Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing highly accurate three dimensional and wall-to-wall forest structural characteristics. In this study, we evaluated the utility of standard metrics and canopy metrics derived from airborne LiDAR data for estimating plot-level forest structural parameters individually and in combination, over a subtropical forest in Yushan forest farm, southeastern China. Standard metrics, i.e., height-based and density-based metrics, and canopy metrics extracted from canopy vertical profiles, i.e., canopy volume profile (CVP), canopy height distribution (CHD), and foliage profile (FP), were extracted from LiDAR point clouds. Then the standard metrics and canopy metrics were used for estimating forest structural parameters individually and in combination by multiple regression models, including forest type-specific (coniferous forest, broad-leaved forest, mixed forest) models and general models. Additionally, the synergy of standard metrics and canopy metrics for estimating structural parameters was evaluated using field measured data. Finally, the sensitivity of vertical and horizontal resolution of voxel size for estimating forest structural parameters was assessed. The results showed that, in general, the accuracies of forest type-specific models (Adj-R2 = 0.44–0.88) were relatively higher than general models (Adj-R2 = 0.39–0.77). For forest structural parameters, the estimation accuracies of Lorey’s mean height (Adj-R2 = 0.61–0.88) and aboveground biomass (Adj-R2 = 0.54–0.81) models were the highest, followed by volume (Adj-R2 = 0.42–0.78), DBH (Adj-R2 = 0.48–0.74), basal area (Adj-R2 = 0.41–0.69), whereas stem density (Adj-R2 = 0.39–0.64) models were relatively lower. The combination models (Adj-R2 = 0.45–0.88) had higher performance compared with models developed using standard metrics (only) (Adj-R2 = 0.42–0.84) and canopy metrics (only) (Adj-R2 = 0.39–0.83). The results also demonstrated that the optimal voxel size was 5 × 5 × 0.5 m3 for estimating most of the parameters. This study demonstrated that canopy metrics based on canopy vertical profiles can be effectively used to enhance the estimation accuracies of forest structural parameters in subtropical forests

Extrapolation Assessment for Forest Structural Parameters in Planted Forests of Southern China by UAV-LiDAR Samples and Multispectral Satellite Imagery

Accurate estimation and extrapolation of forest structural parameters in planted forests are essential for monitoring forest resources, investigating their ecosystem services (e.g., forest structure and functions), as well as supporting decisions for precision silviculture. Advances in unmanned aerial vehicle (UAV)-borne Light Detection and Ranging (LiDAR) technology have enhanced our ability to precisely characterize the 3-D structure of the forest canopy with high flexibility, usually within forest plots and stands. For wall-to-wall forest structure mapping in broader landscapes, samples (transects) of UAV-LiDAR datasets are a cost-efficient solution as an intermediate layer for extrapolation from field plots to full-coverage multispectral satellite imageries. In this study, an advanced two-stage extrapolation approach was established to estimate and map large area forest structural parameters (i.e., mean DBH, dominant height, volume, and stem density), in synergy with field plots and UAV-LiDAR and GF-6 satellite imagery, in a typical planted forest of southern China. First, estimation models were built and used to extrapolate field plots to UAV-LiDAR transects; then, the maps of UAV-LiDAR transects were extrapolated to the whole study area using the wall-to-wall grid indices that were calculated from GF-6 satellite imagery. By comparing with direct prediction models that were fitted by field plots and GF-6-derived spectral indices, the results indicated that the two-stage extrapolation models (R2 = 0.64–0.85, rRMSE = 7.49–26.85%) obtained higher accuracy than direct prediction models (R2 = 0.58–0.75, rRMSE = 21.31–38.43%). In addition, the effect of UAV-LiDAR point density and sampling intensity for estimation accuracy was studied by sensitivity analysis as well. The results showed a stable level of accuracy for approximately 10% of point density (34 pts·m−2) and 20% of sampling intensity. To understand the error propagation through the extrapolation procedure, a modified U-statistics uncertainty analysis was proposed to characterize pixel-level estimates of uncertainty and the results demonstrated that the uncertainty was 0.75 cm for mean DBH, 1.23 m for dominant height, 14.77 m3·ha−1 for volume and 102.72 n·ha−1 for stem density, respectively

Mapping Above- and Below-Ground Biomass Components in Subtropical Forests Using Small-Footprint LiDAR

In order to better assess the spatial variability in subtropical forest biomass, the goal of our study was to use small-footprint, discrete-return Light Detection and Ranging (LiDAR) data to accurately estimate and map above- and below-ground biomass components of subtropical forests. Foliage, branch, trunk, root, above-ground and total biomass of 53 plots (30 × 30 m) were modeled using a range of LiDAR-derived metrics, with individual models built for each of the three dominant forest types using stepwise multi-regression analysis. A regular grid covered the entire study site with cell size 30 × 30 m corresponding to the same size of the plots; it was generated for mapping each biomass component. Overall, results indicate that biomass estimation was more accurate in coniferous forests, compared with the mixed and broadleaved plots. The coefficient of determination (R²) for individual models was significantly enhanced compared with an overall generic, or common, model. Using independent stand-level data from ground inventory, our results indicated that overall the model fit was significant for most of the biomass components, with relationships close to a 1:1 line, thereby indicating no significant bias. This research illustrates the potential for LiDAR as a technology to assess subtropical forest carbon accurately and to provide a better understanding of how forest ecosystems function in this region.Forestry, Faculty ofNon UBCForest Resources Management, Department ofReviewedFacult

Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data

Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests

Spatio-Temporal Variability of Soil Moisture and Its Effect on Vegetation in a Desertified Aeolian Riparian Ecotone on the Tibetan Plateau, China

Soil moisture content is one of the limiting factors for natural vegetation succession in alpine river valleys on the Tibetan Plateau. However, its spatio-temporal variability and effect on artificial vegetation restoration has rarely been reported. In this paper, we conducted a case study to examine the spatio-temporal variability and vertical characteristics of soil moisture by setting up a 100 x 120 m experimental plot on aeolian sandy land in a riparian ecotone and analyzing the result data using a geo-statistical approach. The soil moisture contents at different depths all showed strong temporal variability, with the mean soil moisture ranging from 4% to 6% in spring, from 6% to 14% in summer and from 9% to 12% in autumn. The Gaussian semi-variogram model was the best fitted theoretical model for the spatial structure of soil moisture contents in different seasons, and soil moisture at the field scale showed strong spatial dependence. The spatial patterns of soil moisture all demonstrated strong similarity between depths with the strongest between 0-20 cm and 20-40 cm depths in autumn. The soil moisture content over the upper 60 cm was the key factor that restricted the seed germination and seedling growth, and it affected vegetation coverage and density. The precipitation, variations of river water level, elevation as well as landform types were the main factors determining the variations of soil moisture content in different seasons. The results have important implications for the ongoing vegetation restoration in the study area. (C) 2012 Elsevier B.V. All rights reserved

Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests

An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR) dataset using two different techniques focusing on (i) 3-D canopy structural information by discrete (XYZ) LiDAR metrics (DR-metrics), and (ii) the detailed geometric and radiometric information of the returned waveform by full-waveform LiDAR metrics (FW-metrics), and to evaluate the capacity of these metrics in predicting biomass and its components in subtropical forest ecosystems. This study was undertaken in a mixed subtropical forest in Yushan Mountain National Park, Jiangsu, China. LiDAR metrics derived from DR and FW LiDAR data were used alone, and in combination, in stepwise regression models to estimate total as well as above-ground, root, foliage, branch and trunk biomass. Overall, the results indicated that three sets of predictive models performed well across the different subtropical forest types (Adj-R² = 0.42–0.93, excluding foliage biomass). Forest type-specific models (Adj-R² = 0.18–0.93) were generally more accurate than the general model (Adj-R² = 0.07–0.79) with the most accurate results obtained for coniferous stands (Adj-R² = 0.50–0.93). In addition, LiDAR metrics related to vegetation heights were the strongest predictors of total biomass and its components. This research also illustrates the potential for the synergistic use of DR and FW LiDAR metrics to accurately assess biomass stocks in subtropical forests, which suggest significant potential in research and decision support in sustainable forest management, such as timber harvesting, biofuel characterization and fire hazard analyses.Forestry, Faculty ofNon UBCForest Resources Management, Department ofReviewedFacult

Serum hepatokines in dairy cows: periparturient variation and changes in energy-related metabolic disorders

International audienceBACKGROUND: During peripartum period, dairy cows are highly susceptible to energy metabolism disorders such as fatty liver and ketosis. Angiopoietin-like protein 4 (ANGPTL4) and fibroblast growth factor 21 (FGF21), known as hepatokines, play important roles in lipid metabolism. The purposes of our study were to evaluate variations of serum ANGPTL4 and FGF21 concentrations in periparturient dairy cows and changes in these serum analyte concentrations of energy-related metabolic disorders in early lactation dairy cows. This study was divided into two experiments. Experiment I: Blood parameters were measured in healthy periparturient Holstein cows from 4 wk antepartum to 4 wk postpartum (n = 219). In this experiment, weekly blood samples were obtained from 4 wk before the expected calving date through 4 wk after calving. Experiment II: Blood parameters were measured in healthy cows (n = 30) and cows with clinical ketosis (n = 29) and fatty liver (n = 25) within the first 4 wk of lactation. In the present study, all blood samples were collected from the coccygeal vein in the early morning before feeding.RESULTS: Serum ANGPTL4 and FGF21 concentrations peaked at parturition, and declined rapidly over the following 2 wk Serum ANGPTL4 and FGF21 concentrations were positively correlated with serum non-esterified fatty acids (NEFA) concentration (r = 0.856, P = 003; r = 0.848, P = 0.004, respectively). Cows with clinical ketosis and fatty liver had significantly higher serum ANGPTL4 and FGF21 concentrations than healthy cows (P < 0.01).CONCLUSION: Serum ANGPTL4 and FGF21 concentrations were elevated during peripartum period, suggesting that energy balance changes that were associated with parturition contributed significantly to these effects. Although FGF21 and ANGPTL4 could play important roles in the adaptation of energy metabolism, they may be involved in the pathological processes of energy metabolism disorders of dairy cows in the peripartum period