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Frontiers in Plant Science 2023Tree height (H) and stem diameter at breast height (DBH) (H-D) relationship is correlated with timber yield and quality as well as stability of forest and is crucial in...
Tree height (H) and stem diameter at breast height (DBH) (H-D) relationship is correlated with timber yield and quality as well as stability of forest and is crucial in forest management and genetic breeding. It is influenced by not only environmental factors such as site quality and climate factors but also genetic control that is mostly neglected. A dataset of H and DBH of 25 provenances of Buch.-Ham. ex D. Don at four sites was used to model the H-D relationship. The dummy variable nonliner mixed-effect equations were applied to evaluate the effects of sites and provenances on variations of the H-D relationship and to select superior provenances of . Weibull equation was selected as the base model for the H-D relationship. The sites affected asymptotes of the H-D curves, and the provenance effect on asymptotes of the H-D curves varied across sites. Taking above-average DBH and lower asymptote of the H-D curves as indicators, five excellent provenances were screened out at each site with a rate of 20%. Their selection gains of individual volume ranged from 1.99% to 29.81%, and their asymptote parameter () and H-D ratio were 7.17%-486.05% and 3.07-4.72% lower than the relevant total means at four sites, respectively. Genetic selection based on the H-D relationship could promote selection efficiency of excellent germplasms and was beneficial for the large-sized timber production of .
PubMed: 37849846
DOI: 10.3389/fpls.2023.1248278 -
Journal of Clinical Nursing Jul 2023To describe the quality of information coming from previous care units to palliative care.
AIMS AND OBJECTIVES
To describe the quality of information coming from previous care units to palliative care.
BACKGROUND
Information quality is an interconnected concept that includes different dimensions and can be viewed from different perspectives. More knowledge is needed from a multi-professional perspective on the information quality coming to palliative care.
DESIGN
Descriptive qualitative study.
METHODS
Altogether 33 registered nurses, practical nurses, social workers and physicians working in palliative care were purposively selected to participate in thematic interviews. The research was carried out in six palliative care units in three hospital districts. The data were analysed by using deductive and inductive content analysis. The COREQ checklist was used.
RESULTS
Three main categories with thirteen categories were identified in connection with the deductive analysis based on the Clinical Information Quality framework: (1). Informativeness of information coming from previous care units to palliative care included accuracy, completeness, interpretability, plausibility, provenance and relevance. (2). Availability of information coming from previous care units to palliative care included accessibility, portability, security and timeliness. (3). Usability of information coming from previous care units to palliative care included conformance, consistency and maintainability. Each category is divided into sub-categories followed by narratives of their content.
CONCLUSIONS
This study provides new knowledge on the quality of information coming to palliative care from a multi-professional perspective. Professionals working in palliative care units highlight issues describing good information quality, but also point out quality issues and areas for improvement.
RELEVANCE TO CLINICAL PRACTICE
The results can guide the development of documentation practices and Health Information System development as well as be used in the generation of a new audit instrument of information quality.
Topics: Humans; Palliative Care; Hospice and Palliative Care Nursing; Qualitative Research; Physicians; Narration
PubMed: 35844084
DOI: 10.1111/jocn.16453 -
BMJ (Clinical Research Ed.) Aug 2023
Topics: Humans; Physicians; Inservice Training; United Kingdom
PubMed: 37567589
DOI: 10.1136/bmj.p1826 -
BMJ (Clinical Research Ed.) Nov 2023
Topics: Female; Humans; Pregnancy; Quality of Health Care; State Medicine; England; Health Care Reform
PubMed: 37984976
DOI: 10.1136/bmj.p2700 -
BMJ (Clinical Research Ed.) Oct 2023
Topics: Humans; Tobacco Control; Smoking; Advertising; England
PubMed: 37832953
DOI: 10.1136/bmj.p2358 -
Isotopes in Environmental and Health... Aug 2023This paper presents a detailed review of the use of Sr/Sr isotope systematics for wine provenance studies. The method is based on the principle that the Sr isotope ratio... (Review)
Review
This paper presents a detailed review of the use of Sr/Sr isotope systematics for wine provenance studies. The method is based on the principle that the Sr isotope ratio in wine reflects that of the labile fraction of the vineyard soil from which the wine is produced. The review encompasses Sr/Sr data from wine samples published between 1993 and 2021 from terroirs in 22 different countries. The analytical procedures and techniques adopted by the different authors and the range of isotope ratios obtained in the different studies are discussed and evaluated. This study provides a bibliometric analysis of the Sr/Sr isotope approach for wine authentication at different scales. Although limitations are evident when implemented at large (global) scales, we demonstrate that the Sr/Sr isotope tracing technique remains a powerful and reliable tool for determining the geographical origin of wine when combined with detailed knowledge of the geological and soil characteristics of the substrata. For example, this combination of data allows the wines grown in the volcanic soils of Central and Southern Italy to be unambiguously fingerprinted. We present a detailed protocol for the application of the Sr isotope technique to wine authentication.
