Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.The vast size of oil palm (Elaeis guineensis) plantations has led to lightweight unmanned aerial vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution. If acquired with sufficient overlap, aerial data from UAVs can be processed within structure-from-motion (SfM) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted. Using lightweight UAVs we captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 31 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and >75% image overlap. Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms). Stem heights could be predicted from point cloud derived metrics with root-mean-square errors (RMSEs) of 0.27 m (R2= 0.63) for 7 year old and 0.45 m (R2=0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (>75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs. We conclude that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.European CommissionMarie Skłodowska-Curi

A unified approach for the solution of the Fokker-Planck equation

This paper explores the use of a discrete singular convolution algorithm as a unified approach for numerical integration of the Fokker-Planck equation. The unified features of the discrete singular convolution algorithm are discussed. It is demonstrated that different implementations of the present algorithm, such as global, local, Galerkin, collocation, and finite difference, can be deduced from a single starting point. Three benchmark stochastic systems, the repulsive Wong process, the Black-Scholes equation and a genuine nonlinear model, are employed to illustrate the robustness and to test accuracy of the present approach for the solution of the Fokker-Planck equation via a time-dependent method. An additional example, the incompressible Euler equation, is used to further validate the present approach for more difficult problems. Numerical results indicate that the present unified approach is robust and accurate for solving the Fokker-Planck equation.Comment: 19 page

Fluvial organic carbon fluxes from oil palm plantations on tropical peatland

Intact tropical peatlands are dense long-term stores of carbon. However, the future security of these ecosystems is at risk from land conversion and extensive peatland drainage. This can enhance peat oxidation and convert long-term carbon sinks into significant carbon sources. In Southeast Asia, the largest land use on peatland is for oil palm plantation agriculture. Here, we present the first annual estimate of exported fluvial organic carbon in the drainage waters of four peatland oil palm plantation areas in Sarawak, Malaysia. Total organic carbon (TOC) fluxes from the plantation second- and third-order drains were dominated (91 %) by dissolved organic carbon (DOC) and ranged from 34.4 ± 9.7 C m−2 yr−1 to 57.7 %, 16.3 g C m−2 yr−1 (± 95 % confidence interval). These fluxes represent a single-year survey which was strongly influenced by an El Ninõ event and therefore lower discharge than usual was observed. The magnitude of the flux was found to be influenced by water table depth, with higher TOC fluxes observed from more deeply drained sites. Radiocarbon dating on the DOC component indicated the presence of old (pre-1950s) carbon in all samples collected, with DOC at the most deeply drained site having a mean age of 735 years. Overall, our estimates suggest fluvial TOC contributes ∼ 5 % of total carbon losses from oil palm plantations on peat. Maintenance of high and stable water tables in oil palm plantations appears to be key to minimising TOC losses. This reinforces the importance of considering all carbon loss pathways, rather than just CO2 emissions from the peat surface, in studies of tropical peatland land conversion

Design patterns for the development of electronic health record-driven phenotype extraction algorithms

AbstractBackgroundDesign patterns, in the context of software development and ontologies, provide generalized approaches and guidance to solving commonly occurring problems, or addressing common situations typically informed by intuition, heuristics and experience. While the biomedical literature contains broad coverage of specific phenotype algorithm implementations, no work to date has attempted to generalize common approaches into design patterns, which may then be distributed to the informatics community to efficiently develop more accurate phenotype algorithms.MethodsUsing phenotyping algorithms stored in the Phenotype KnowledgeBase (PheKB), we conducted an independent iterative review to identify recurrent elements within the algorithm definitions. We extracted and generalized recurrent elements in these algorithms into candidate patterns. The authors then assessed the candidate patterns for validity by group consensus, and annotated them with attributes.ResultsA total of 24 electronic Medical Records and Genomics (eMERGE) phenotypes available in PheKB as of 1/25/2013 were downloaded and reviewed. From these, a total of 21 phenotyping patterns were identified, which are available as an online data supplement.ConclusionsRepeatable patterns within phenotyping algorithms exist, and when codified and cataloged may help to educate both experienced and novice algorithm developers. The dissemination and application of these patterns has the potential to decrease the time to develop algorithms, while improving portability and accuracy

Early increase in single-kidney glomerular filtration rate after living kidney donation predicts long-term kidney function

Single-kidney glomerular filtration rate (GFR) increases after living kidney donation due to compensatory hyperfiltration and structural changes. The implications of inter-individual variability in this increase in single-kidney GFR are unknown. Here, we aimed to identify determinants of the increase in single-kidney GFR at three-month postdonation, and to investigate its relationship with long-term kidney function. In a cohort study in 1024 donors, we found considerable inter-individual variability of the early increase in remaining single-kidney estimated GFR (eGFR) (median [25th-75th percentile]) 12 [8-18] mL/min/1.73m(2). Predonation eGFR, age, and cortical kidney volume measured by CT were the main determinants of the early postdonation increase in single-kidney eGFR. Individuals with a stronger early increase in single-kidney eGFR had a significantly higher five-year postdonation eGFR, independent of predonation eGFR and age. Addition of the postdonation increase in single-kidney eGFR to a model including predonation eGFR and age significantly improved prediction of a five-year postdonation eGFR under 50 mL/min/1.73m(2). Results at ten-year follow-up were comparable, while accounting for left-right differences in kidney volume did not materially change the results. Internal validation using 1251-iothalamate-based measured GFR in 529 donors and external validation using eGFR data in 647 donors yielded highly similar results. Thus, individuals with a more pronounced increase in single-kidney GFR had better long-term kidney function, independent of predonation GFR and age. Hence, the early postdonation increase in single-kidney GFR, considered indicative for kidney reserve capacity, may have additional value to eGFR and age to personalize follow-up intensity after living kidney donation

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Architecting the IoT Paradigm: A Middleware for Autonomous Distributed Sensor Networks

Actualizing Internet of Things undoubtedly constitutes a major challenge of modern computing and is a promising next step in realizing the unification of all seamlessly interacting entities, either human users or participating machines, under a shared, coherent architecture. While it has now become common belief that the related solutions should be based on compatible network infrastructure employing widely accepted communication schemes, the specifics of the intermediate system that would act as global interface for all involved “things” are yet to be determined. A rising trend to define such machine-based entities is through cyber-physical systems, in terms of collaborating elements with physical input and output. Certainly, sensor networks constitute the most representative realization of such systems. Taking these issues and opportunities under consideration, this work proposes a bioinspired distributed architecture for an Internet of Things that exhibits self-organization properties to enable efficient interaction between entities modeled as cyber-physical systems, mainly focusing on sensor networks. Furthermore, a middleware has been implemented according to the proposed architecture, which serves the role of the backbone of this network as a multiagent and autonomous distributed system. The evaluation results demonstrate the self-optimization properties of the introduced scheme and indicate global network convergence

The Role of Protein Kinase A Regulation of the E6 PDZ-Binding Domain during the Differentiation-Dependent Life Cycle of Human Papillomavirus Type 18

Human papillomavirus (HPV) E6 proteins of high-risk alpha types target a select group of PSD95/DLG1/ZO1 (PDZ) domain-containing proteins by using a C-terminal PDZ-binding motif (PBM), an interaction that can be negatively regulated by phosphorylation of the E6 PBM by protein kinase A (PKA). Here, we have mutated the canonical PKA recognition motif that partially overlaps with the E6 PBM in the HPV18 genome (E6153PKA) and compared the effect of this mutation on the HPVl8 life cycle in primary keratinocytes with the wild-type genome and with a second mutant genome that lacks the E6 PBM (E6ΔPDZ). Loss of PKA recognition of E6 was associated with increased growth of the genome-containing cells relative to cells carrying the wild-type genome, and upon stratification, a more hyperplastic phenotype, with an increase in the number of S-phase competent cells in the upper suprabasal layers, while the opposite was seen with the E6ΔPDZ genome. Moreover, the growth of wild-type genome-containing cells was sensitive to changes in PKA activity, and these changes were associated with increased phosphorylation of the E6 PBM. In marked contrast to E6ΔPDZ genomes, the E6153PKA mutation exhibited no deleterious effects on viral genome amplification or expression of late proteins. Our data suggest that the E6 PBM function is differentially regulated by phosphorylation in the HPV18 life cycle. We speculate that perturbation of protein kinase signaling pathways could lead to changes in E6 PBM function, which in turn could have a bearing on tumor promotion and progression

A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores(PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.Results: The integrated polygenic prediction improved the average performance (pseudo-R2)for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity","type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension",and "sleep apnea" reaching phenome-wide significance.Conclusions: Our study has expanded the methodological utility of PRS in patient stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICDCM, International Classification of Diseases, Clinical Modification; LD, linkage disequilibrium;MA, multi-ancestry; MAF, minor allele frequency; NIH, National Institutes of Health; PCA,principal component analysis; PheWAS, phenome-wide association study; PCOS, polycysticovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk sc