Dataset Information

Reconstruction and analysis of a deciduous sapling using digital photographs or terrestrial-LiDAR technology.

ABSTRACT: BACKGROUND AND AIMS:To meet the increasing need for rapid and non-destructive extraction of canopy traits, two methods were used and compared with regard to their accuracy in estimating 2-D and 3-D parameters of a hybrid poplar sapling. METHODS:The first method consisted of the analysis of high definition photographs in Tree Analyser (TA) software (PIAF-INRA/Kasetsart University). TA allowed the extraction of individual traits using a space carving approach. The second method utilized 3-D point clouds acquired from terrestrial light detection and ranging (T-LiDAR) scans. T-LiDAR scans were performed on trees without leaves to reconstruct the lignified structure of the sapling. From this skeleton, foliage was added using simple modelling rules extrapolated from field measurements. Validation of the estimated dimension and the accuracy of reconstruction was then achieved by comparison with an empirical data set. KEY RESULTS:TA was found to be slightly less precise than T-LiDAR for estimating tree height, canopy height and mean canopy diameter, but for 2-D traits both methods were, however, fully satisfactory. TA tended to over-estimate total leaf area (error up to 50 %), but better estimates were obtained by reducing the size of the voxels used for calculations. In contrast, T-LiDAR estimated total leaf area with an error of <6 %. Finally, both methods led to an over-estimation of canopy volume. With respect to this trait, T-LiDAR (14·5 % deviation) greatly surpassed the accuracy of TA (up to 50 % deviation), even if the voxels used were reduced in size. CONCLUSIONS:Taking into account their magnitude of data acquisition and analysis and their accuracy in trait estimations, both methods showed contrasting potential future uses. Specifically, T-LiDAR is a particularly promising tool for investigating the development of large perennial plants, by itself or in association with plant modelling.

SUBMITTER: Delagrange S

PROVIDER: S-EPMC3189831 | biostudies-other | 2011 Oct

REPOSITORIES: biostudies-other

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Reconstruction and analysis of a deciduous sapling using digital photographs or terrestrial-LiDAR technology.

Delagrange Sylvain S Rochon Pascal P

Annals of botany. 20110422 6

<h4>Background and aims</h4>To meet the increasing need for rapid and non-destructive extraction of canopy traits, two methods were used and compared with regard to their accuracy in estimating 2-D and 3-D parameters of a hybrid poplar sapling.<h4>Methods</h4>The first method consisted of the analysis of high definition photographs in Tree Analyser (TA) software (PIAF-INRA/Kasetsart University). TA allowed the extraction of individual traits using a space carving approach. The second method util ...[more]

PMID: 21515607

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Project description:INTRODUCTION:The objective of this study was to examine changes in hemoglobin A1c (HbA1c), anti-diabetic medication use, insulin resistance, and other ambulatory glucose profile metrics between baseline and after 90 days of participation in the Twin Precision Nutrition (TPN) Program enabled by Digital Twin Technology. METHODS:This was a retrospective study of patients with type 2 diabetes who participated in the TPN Program and had at least 3 months of follow-up. The TPN machine learning algorithm used daily continuous glucose monitor (CGM) and food intake data to provide guidelines that would enable individual patients to avoid foods that cause blood glucose spikes and to replace them with foods that do not produce spikes. Physicians with access to daily CGM data titrated medications and monitored patient conditions. RESULTS:Of the 89 patients who initially enrolled in the TPN Program, 64 patients remained in the program and adhered to it for at least 90 days; all analyses were performed on these 64 patients. At the 90-day follow-up assessment, mean (±?standard deviation) HbA1c had decreased from 8.8?±?2.2% at baseline by 1.9 to 6.9?±?1.1%, mean weight had decreased from 79.0?±?16.2 kg at baseline to 74.2?±?14.7 kg, and mean fasting blood glucose had fallen from 151.2 ±?45.0 mg/dl at baseline to 129.1?±?36.7 mg/dl. Homeostatic model assessment of insulin resistance (HOMA-IR) had decreased by 56.9% from 7.4?±?3.5 to 3.2?±?2.8. At the 90-day follow-up assessment, all 12 patients who were on insulin had stopped taking this medication; 38 of the 56 patients taking metformin had stopped metformin; 26 of the 28 patients on dipeptidyl peptidase-4 (DPP-4) inhibitors discontinued DPP-4 inhibitors; all 13 patients on alpha-glucosidase inhibitors discontinued these inhibitors; all 34 patients on sulfonylureas were able to stop taking these medications; two patients stopped taking pioglitazone; all ten patients on sodium-glucose cotransporter-2 (SGLT2) inhibitors stopped taking SGLT2 inhibitors; and one patient stopped taking glucagon-like peptide-1 analogues. CONCLUSION:The results provide evidence that daily precision nutrition guidance based on CGM, food intake data, and machine learning algorithms can benefit patients with type 2 diabetes. Adherence for 3 months to the TPN Program resulted in patients achieving a 1.9 percentage point decrease in HbA1c, a 6.1% drop in weight, a 56.9% reduction in HOMA-IR, a significant decline in glucose time below range, and, in most patients, the elimination of diabetes medication use.

Project description:IntroductionThe world is awash with claims about the effects of health interventions. Many of these claims are untrustworthy because the bases are unreliable. Acting on unreliable claims can lead to waste of resources and poor health outcomes. Yet, most people lack the necessary skills to appraise the reliability of health claims. The Informed Health Choices (IHC) project aims to equip young people in Ugandan lower secondary schools with skills to think critically about health claims and to make good health choices by developing and evaluating digital learning resources. To ensure that we create resources that are suitable for use in Uganda's secondary schools and can be scaled up if found effective, we conducted a context analysis. We aimed to better understand opportunities and barriers related to demand for the resources, how the learning content overlaps with existing curriculum and conditions in secondary schools for accessing and using digital resources, in order to inform resource development.MethodsWe used a mixed methods approach and collected both qualitative and quantitative data. We conducted document analyses, key informant interviews, focus group discussions, school visits, and a telephone survey regarding information communication and technology (ICT). We used a nominal group technique to obtain consensus on the appropriate number and length of IHC lessons that should be planned in a school term. We developed and used a framework from the objectives to code the transcripts and generated summaries of query reports in Atlas.ti version 7.FindingsCritical thinking is a key competency in the lower secondary school curriculum. However, the curriculum does not explicitly make provision to teach critical thinking about health, despite a need acknowledged by curriculum developers, teachers and students. Exam oriented teaching and a lack of learning resources are additional important barriers to teaching critical thinking about health. School closures and the subsequent introduction of online learning during the COVID-19 pandemic has accelerated teachers' use of digital equipment and learning resources for teaching. Although the government is committed to improving access to ICT in schools and teachers are open to using ICT, access to digital equipment, unreliable power and internet connections remain important hinderances to use of digital learning resources.ConclusionsThere is a recognized need for learning resources to teach critical thinking about health in Ugandan lower secondary schools. Digital learning resources should be designed to be usable even in schools with limited access and equipment. Teacher training on use of ICT for teaching is needed.

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Project description:BackgroundDigital technology and media use is integral to adolescents' lives and has been associated with both positive and negative health consequences. Previous studies have largely focused on understanding technology behaviors and outcomes within adolescent populations, which can promote assumptions about adolescent technology use as homogeneous. Furthermore, many studies on adolescent technology use have focused on risks and negative outcomes. To better understand adolescent digital technology use, we need new approaches that can assess distinct profiles within study populations and take a balanced approach to understanding the risks and benefits of digital technology use.ObjectiveThe purpose of this study was to identify profiles of adolescent technology use within a large study population focusing on four evidence-based constructs: technology ownership and use, parental involvement, health outcomes, and well-being indicators.MethodsAdolescent-parent dyads were recruited for a cross-sectional web-based survey using the Qualtrics (Qualtrics International, Inc) platform and panels. Technology use measures included ownership of devices, social media use frequency, and the Adolescents' Digital Technology Interactions and Importance scale. Parent involvement measures included household media rules, technology-related parenting practices, parent social media use frequency, and the parent-child relationship. Health outcome measures included physical activity, sleep, problematic internet use, and mental health assessments. Well-being indicators included mental wellness, communication, and empathy. We used latent class analysis (LCA) to identify distinct profile groups across the aforementioned 4 critical constructs.ResultsAmong the 3981 adolescent-parent dyads recruited, adolescent participants had a mean age of 15.0 (SD 1.43) years; a total of 46.3% (1842/3981) were female, 67.8% (2701/3981) were White, and 75% (2986/3981) lived in a household with an income above the poverty line. The LCA identified 2 discrete classes. Class 1 was made up of 62.8% (2501/3981) of the participants. Class 1 participants were more likely than Class 2 participants to report family-owned devices, have lower technology importance scores, have household technology rules often centered on content, have positive parent relationships and lower parent social media use, and report better health outcomes and well-being indicators.ConclusionsFindings from this national cross-sectional survey using LCA led to 2 distinct profile groups of adolescent media use and their association with technology use and parent involvement as well as health and well-being outcomes. The two classes included a larger Class 1 (Family-Engaged Adolescents) and a smaller Class 2 (At-Risk Adolescents). The findings of this study can inform interventions to reinforce positive technology use and family support.

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Dataset Information

Reconstruction and analysis of a deciduous sapling using digital photographs or terrestrial-LiDAR technology.

Publications

Reconstruction and analysis of a deciduous sapling using digital photographs or terrestrial-LiDAR technology.

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