PubMed: 37593993
DOI: 10.1080/10256016.2023.2245122 -
Nature Communications Jul 2023Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite...
Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.
Topics: Chromatography, Liquid; Reproducibility of Results; Tandem Mass Spectrometry; Metabolomics
PubMed: 37433854
DOI: 10.1038/s41467-023-39889-1 -
Journal of Medical Internet Research Nov 2023In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data...
BACKGROUND
In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status.
OBJECTIVE
Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data.
METHODS
In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist.
RESULTS
Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies.
CONCLUSIONS
The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.
Topics: Humans; Data Management; Delivery of Health Care; Medical Informatics; Surveys and Questionnaires
PubMed: 37938878
DOI: 10.2196/48809 -
IEEE Transactions on Visualization and... Sep 2023In domains, such as agronomy or manufacturing, experts need to consider trade-offs when making decisions that involve several, often competing, objectives. Such analysis...
In domains, such as agronomy or manufacturing, experts need to consider trade-offs when making decisions that involve several, often competing, objectives. Such analysis is complex and may be conducted over long periods of time, making it hard to revisit. In this paper, we consider the use of analytic provenance mechanisms to aid experts recall and keep track of trade-off analysis. We implemented VisProm, a web-based trade-off analysis system, that incorporates in-visualization provenance views, designed to help experts keep track of trade-offs and their objectives. We used VisProm as a technology probe to understand user needs and explore the potential role of provenance in this context. Through observation sessions with three groups of experts analyzing their own data, we make the following contributions. We first, identify eight high-level tasks that experts engaged in during trade-off analysis, such as locating and characterizing interest zones in the trade-off space, and show how these tasks can be supported by provenance visualization. Second, we refine findings from previous work on provenance purposes such as recall and reproduce, by identifying specific objects of these purposes related to trade-off analysis, such as interest zones, and exploration structure (e.g., exploration of alternatives and branches). Third, we discuss insights on how the identified provenance objects and our designs support these trade-off analysis tasks, both when revisiting past analysis and while actively exploring. And finally, we identify new opportunities for provenance-driven trade-off analysis, for example related to monitoring the coverage of the trade-off space, and tracking alternative trade-off scenarios.
PubMed: 35507619
DOI: 10.1109/TVCG.2022.3171074 -
ArXiv Aug 2023The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works...
The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve performance via recently-developed "enhancement attacks" may be a greater threat to biomedical machine learning. In the spirit of developing attacks to better understand trustworthiness, we developed two techniques to drastically enhance prediction performance of classifiers with minimal changes to features: 1) general enhancement of prediction performance, and 2) enhancement of a particular method over another. Our enhancement framework falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities between original and enhanced data (Pearson's ' > 0.99). Similarly, the method-specific enhancement framework was effective in falsely improving the performance of one method over another. For example, a simple neural network outperformed logistic regression by 17% on our enhanced dataset, although no performance differences were present in the original dataset. Crucially, the original and enhanced data were still similar ( = 0.99). Our results demonstrate the feasibility of minor data manipulations to achieve any desired prediction performance, which presents an interesting ethical challenge for the future of biomedical machine learning. These findings emphasize the need for more robust data provenance tracking and other precautionary measures to ensure the integrity of biomedical machine learning research. Code is available at https://github.com/mattrosenblatt7/enhancement_EPIMI.
PubMed: 36713237
DOI: No ID Found