Estimating Tropical Forest Structure Using a Terrestrial Lidar

Forest structure comprises numerous quantifiable biometric components and characteristics, which include tree geometry and stand architecture. These structural components are important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying biometric properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in a predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, Fast Fourier Transform (FFT), number of layers and plant area index to develop statistical relationships with field data.We developed statistical models using a series of multiple linear regressions, all of which converged on significant relationships with the strongest relationship being for mean crown depth (r2 = 0.88, p \u3c 0.001, RMSE = 1.04 m). Tree density was found to have the poorest significant relationship (r2 = 0.50, p \u3c 0.01, RMSE = 153.28 n ha-1). We found a significant relationship between basal area and lidar metrics (r2 = 0.75, p \u3c 0.001, RMSE = 3.76 number ha-1). Parameters selected in our models varied, thus indicating the potential relevance of multiple features in canopy profiles and geometry that are related to field-measured structure. Models for biomass estimation included structural canopy variables in addition to height metrics. Our work indicates that vegetation profiles from TLS data can provide useful information on forest structure

Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass (p-value = 0.31, r2 = 0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter (p-value = 0.004, r2 = 0.51) and tree density (p-value = 0.002, r2 = 0.43). We were also able to develop a significant model relating lidar profiles to basal area (p-value = 0.003, r2 = 0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties

Evaluating multiple causes of persistent low microwave backscatter from Amazon forests after the 2005 drought

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon’s vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999–2009, and low morning backscatter persisted for 2006–2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts

Ancient Amazonian populations left lasting impacts on forest structure

Amazonia contains a vast expanse of contiguous tropical forest and is influential in global carbon and hydrological cycles. Whether ancient Amazonia was highly disturbed or modestly impacted, and how ancient disturbances have shaped current forest ecosystem processes, is still under debate. Amazonian Dark Earths (ADEs), which are anthropic soil types with enriched nutrient levels, are one of the primary lines of evidence for ancient human presence and landscape modifications in settings that mostly lack stone structures and which are today covered by vegetation. We assessed the potential of using moderate spatial resolution optical satellite imagery to predict ADEs across the Amazon Basin. Maximum entropy modeling was used to develop a predictive model using locations of ADEs across the basin and satellite‐derived remotely sensed indices. Amazonian Dark Earth sites were predicted to be primarily along the main rivers and in eastern Amazonia. Amazonian Dark Earth sites, when compared with randomly selected forested sites located within 50 km of ADE sites, were less green canopies (lower normalized difference vegetation index) and had lower canopy water content. This difference was accentuated in two drought years, 2005 and 2010. This is contrary to our expectation that ADE sites would have nutrient‐rich soils that support trees with greener canopies and forests on ADE soils being more resilient to drought. Biomass and tree height were lower on ADE sites in comparison with randomly selected adjacent sites. Our results suggested that ADE‐related ancient human impact on the forest is measurable across the entirety of the 6 million km2 of Amazon Basin using remotely sensed data

Immune sensitization to methylene diphenyl diisocyanate (MDI) resulting from skin exposure: albumin as a carrier protein connecting skin exposure to subsequent respiratory responses

<p>Abstract</p> <p>Background</p> <p>Methylene diphenyl diisocyanate (MDI), a reactive chemical used for commercial polyurethane production, is a well-recognized cause of occupational asthma. The major focus of disease prevention efforts to date has been respiratory tract exposure; however, skin exposure may also be an important route for inducing immune sensitization, which may promote subsequent airway inflammatory responses. We developed a murine model to investigate pathogenic mechanisms by which MDI skin exposure might promote subsequent immune responses, including respiratory tract inflammation.</p> <p>Methods</p> <p>Mice exposed via the skin to varying doses (0.1-10% w/v) of MDI diluted in acetone/olive oil were subsequently evaluated for MDI immune sensitization. Serum levels of MDI-specific IgG and IgE were measured by enzyme-linked immunosorbant assay (ELISA), while respiratory tract inflammation, induced by intranasal delivery of MDI-mouse albumin conjugates, was evaluated based on bronchoalveolar lavage (BAL). Autologous serum IgG from "skin only" exposed mice was used to detect and guide the purification/identification of skin proteins antigenically modified by MDI exposure in vivo.</p> <p>Results</p> <p>Skin exposure to MDI resulted in specific antibody production and promoted subsequent respiratory tract inflammation in animals challenged intranasally with MDI-mouse albumin conjugates. The degree of (secondary) respiratory tract inflammation and eosinophilia depended upon the (primary) skin exposure dose, and was maximal in mice exposed to 1% MDI, but paradoxically limited in mice receiving 10-fold higher doses (e.g. 10% MDI). The major antigenically-modified protein at the local MDI skin exposure site was identified as albumin, and demonstrated biophysical changes consistent with MDI conjugation.</p> <p>Conclusions</p> <p>MDI skin exposure can induce MDI-specific immune sensitivity and promote subsequent respiratory tract inflammatory responses and thus, may play an important role in MDI asthma pathogenesis. MDI conjugation and antigenic modification of albumin at local (skin/respiratory tract) exposure sites may represent the common antigenic link connecting skin exposure to subsequent respiratory tract inflammation.</p

Lipopolysaccharide-enhanced, Toll-like Receptor 4–dependent T Helper Cell Type 2 Responses to Inhaled Antigen

Allergic asthma is an inflammatory lung disease initiated and directed by T helper cells type 2 (Th2). The mechanism involved in generation of Th2 responses to inert inhaled antigens, however, is unknown. Epidemiological evidence suggests that exposure to lipopolysaccharide (LPS) or other microbial products can influence the development and severity of asthma. However, the mechanism by which LPS influences asthma pathogenesis remains undefined. Although it is known that signaling through Toll-like receptors (TLR) is required for adaptive T helper cell type 1 (Th1) responses, it is unclear if TLRs are needed for Th2 priming. Here, we report that low level inhaled LPS signaling through TLR4 is necessary to induce Th2 responses to inhaled antigens in a mouse model of allergic sensitization. The mechanism by which LPS signaling results in Th2 sensitization involves the activation of antigen-containing dendritic cells. In contrast to low levels, inhalation of high levels of LPS with antigen results in Th1 responses. These studies suggest that the level of LPS exposure can determine the type of inflammatory response generated and provide a potential mechanistic explanation of epidemiological data on endotoxin exposure and asthma prevalence

Tumor Transcriptome Sequencing Reveals Allelic Expression Imbalances Associated with Copy Number Alterations

Due to growing throughput and shrinking cost, massively parallel sequencing is rapidly becoming an attractive alternative to microarrays for the genome-wide study of gene expression and copy number alterations in primary tumors. The sequencing of transcripts (RNA-Seq) should offer several advantages over microarray-based methods, including the ability to detect somatic mutations and accurately measure allele-specific expression. To investigate these advantages we have applied a novel, strand-specific RNA-Seq method to tumors and matched normal tissue from three patients with oral squamous cell carcinomas. Additionally, to better understand the genomic determinants of the gene expression changes observed, we have sequenced the tumor and normal genomes of one of these patients. We demonstrate here that our RNA-Seq method accurately measures allelic imbalance and that measurement on the genome-wide scale yields novel insights into cancer etiology. As expected, the set of genes differentially expressed in the tumors is enriched for cell adhesion and differentiation functions, but, unexpectedly, the set of allelically imbalanced genes is also enriched for these same cancer-related functions. By comparing the transcriptomic perturbations observed in one patient to his underlying normal and tumor genomes, we find that allelic imbalance in the tumor is associated with copy number mutations and that copy number mutations are, in turn, strongly associated with changes in transcript abundance. These results support a model in which allele-specific deletions and duplications drive allele-specific changes in gene expression in the developing tumor

dopamine D 5 receptors (Tiberi and Caron, 1994), serotonin 5-HT 2C recep-tors

ABSTRACT Single amino acid mutations in the third intracellular loop, as well as other domains of G protein-coupled receptors, have been shown to confer drastic changes in receptor properties and have been postulated to be responsible for various disease states. To determine whether an amino acid mutation can confer dramatic alterations in the 5-hydroxytryptamine 2A (5-HT 2A ) receptor, we mutated amino acid 322 to lysine (C322K), glutamate (C322E) or arginine (C322R). Transient expression of the mutant receptors revealed properties associated with constitutive activity. Radioligand binding studies revealed an increase in 5-HT affinity from 293 nM (native) to 86 nM (C322E), 25 nM (C322K) and 11 nM (C322R). 5-HT potency for stimulation of inositol phosphate production increased from 152 nM (native) to 61 nM (C322E) and 25 nM (C322K). Basal inositol phosphate levels in COS-7 cells expressing C322K and C322E mutant receptors were 8-fold and 4-fold higher, respectively, than cells expressing native 5-HT 2A receptors. Basal levels of inositol phosphate stimulated by C322K receptors represented 48% of total inositol phosphate production stimulated by native receptors in the presence of 10 M 5-HT. Antipsychotic drugs (chlorpromazine, clozapine, haloperidol, loxapine and risperidone) displayed inverse agonist activity by inhibiting C322K constitutive activation of phosphatidylinositol hydrolysis. These data indicate that amino acid 322 in the 5-HT 2A receptor plays an important role in maintaining the inactive conformation and provide further evidence that amino acid mutations can produce profound alterations in G protein-coupled receptor activity. The third intracellular loop of GPCR has been identified as a region that is crucial for receptor/G protein interactions (Strader et al., 1987; Recently, several GPCR have exhibited constitutive activity in vivo. Naturally occurring amino acid mutations in the luteinizing hormone recepto