS: We operationalised contextual factors and motivations as determinants that influence sanitation conditions independently of the examined intervention. We conducted a systematic search of both peer-reviewed and grey literature with no restriction on the methods After screening the titles and abstracts of 19,198 records obtained through initial searches, we scrutinised the full content of 621 studies for relevance. While 102 of these studies qualified to be assessed for risk of bias and information content, ultimately, just 40 studies met our eligibility criteria. Of these 40 studies from 16 countries, 26 analysed specific interventions and 14 were non-interventional. None of the experimental studies reported the effects of contextual factors or motivations as operationalised in this study and only observational evidence was thus used in our review. We found that sanitation interventions are typically seen as the principal vehicles of change, the main instruments to fix 'deviant' behaviour or ensure access to infrastructure. The programmatic focus of this study on sanitation determinants that act independently of specific interventions questions this narrow understanding of sanitation dynamics. We identified 613 unique observations of quantitatively or qualitatively established relationships between certain contextual factors or motivations and 12 different types of sanitation outcomes. The sanitation determinants were classified into 77 typologically similar groups clustered into 12 broader types and descriptively characterised. We developed a graphical synthesis of evidence in the form of a network model referred to as the sanitation nexus. The sanitation nexus depicts how different groups of determinants interlink different sanitation outcomes. It provides an empirically derived conceptual model of sanitation with an aggregate structure indicating similarities and dissimilarities between sanitation outcomes with respect to how their sets of underlying determinants overlap. CONCLUSION: This study challenged the understanding of context as merely something that should be controlled for. Factors that affect targeted outcomes independently of the analysed interventions should be scrutinised and reported. This particularly applies to interventions involving complex human-environment interactions where generalisability is necessarily indirect. We presented a novel approach to comprehending the contextual factors and motivations which influence sanitation outcomes. Our approach can be analogously applied when mapping and organising underlying drivers in other areas of public and environmental health. The sanitation nexus derived in this study is designed to inform practitioners and researchers about sanitation determinants and the outcomes they influence.

• Klíčová slova
• Community sanitation, Context, Intervention, Motivations, Rural sanitation, Systematic review,
• MeSH
• lidé MeSH
• motivace MeSH
• nakládání s odpady etika   metody   normy   MeSH
• rozvojové země MeSH
• sanitace normy   MeSH
• socioekonomické faktory MeSH
• venkovské obyvatelstvo MeSH
• veřejné zdravotnictví ekonomika   normy   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• systematický přehled MeSH

The utilization of computer vision in smart farming is becoming a trend in constructing an agricultural automation scheme. Deep learning (DL) is famous for the accurate approach to addressing the tasks in computer vision, such as object detection and image classification. The superiority of the deep learning model on the smart farming application, called Progressive Contextual Excitation Network (PCENet), has also been studied in our recent study to classify cocoa bean images. However, the assessment of the computational time on the PCENet model shows that the original model is only 0.101s or 9.9 FPS on the Jetson Nano as the edge platform. Therefore, this research demonstrates the compression technique to accelerate the PCENet model using pruning filters. From our experiment, we can accelerate the current model and achieve 16.7 FPS assessed in the Jetson Nano. Moreover, the accuracy of the compressed model can be maintained at 86.1%, while the original model is 86.8%. In addition, our approach is more accurate than ResNet18 as the state-of-the-art only reaches 82.7%. The assessment using the corn leaf disease dataset indicates that the compressed model can achieve an accuracy of 97.5%, while the accuracy of the original PCENet is 97.7%.

• Klíčová slova
• deep learning, model compression, progressive contextual excitation, pruning filters,
• MeSH
• automatizace MeSH
• farmy MeSH
• fyzikální jevy MeSH
• komprese dat * MeSH
• zemědělství * MeSH
• Publikační typ
• časopisecké články MeSH

Since the initial study on contextual interference (CI) in 1966, research has explored how practice schedules impact retention and transfer. Apart from support from scientists and practitioners, the CI effect has also faced skepticism. Therefore, we aimed to review the existing literature on the CI effect and determine how it affects transfer in laboratory and applied settings and in different age groups. We found 1,287 articles in the following databases: Scopus, EBSCO, Web of Science, ScienceDirect, supplemented by the Google Scholar search engine and manual search. Of 300 fully screened articles, 42 studies were included in the systematic review and 34 in the quantitative analysis (meta-analysis). The overall CI effect on transfer in motor learning was medium (SMD = 0.55), favoring random practice. Random practice was favored in the laboratory and applied settings. However, in laboratory studies, the medium effect size was statistically significant (SMD = 0.75), whereas, in applied studies, the effect size was small and statistically non-significant (SMD = 0.34). Age group analysis turned out to be significant only in adults and older adults. In both, the random practice was favored. In adults, the effect was medium (SMD = 0.54), whereas in older adults was large (SMD = 1.28). In young participants, the effect size was negligible (SMD = 0.12). Systematic review registration: https://clinicaltrials.gov/, identifier CRD42021228267.

• Klíčová slova
• blocked practice, contextual interference, motor learning, practice schedule, random practice, transfer,
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH

Current research has identified extensive changes in land-use structure and land management of Central European rural landscapes due to shifting political and economic trajectories. These changes are exemplified by diverse processes of agricultural intensification, privatization and land fragmentation, land abandonment and overall changes in modes of production. The extensive record of these historically specific processes has posed a fundamental challenge for rural landscape conservation, which is addressed in this paper. First, we identify the key contradictions in rural landscape conservation, which include (i) conservation based on cultural versus environmental/ecological values, (ii) conservation based on spatial landscape patterns versus individual landscape features, and (iii) conservation based on the preservation of past landscapes versus conservation that also address the processes of change. Subsequently, we use a case study of an existing open-air museum in Zubrnice, northern Czechia, to analyze and discuss these dichotomies. In this regional case study, we first contextualize the land-use/land-cover change (LULC) reconstructed from old maps using a thorough archive (documentary proxies) and field research (survey of agrarian terraces, clearance cairns, and remnants of orchards) that enabled the construction of a narrative of specific landscape structures and features. Finally, we integrate the collected data with a novel methodological approach that allow for the spatial identification of the landscape segments that represent the narratives of historical change in modes of rural production and are therefore suitable for integration within the existing open-air museum to improve its conservation and education status.

• Klíčová slova
• Cultural landscape, Land cover, Land use, Landscape biography, Natural heritage,
• MeSH
• ekologie MeSH
• ekosystém MeSH
• lidé MeSH
• venkovské obyvatelstvo MeSH
• zachování přírodních zdrojů * MeSH
• zemědělství * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The effect of practice schedule on retention and transfer has been studied since the first publication on contextual interference (CI) in 1966. However, strongly advocated by scientists and practitioners, the CI effect also aroused some doubts. Therefore, our objective was to review the existing literature on CI and to determine how it affects retention in motor learning. We found 1255 articles in the following databases: Scopus, EBSCO, Web of Science, PsycINFO, ScienceDirect, supplemented by the Google Scholar search engine. We screened full texts of 294 studies, of which 54 were included in the meta-analysis. In the meta-analyses, two different models were applied, i.e., a three-level mixed model and random-effects model with averaged effect sizes from single studies. According to both analyses, high CI has a medium beneficial effect on the whole population. These effects were statistically significant. We found that the random practice schedule in laboratory settings effectively improved motor skills retention. On the contrary, in the applied setting, the beneficial effect of random practice on the retention was almost negligible. The random schedule was more beneficial for retention in older adults (large effect size) and in adults (medium effect size). In young participants, the pooled effect size was negligible and statically insignificant.

• MeSH
• lidé MeSH
• motorické dovednosti * fyziologie   MeSH
• retence (psychologie) fyziologie   MeSH
• učení * fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• systematický přehled MeSH

BACKGROUND: Discovering gene interactions and their characterizations from biological text collections is a crucial issue in bioinformatics. Indeed, text collections are large and it is very difficult for biologists to fully take benefit from this amount of knowledge. Natural Language Processing (NLP) methods have been applied to extract background knowledge from biomedical texts. Some of existing NLP approaches are based on handcrafted rules and thus are time consuming and often devoted to a specific corpus. Machine learning based NLP methods, give good results but generate outcomes that are not really understandable by a user. RESULTS: We take advantage of an hybridization of data mining and natural language processing to propose an original symbolic method to automatically produce patterns conveying gene interactions and their characterizations. Therefore, our method not only allows gene interactions but also semantics information on the extracted interactions (e.g., modalities, biological contexts, interaction types) to be detected. Only limited resource is required: the text collection that is used as a training corpus. Our approach gives results comparable to the results given by state-of-the-art methods and is even better for the gene interaction detection in AIMed. CONCLUSIONS: Experiments show how our approach enables to discover interactions and their characterizations. To the best of our knowledge, there is few methods that automatically extract the interactions and also associated semantics information. The extracted gene interactions from PubMed are available through a simple web interface at https://bingotexte.greyc.fr/. The software is available at https://bingo2.greyc.fr/?q=node/22.

• Klíčová slova
• Data mining, Gene interactions, Information extraction, Natural language processing, Sequential pattern mining,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The hippocampal representation of space, formed by the collective activity of populations of place cells, is considered as a substrate of spatial memory. Alzheimer's disease (AD), a widespread severe neurodegenerative condition of multifactorial origin, typically exhibits spatial memory deficits among its early clinical signs before more severe cognitive impacts develop. OBJECTIVE: To investigate mechanisms of spatial memory impairment in a double transgenic rat model of AD. METHODS: In this study, we utilized 9-12-month-old double-transgenic TgF344-AD rats and age-matched controls to analyze the spatial coding properties of CA1 place cells. We characterized the spatial memory representation, assessed cells' spatial information content and direction-specific activity, and compared their population coding in familiar and novel conditions. RESULTS: Our findings revealed that TgF344-AD animals exhibited lower precision in coding, as evidenced by reduced spatial information and larger receptive zones. This impairment was evident in maps representing novel environments. While controls instantly encoded directional context during their initial exposure to a novel environment, transgenics struggled to incorporate this information into the newly developed hippocampal spatial representation. This resulted in impairment in orthogonalization of stored activity patterns, an important feature directly related to episodic memory encoding capacity. CONCLUSIONS: Overall, the results shed light on the nature of impairment at both the single-cell and population levels in the transgenic AD model. In addition to the observed spatial coding inaccuracy, the findings reveal a significantly impaired ability to adaptively modify and refine newly stored hippocampal memory patterns.

• Klíčová slova
• Alzheimer’s disease, TgF344-AD rats, place cell directionality, place field size, spatial memory,
• MeSH
• Alzheimerova nemoc * patofyziologie   MeSH
• amyloidový prekurzorový protein beta genetika   MeSH
• hipokampální oblast CA1 patofyziologie   MeSH
• hipokampus patofyziologie   MeSH
• krysa rodu Rattus MeSH
• lidé MeSH
• modely nemocí na zvířatech * MeSH
• poruchy paměti etiologie   patofyziologie   MeSH
• potkani inbrední F344 MeSH
• potkani transgenní * MeSH
• prostorová paměť fyziologie   MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• lidé MeSH
• mužské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• amyloidový prekurzorový protein beta MeSH

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.

• MeSH
• COVID-19 * epidemiologie   prevence a kontrola   MeSH
• fyzický odstup MeSH
• kontrola infekčních nemocí MeSH
• lidé MeSH
• pandemie MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH