Application of meteorological data to agroclimatological mapping

During the years 1969–72 a comprehensive agroclimatic survery was carried out in Aust-Agder, a county of approximately 9000 km² in the southernmost part of Norway. Meteorological data were collected from some 70 stations grouped in 11 model locations. In the analysis the agricultural purposes behind the survey are born in mind and importance is attached to simple and direct methods that will allow extrapolations within the region with sufficient confidence. The geographical variations in growth climate is expressed by average radiation and temperature conditions, and by estimates of plant-available soil moisture.Presented at the Eighth International Congress of Biometeorology, 9–14 September 1979, Shefayim, Israel.     

Quality classification of tandem mass spectrometry data

Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. RESULTS: We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.     

Protocols for disease classification from mass spectrometry data

We report our results in classifying protein matrix-assisted laser desorption/ionization-time of flight mass spectra obtained from serum samples into diseased and healthy groups. We discuss in detail five of the steps in preprocessing the mass spectral data for biomarker discovery, as well as our criterion for choosing a small set of peaks for classifying the samples. Cross-validation studies with four selected proteins yielded misclassification rates in the 10-15% range for all the classification methods. Three of these proteins or protein fragments are down-regulated and one up-regulated in lung cancer, the disease under consideration in this data set. When cross-validation studies are performed, care must be taken to ensure that the test set does not influence the choice of the peaks used in the classification. Misclassification rates are lower when both the training and test sets are used to select the peaks used in classification versus when only the training set is used. This expectation was validated for various statistical discrimination methods when thirteen peaks were used in cross-validation studies. One particular classification method, a linear support vector machine, exhibited especially robust performance when the number of peaks was varied from four to thirteen, and when the peaks were selected from the training set alone. Experiments with the samples randomly assigned to the two classes confirmed that misclassification rates were significantly higher in such cases than those observed with the true data. This indicates that our findings are indeed significant. We found closely matching masses in a database for protein expression in lung cancer for three of the four proteins we used to classify lung cancer. Data from additional samples, increased experience with the performance of various preprocessing techniques, and affirmation of the biological roles of the proteins that help in classification, will strengthen our conclusions in the future.     

Comparison of classification methods that combine clinical data and high-dimensional mass spectrometry data

Background

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. Technologies like mass spectrometry are commonly being used in proteomic research. Mass spectrometry signals show the proteomic profiles of the individuals under study at a given time. These profiles correspond to the recording of a large number of proteins, much larger than the number of individuals. These variables come in addition to or to complete classical clinical variables. The objective of this study is to evaluate and compare the predictive ability of new and existing models combining mass spectrometry data and classical clinical variables. This study was conducted in the context of binary prediction.

Results

To achieve this goal, simulated data as well as a real dataset dedicated to the selection of proteomic markers of steatosis were used to evaluate the methods. The proposed methods meet the challenge of high-dimensional data and the selection of predictive markers by using penalization methods (Ridge, Lasso) and dimension reduction techniques (PLS), as well as a combination of both strategies through sparse PLS in the context of a binary class prediction. The methods were compared in terms of mean classification rate and their ability to select the true predictive values. These comparisons were done on clinical-only models, mass-spectrometry-only models and combined models.

Conclusions

It was shown that models which combine both types of data can be more efficient than models that use only clinical or mass spectrometry data when the sample size of the dataset is large enough.     

Thermal plasticity is independent of environmental history in an intertidal seaweed

Organisms inhabiting the intertidal zone have been used to study natural ecophysiological responses and adaptations to thermal stress because these organisms are routinely exposed to high‐temperature conditions for hours at a time. While intertidal organisms may be inherently better at withstanding temperature stress due to regular exposure and acclimation, they could be more vulnerable to temperature stress, already living near the edge of their thermal limits. Strong gradients in thermal stress across the intertidal zone present an opportunity to test whether thermal tolerance is a plastic or canalized trait in intertidal organisms. Here, we studied the intertidal pool‐dwelling calcified alga, Ellisolandia elongata, under near‐future temperature regimes, and the dependence of its thermal acclimatization response on environmental history. Two timescales of environmental history were tested during this experiment. The intertidal pool of origin was representative of long‐term environmental history over the alga's life (including settlement and development), while the pool it was transplanted into accounted for recent environmental history (acclimation over many months). Unexpectedly, neither long‐term nor short‐term environmental history, nor ambient conditions, affected photosynthetic rates in E. elongata. Individuals were plastic in their photosynthetic response to laboratory temperature treatments (mean 13.2°C, 15.7°C, and 17.7°C). Further, replicate ramets from the same individual were not always consistent in their photosynthetic performance from one experimental time point to another or between treatments and exhibited no clear trend in variability over experimental time. High variability in climate change responses between individuals may indicate the potential for resilience to future conditions and, thus, may play a compensatory role at the population or species level over time.     

Application of airborne hyperspectral data for mapping of invasive alien Spiraea tomentosa L.: a serious threat to peat bog plant communities

Wetlands Ecology and Management - Remote sensing is increasingly widely used in nature conservation management. The research focuses on developing an optimal set of airborne raster data for the...     

Ferry wakes increase seaweed richness and abundance in a sheltered rocky intertidal habitat

Because hydrodynamic regimes influence community assemblages, commercial ferry traffic can directly affect neighboring marine ecosystems by altering water movement. One of the largest ferry fleets in the world traverses the calm, protected waters of the Canadian Gulf Islands (British Columbia). To address the effects of ferry wakes on rocky marine intertidal communities, we examined community assemblages in sites impacted by ferry wakes (N?=?5) relative to geographically similar control sites not directly exposed to ferry wakes (N?=?6). Intertidal communities were significantly different between wake-influenced and control sites. Further analyses revealed that community level differences resulted from differences in seaweed assemblages, while invertebrate assemblages were similar. Sites exposed to ferry traffic displayed significantly greater overall seaweed abundance and seaweed species richness. Nitrate and nitrite concentrations, salinity, fetch, and tidal zonation were not significantly different between wake-impacted and control sites. However, dissolution blocks revealed that wake-impacted sites experienced increased overall water movement. Furthermore, block dissolution was negatively associated with distance from nearest ferry route and not fetch. Although dissolution block cannot disentangle effects of increased flow versus waves resulting from ferry wakes, we conclude that increased overall water movement from frequent and proximate ferry traffic stimulates primary production in rocky intertidal marine seaweeds by ameliorating mass transfer limitation.     

imzML--a common data format for the flexible exchange and processing of mass spectrometry imaging data

The application of mass spectrometry imaging (MS imaging) is rapidly growing with a constantly increasing number of different instrumental systems and software tools. The data format imzML was developed to allow the flexible and efficient exchange of MS imaging data between different instruments and data analysis software. imzML data is divided in two files which are linked by a universally unique identifier (UUID). Experimental details are stored in an XML file which is based on the HUPO-PSI format mzML. Information is provided in the form of a 'controlled vocabulary' (CV) in order to unequivocally describe the parameters and to avoid redundancy in nomenclature. Mass spectral data are stored in a binary file in order to allow efficient storage. imzML is supported by a growing number of software tools. Users will be no longer limited to proprietary software, but are able to use the processing software best suited for a specific question or application. MS imaging data from different instruments can be converted to imzML and displayed with identical parameters in one software package for easier comparison. All technical details necessary to implement imzML and additional background information is available at www.imzml.org.     

Intergametophytic selfing and microgeographic genetic structure shape populations of the intertidal red seaweed Chondrus crispus

Understanding how abiotic factors influence the spatial distribution of genetic variation provides insight into microevolutionary processes. The intertidal seascape is characterized by highly heterogeneous habitats which probably influence the partitioning of genetic variation at very small scales. The effects of tidal height on genetic variation in both the haploid (gametophytes) and diploid (tetrasporophytes) stages of the red alga Chondrus crispus were studied. Fronds were sampled every 25 cm within a 5 m × 5 m grid and along a 90-m transect at two shore heights (high and low) in one intertidal site in France. The multilocus genotype of 799 fronds was determined (N_haploid = 586; N_diploid = 213) using eight microsatellite loci to test the following hypotheses: (i) high and low shore fronds belong to genetically differentiated populations, (ii) gene flow is restricted within the high shore habitat due to tidal-influenced isolation and (iii) significant F_IS values are driven by life history characteristics. Pairwise F_ST estimates between high and low shore levels supported the hypothesis that high and low shore fronds were genetically differentiated. The high shore was characterized by the occurrence of within-shore genetic differentiation, reduced genetic diversity and increased levels of intergametophytic selfing, suggesting it is a marginal environment. These results suggest at fine scales within the intertidal seascape the same mechanisms as those over the species’ distributional range are at work with core and marginal population dynamics.     

Implementation of a new index to assess intertidal seaweed communities as bioindicators for the European Water Framework Directory

An index CCO (cover, characteristic species, opportunistic species) has been developed for the implementation of the European Water Framework Directory (WFD) in coastal waters, using intertidal macroalgal communities as bio-indicator (Biological Quality Element). CCO is based on the calculation of three metrics corresponding to the global cover of macroalgal communities (metric 1), the number of characteristic species per topographic level/seaweed community (metric 2) and the cover of opportunistic species (metric 3). The final rating is obtained by pooling the scores of the three metrics. Results are given for 32 sites in 29 water bodies, grouped into four biogeographic regions along the Channel–Atlantic coasts of France. Over the six-year study, most of sites were sampled twice each (every three years). CCO index revealed that 25 coastal water bodies of both the Channel and the Bay of Biscay were in good or high ecological quality status (EQS), whereas only 4 of them were moderate and none in poor to bad status. However, significant differences have been found between sites and between geographic regions, water bodies located in Brittany obtaining the best EQS. No significant change occurred between the three-year sampling sets. A significant correlation has been established between a three-component anthropogenic pressure index and CCO ratings, showing the accuracy of CCO to evaluate the impact of anthropic activities on the structure and development of macroalgal communities as indicator of the ecological quality of coastal water bodies.     

Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data

We evaluated the effectiveness of integrating discrete return light detection and ranging (LiDAR) data with high spatial resolution near-infrared digital imagery for object-based classification of land cover types and dominant tree species. In particular we adopted LiDAR ratio features based on pulse attributes that have not been used in past studies. Object-based classifications were performed first on land cover types, and subsequently on dominant tree species within the area classified as trees. In each classification stage, two different data combinations were examined: LiDAR data integrated with digital imagery or digital imagery only. We created basic image objects and calculated a number of spectral, textural, and LiDAR-based features for each image object. Decision tree analysis was performed and important features were investigated in each classification. In the land cover classification, the overall accuracy was improved to 0.975 when using the object-based method and integrating LiDAR data. The mean height value derived from the LiDAR data was effective in separating “trees” and “lawn” objects having different height. As for the tree species classification, the overall accuracy was also improved by object-based classification with LiDAR data although it remained up to 0.484 because spectral and textural signatures were similar among tree species. We revealed that the LiDAR ratio features associated with laser penetration proportion were important in the object-based classification as they can distinguish tree species having different canopy density. We concluded that integrating LiDAR data was effective in the object-based classifications of land cover and dominant tree species.     

Nitrate uptake varies with tide height and nutrient availability in the intertidal seaweed Fucus vesiculosus

Intertidal seaweeds must cope with a suite of stressors imposed by aerial exposure at low tide, including nutrient limitation due to emersion. Seaweeds can access nutrients only when submerged, so individuals living higher compared to lower on the shore may have adaptations allowing them to acquire sufficient amounts of nutrients to survive and maintain growth. Using a combination of observations and experiments, we aimed to identify intraspecific variation in nitrate uptake rates across the intertidal distribution of F. vesiculosus, as well as test for acclimation in response to a change in tide height. We replicated our study at sites spanning nearly the entire Gulf of Maine coastline, to examine how local environmental variability may alter intraspecific variation in nitrate uptake. We found that average nitrate uptake rates were ~18% higher in upper compared to lower intertidal Fucus vesiculosus. Furthermore, we found evidence for both acclimation and adaptation to tide height during a transplant experiment. F. vesiculosus transplanted from the lower to the upper intertidal zone was characterized by increased nitrate uptake, but individuals transplanted from the upper to the lower intertidal zone retained high uptake rates. Our observations differed among Gulf of Maine regions and among time points of our study. Importantly, these differences may reflect associations between nitrate uptake rates and abiotic environmental conditions and seaweed nutrient status. Our study highlights the importance of long‐term variation in ambient nutrient supply in driving intraspecific variation of seaweeds across the intertidal gradient and local and seasonal variation in ambient nutrient levels in mediating intraspecific differences.     

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

MOTIVATION: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data. RESULTS: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.     

Enhancing wall-to-wall forest structure mapping through detailed co-registration of airborne and terrestrial laser scanning data in Mediterranean forests

This paper presents a new co-registration procedure of complementary point clouds captured by both Terrestrial (TLS) and Airborne Laser Scanning (ALS) technologies. Starting from the geographic position of the TLS point cloud, a geometric features recognition algorithm, which evaluates digital terrain models obtained from both ALS and TLS, was developed and implemented in a new GIS software (ForeSight®). As a case study, we tested this new approach using point clouds acquired from both hand-held mobile TLS and ALS sensors over 24 test sites located in a protected area in southern Italy, with the ultimate goal of characterizing the different forest stand structures. From each aligned point cloud, a plot-level spatially explicit index (Enhanced Structural Spatial Index, ESCI) was derived to assess the three-dimensional structure of the considered forest stands. Then, we compared structural features derived from the ESCI index with different computed ALS metrics. Finally, the most correlated ALS metrics were used as predictors to produce an ESCI-map of the entire region of interest.     

Lipid mapping in human dystrophic muscle by cluster-time-of-flight secondary ion mass spectrometry imaging

Human striated muscle samples, from male control and Duchenne muscular dystrophy-affected children, were subjected to cluster-time-of-flight secondary ion mass spectrometry (cluster-ToF-SIMS) imaging using a 25 keV Bi(3)(+) liquid metal ion gun under static SIMS conditions. Spectra and ion density maps, or secondary ion images, were acquired in both positive and negative ion mode over several areas of 500 x 500 microm(2) (image resolution, 256 x 256 pixels). Characteristic distributions of various lipids were observed. Vitamin E and phosphatidylinositols were found to concentrate within the cells, whereas intact phosphocholines accumulated over the most damaged areas of the dystrophic muscles, together with cholesterol and sphingomyelin species. Fatty acyl chain composition varied depending on the region, allowing estimation of the local damage extent.     

Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering

In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules. However, despite this impressive technological advance, only a few papers have been published concerned with computational methods for MALDI-imaging data. We address this issue proposing a new procedure for spatial segmentation of MALDI-imaging data sets. This procedure clusters all spectra into different groups based on their similarity. This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample. The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly. Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1% of spectra. High dimensional discriminant clustering completes the procedure. We analyzed two data sets using the proposed pipeline. First, for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain. Second, a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated.