Assessing biomass and architecture of tropical trees with terrestrial laser scanning

Over the last two decades, terrestrial light detection and ranging (LiDAR), also known as terrestrial laser scanning (TLS) has become a valuable tool in assessing the woody structure of trees, in a method that is accurate, non-destructive, and replicable. This technique provides the ability to scan an area, and utilizes specialized software to create highly detailed 3D point cloud representations of its surroundings. Although the original usage of LiDAR was for precision survey applications, researchers have begun to apply LiDAR to forest research. Tree metrics can be extracted from TLS tree point clouds, and in combination with structure modelling, can be used to extract tree volume, aboveground biomass (AGB), growth, species, and to understand ecological questions such as tree mechanics, branching architecture, and surface area. TLS can provide a robust and rapid assessment of tree characteristics. These characteristics will improve current global efforts to measure forest carbon emissions, understand their uncertainties, and provide new insight into tropical forest ecology. Thus, the main objective of this PhD is to explore the use of 3D models from terrestrial laser scanning point clouds to estimate biomass and architecture of tropical trees. TLS-derived biomass and TLS-derived architecture can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests. In this thesis, a dataset of forest inventory with TLS point clouds and destructive tree harvesting were created from three tropical regions: Indonesia, Guyana, and Peru. A total of 1858 trees were traditionally inventoried, 135 trees were TLS scanned, and 55 trees were destructively harvested. In this thesis, procedures to estimate tree metrics such as tree height (H), diameter at breast height (D), crown diameter (CD), and the length and diameter of individual branches were developed using 3D point clouds and 3D modelling. From these tree metrics, I infer AGB, develop allometric models, and estimate metabolic plant scaling of individual tropical trees. All these metrics are validated against a traditional forest inventory data and destructively harvested trees. Chapter 2 presents a procedure to estimate tree volume and quantify AGB for large tropical trees based on estimates of tree volume and basic wood density. The accurate estimation of AGB of large tropical trees (diameter > 70 cm) is particularly relevant due to their major influence on tropical forest AGB variation. Nevertheless, current allometric models have large uncertainties for large tree AGB, partly due to the relative lack of large trees in the empirical datasets used to create them. The key result of this chapter is that TLS and 3D modelling are able to provide individual large tree volume and AGB estimates that are less likely to be biased by tree size or structural irregularities, and are more accurate than allometric models. Chapter 3 focuses on the development of accurate local allometric models to estimate tree AGB in Guyana based solely on TLS-based tree metrics (H, CD, and D) and validated against destructive measurements. Current tropical forest AGB estimates typically rely on pantropical allometric models that are developed with relatively few large trees. This leads to large uncertainties with increasing tree size and often results in an underestimation of AGB for large trees. I showed in Chapter 2 that AGB of individual large trees can be estimated regardless of their size and architecture. This chapter evaluates the performance of my local allometric models against existing pantropical models and evidenced that inclusion of TLS-based metrics to build allometric models provides as good as, or even better, AGB estimates than current pantropical models. Chapter 4 provides an insight into the architecture and branching structure of tropical trees. In Chapter 2, I demonstrated the potential of TLS to characterize woody tree structure as a function of tree volume, but little is known regarding their detailed architecture. Previous studies have quantitatively described tree architectural traits, but they are limited to the intensity of quantifying tree structure in-situ with enough detail. Here, I analysed the length and diameter of individual branches, and compared them to reference measurements. I demonstrated that basic tree architecture parameters could be reconstructed from large branches (> 40 cm diameter) with sufficient accuracy. I also discuss the limitations found when modelling small branches and how future studies could use my results as a basis for understanding tree architecture. Chapter 5 describes an alternative approach to estimating metabolic scaling exponents using the branching architecture derived from TLS point clouds. This approach does not rely on destructive sampling and can help to increase data collection. A theory on metabolic scaling, the West, Brown & Enquist (WBE) theory, suggests that metabolic rate and other biological functions have their origins in an optimal branching system network (among other assumptions). This chapter demonstrates that architecture-based metabolic scaling can be estimated for big branches of tropical trees with some limitations and provides an alternative method that can be implemented for large-scale assessments and provides better understanding of metabolic scaling. The results from this thesis provide a scientific contribution to the current development of new methods using terrestrial LiDAR and 3D modelling in tropical forests. The results can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests. I encourage further testing of my work using more samples including other types of forests to reduce inherent uncertainties.</p

Tree biomass equations from terrestrial LiDAR : a case study in Guyana

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates (R-2 = 0.92-0.93) than traditional pantropical models (R-2 = 0.85-0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested (R-2 = 0.89) and predicted AGB accurately across all size classes-which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees

Covalent co-assembly between resilin-like polypeptide and peptide amphiphile into hydrogels with controlled nanostructure and improved mechanical properties

Covalent co-assembly holds great promise for the fabrication of hydrogels with controllable nanostructure, versatile chemical composition, and enhanced mechanical properties given its relative simplicity, high efficiency, and bond stability. This report describes our approach to designing functional multicomponent hydrogels based on photo-induced chemical interactions between an acrylamide-functionalized resilin-like polypeptide (RLP) and a peptide amphiphile (PA). Circular dichroism (CD) spectroscopy, electron microscopy, and amplitude sweep rheology were used to demonstrate that the co-assembled hydrogel systems acquired distinct structural conformations, tunable nanostructures, and enhanced elasticity in a PA concentration-dependent manner. We envisage the use of these materials in numerous biomedical applications such as controlled drug release systems, microfluidic devices, and scaffolds for tissue engineering

Valorización de Cementos Pacasmayo

Cementos Pacasmayo S.A.A. y sus subsidiarias son los productores, distribuidores y comercializadores de cemento más importante en el norte del Perú, cuenta con 3 plantas de producción en Pacasmayo, Rioja y Piura, con una capacidad de producción total anual de 4,9 millones de TM. Su participación de mercado es aproximadamente el 95% y tienen más de 200 puntos de venta. La valorización por el método de flujos de caja descontados (DCF) tiene como fecha base el 30 de diciembre del 2016. Entre los principales supuestos empleados se encuentra un periodo de proyección de 10 años, un crecimiento promedio de los ingresos de 4,20% en dicho periodo y una inversión en el aumento de la capacidad de planta por S/ 1.057 millones que se realizaría posterior al 2023. Además de los supuestos señalados se trabajó con un WACC en soles equivalente a 8,04% y con un crecimiento de los flujos de la perpetuidad (g) de 3,12%. El valor obtenido de la firma fue de S/ 3.592 millones, obteniéndose un valor fundamental de la acción común a diciembre 2016 era de S/ 6,65, que estaba 5,50% por encima del precio de mercado al cierre del 31 de diciembre de 2016 (acción común a S/ 6,30). Con el segundo método se valoriza sobre la base de la comparación con múltiplos de compañías similares a Cementos Pacasmayo. Se tomaron en cuenta los ratios P/E y EV/EBITDA, y se obtuvo como resultado los valores de S/ 6,63 y S/ 6,68, respectivamente. Estos valores se encuentran cerca del valor de S/ 6,65 obtenido a través del método de valorización de flujos. De acuerdo a los resultados obtenidos y al precio actual de la acción de Cementos Pacasmayo y subsidiarias, nuestra recomendación es mantener/comprar

The Discrete Representation of Continuously Moving Indeterminate Objects

AbstractTo incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM (1, 1) model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object's movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment