Morphometric analysis of pollen grains for paleoecological studies: classification of Picea from eastern North America

Little is known about the paleoecological histories of the three spruce species (white spruce, Picea glauca; black spruce, P. mariana; and red spruce P. rubens) in eastern North America, largely because of the difficulty of separating the three species in the pollen record. We describe a novel and effective classification method of distinguishing pollen grains on the basis of quantitative analysis of grain attributes. The method is illustrated by an analysis of a large sample of modern pollen grains (522 grains from 38 collections) of the three Picea species, collected from the region where the three species co-occur today. For each species X we computed a binary regression tree that classified each grain either as X or as not-X; these three determinations for each grain were then combined as Hamming codes in an error/uncertainty detection procedure. The use of Hamming codes to link multiple binary trees for error detection allowed identification and exclusion of problematic specimens, with correspondingly greater classification certainty among the remaining grains. We measured 13 attributes of 419 reference grains of the three species to construct the regression trees and classified 103 other reference grains by testing. Species-specific accuracies among the reliably classified grains were 100, 77, and 76% for P. glauca, P. mariana, and P. rubens, respectively, and 21, 30, and 22% of the grains by species, respectively, were problematic. The method is applicable to any multi-species classification problem for which a large reference sample is available.     

Predicting the Valence of a Scene from Observers’ Eye Movements

Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images.     

Highly Informative Single-Copy Nuclear Microsatellite DNA Markers Developed Using an AFLP-SSR Approach in Black Spruce (Picea mariana) and Red Spruce (P. rubens)

Background

Microsatellites or simple sequence repeats (SSRs) are highly informative molecular markers for various biological studies in plants. In spruce (Picea) and other conifers, the development of single-copy polymorphic genomic microsatellite markers is quite difficult, owing primarily to the large genome size and predominance of repetitive DNA sequences throughout the genome. We have developed highly informative single-locus genomic microsatellite markers in black spruce (Picea mariana) and red spruce (Picea rubens) using a simple but efficient method based on a combination of AFLP and microsatellite technologies.

Principal Findings

A microsatellite-enriched library was constructed from genomic AFLP DNA fragments of black spruce. Sequencing of the 108 putative SSR-containing clones provided 94 unique sequences with microsatellites. Twenty-two of the designed 34 primer pairs yielded scorable amplicons, with single-locus patterns. Fourteen of these microsatellite markers were characterized in 30 black spruce and 30 red spruce individuals drawn from many populations. The number of alleles at a polymorphic locus ranged from 2 to 18, with a mean of 9.3 in black spruce, and from 3 to 15, with a mean of 6.2 alleles in red spruce. The polymorphic information content or expected heterozygosity ranged from 0.340 to 0.909 (mean = 0.67) in black spruce and from 0.161 to 0.851 (mean = 0.62) in red spruce. Ten SSR markers showing inter-parental polymorphism inherited in a single-locus Mendelian mode, with two cases of distorted segregation. Primer pairs for almost all polymorphic SSR loci resolved microsatellites of comparable size in Picea glauca, P. engelmannii, P. sitchensis, and P. abies.

Significance

The AFLP-based microsatellite-enriched library appears to be a rapid, cost-effective approach for isolating and developing single-locus informative genomic microsatellite markers in black spruce. The markers developed should be useful in black spruce, red spruce and other Picea species for various genetics, genomics, breeding, forensics, conservation studies and applications.     

Photosynthetic Action Spectra of Trees: II. The Relationship of Cuticle Structure to the Visible and Ultraviolet Spectral Properties of Needles from Four Coniferous Species

The relative reflectance spectra for control and treated (surface wiped) current-year foliage of Douglas fir, and Sitka, Colorado, and Blue spruce (Pseudotsuga menziesii [Mirb.] Franco, Picea sitchensis [Bong.] Carr., Picea pungens Engelm., and Picea pungens Engelm. var. hoopsii, respectively) were obtained from 220 to 700 nm. The green color of the control foliage of both Douglas fir and Sitka spruce was unaffected by the treatment whereas the blue-green and blue-white foliage of control Colorado and Blue spruce, respectively, became “green” as a result of the wiping. The relative reflectance curves for all green foliage, including the treated Colorado and Blue spruce, were all very similar with a peak in the green (540-560 nm), minima in the red (660-680 nm) and blue (450-500 nm), and very low reflectivities in the ultraviolet (λ < 400 nm). In contrast, the control foliage for Colorado and Blue spruce both showed a generally higher relative reflectance over most of the visible spectrum (400-700 nm) with a marked increase in the blue region (400-500 nm). At wavelengths below 420 nm, their relative reflectances increased sharply with decreasing wavelength, the reflectance at 220 nm for Blue spruce being over four times that at 540 nm.     

Patterns of Cross-Continental Variation in Tree Seed Mass in the Canadian Boreal Forest

Seed mass is an adaptive trait affecting species distribution, population dynamics and community structure. In widely distributed species, variation in seed mass may reflect both genetic adaptation to local environments and adaptive phenotypic plasticity. Acknowledging the difficulty in separating these two aspects, we examined the causal relationships determining seed mass variation to better understand adaptability and/or plasticity of selected tree species to spatial/climatic variation. A total of 504, 481 and 454 seed collections of black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss) and jack pine (Pinus banksiana Lamb) across the Canadian Boreal Forest, respectively, were selected. Correlation analyses were used to determine how seed mass vary with latitude, longitude, and altitude. Structural Equation Modeling was used to examine how geographic and climatic variables influence seed mass. Climatic factors explained a large portion of the variation in seed mass (34, 14 and 29%, for black spruce, white spruce and jack pine, respectively), indicating species-specific adaptation to long term climate conditions. Higher annual mean temperature and winter precipitation caused greater seed mass in black spruce, but annual precipitation was the controlling factor for white spruce. The combination of factors such as growing season temperature and evapotranspiration, temperature seasonality and annual precipitation together determined seed mass of jack pine. Overall, sites with higher winter temperatures were correlated with larger seeds. Thus, long-term climatic conditions, at least in part, determined spatial variation in seed mass. Black spruce and Jack pine, species with relatively more specific habitat requirements and less plasticity, had more variation in seed mass explained by climate than did the more plastic species white spruce. As traits such as seed mass are related to seedling growth and survival, they potentially influence forest species composition in a changing climate and should be included in future modeling of vegetation shifts.     

Effects of NaCl on responses of ectomycorrhizal black spruce (Picea mariana), white spruce (Picea glauca) and jack pine (Pinus banksiana) to fluoride

Black spruce ( Picea mariana ), white spruce ( Picea glauca ) and jack pine ( Pinus banksiana ) were inoculated with Suillus tomentosus and subjected to potassium fluoride (1 m M KF and 5 m M KF) in the presence and absence of 60 m M NaCl. The NaCl and KF treatments reduced total dry weights in jack pine and black spruce seedlings, but they did not affect total dry weights in white spruce seedlings. The addition of 60 m M NaCl to KF treatment solutions alleviated fluoride-induced needle injury in ectomycorrhizal (ECM) black spruce and white spruce, but had little effect in jack pine seedlings. Both KF and 60 m M NaCl treatments reduced E values compared with non-treated control seedlings. However, with the exception of small reductions of K_r by NaCl treatments in black spruce, the applied KF and NaCl treatments had little effect on K_r in ECM plants. Chloride tissue concentrations in NaCl-treated plants were not affected by the presence of KF in treatment solutions. However, shoot F concentrations in ECM black spruce and white spruce treated with 5 m M KF + 60 m M NaCl were significantly reduced compared with the 5 m M KF treatment. The results point to a possible competitive inhibition of F transport by Cl. We also suggest that the possibility that aquaporins may be involved in the transmembrane transport of F should be further investigated.     

Understanding changes in black (Picea mariana) and white spruce (Picea glauca) foliage biomass and leaf area characteristics

Key message

Growth conditions related to inter-tree competition greatly influence black and white spruce foliage biomass and projected leaf area characteristics.

Abstract

Foliage characteristics such as biomass and area are important among other reasons because they can be related to tree growth. Despite their economic and ecologic importance, equations to characterize foliage biomass and projected area of black (Picea mariana (Miller) BSP) and white (Picea glauca (Moench) Voss) spruces are sparse. Total foliage biomass and projected leaf area, foliage biomass and leaf area density, and relative vertical distribution of black and white spruces foliage biomass and leaf area were modelled with linear and nonlinear mixed effect models. A total of 65 white spruces and 57 black spruces were destructively sampled at four different locations in Alberta, Québec, and Ontario, Canada. Our results show that for each species, total tree foliage biomass and projected leaf area is proportional to stem diameter, total height, and crown length. The addition of crown length in the equations improved the precision of the predictions of total foliage biomass for both species and diminishes greatly the site level random effect. An increase in DBH for black spruce and in the DBH to total height ratio for white spruce skewed the relative vertical foliage biomass distribution toward the base of the living crown. According to our results, growth conditions or tree development stage influence both foliage biomass and leaf area characteristics of black and white spruces. Our results emphasize the importance of inter-tree competition on foliage biomass characteristics.     

A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.     

Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems

Background

Machine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with “ill-posed” problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the “curse of dimensionality” very often dimension reduction is applied to the data.

Methodology

Baseline structural MRI data from cognitively normal and Alzheimer''s disease (AD) patients from the AD Neuroimaging Initiative database were used in this study. We evaluated here the ill-posedness of this classification problem across different dimensions and sample sizes and its relationship to the performance of regularized logistic regression (RLR), linear support vector machine (SVM) and linear regression classifier (LRC). In addition, these methods were compared with their principal components space counterparts.

Principal Findings

In voxel space the prediction performance of all methods increased as sample sizes increased. They were not only relatively robust to the increase of dimension, but they often showed improvements in accuracy. We linked this behavior to improvements in conditioning of the linear kernels matrices. In general the RLR and SVM performed similarly. Surprisingly, the LRC was often very competitive when the linear kernel matrices were best conditioned. Finally, when comparing these methods in voxel and principal component spaces, we did not find large differences in prediction performance.

Conclusions and Significance

We analyzed the problem of classifying AD MRI images from the perspective of linear ill-posed problems. We demonstrate empirically the impact of the linear kernel matrix conditioning on different classifiers'' performance. This dependence is characterized across sample sizes and dimensions. In this context we also show that increased dimensionality does not necessarily degrade performance of machine learning methods. In general, this depends on the nature of the problem and the type of machine learning method.     

Large scale development of selectively amplified microsatellite polymorphic loci (SAMPL) markers in spruce (Picea)

The spruce (Picea) species are ecologically and economically important in Canada. Highly informative markers with high multiplex ratios are needed to assist spruce genomics, genetics, and breeding programs. Selectively amplified microsatellite polymorphic loci (SAMPL) markers are highly suitable for these programs. We have developed, optimized, and characterized a set of 10 new SAMPL primers in combination with 16 MseI primers and resolved a large number of polymorphic SAMPL markers in spruce. The SAMPL primers were designed from the compound microsatellite repeats found in Norway spruce (Picea abies) and white spruce (Picea glauca). A total of 6313 polymorphic SAMPL makers were produced by 160 SAMPL–MseI primers combinations in eight progeny of a spruce mapping population.     

The effects of Laccaria proxima and fibrous pulp waste on the growth of nine container-grown conifer seedling species

 Japanese larch (Larix kaempferi), white spruce (Picea glauca), black spruce (Picea mariana), red spruce (Picea rubens), jack pine (Pinus banksiana), mugo pine (Pinus mugo), red pine (Pinus resinosa), Japanese black pine (Pinus thunbergii) and Douglas-fir (Pseudotsuga menziesii var. menziesii), were inoculated to test the effective host range of the ectomycorrhizal fungus Laccaria proxima and the possibility of utilizing pulp waste as a potting medium for containerized seedling production. Laccaria proxima tended to improve the container growth of Japanese black pine and white spruce, and significantly improved that of jack pine, mugo pine, black spruce, red spruce and Douglasfir. The growth of red pine and Japanese larch were only slightly improved with L. proxima. Pulp waste (33% by volume) had negative effects on tree seedling growth, except for Douglasfir (no significant effect). The interactions of Laccaria proxima and pulp waste varied; the hosts were significantly positive (P<0.01) in the case of jack pine and black spruce, but there was no significant effect for the rest. Negative effects were found with Japanese black pine. Use of pulp waste in seedling production of jack pine, black spruce, mugo pine, red spruce and Douglasfir inoculated with L. proxima and of Japanese black pine both with and without L. proxima is feasible, but further research is necessary to determine the optimal percentage of pulp waste that can be utilized in seedling production of tree species and the field performance of these seedlings. Accepted: 30 August 1995     

天山雪岭云杉大气花粉含量对气温变化的响应

对中国东天山天池自2001年7月至2006年7月连续5a收集的雪岭云杉大气花粉含量进行统计分析,结果表明:1)一年四季大气中都有雪岭云杉花粉,但花粉数量变化比较大,超过全年90％的大气花粉集中在5、6月份的花粉高峰期,之后花粉浓度逐渐下降,至翌年1月份浓度降至最低,2月开始花粉浓度有升高的趋势;2)5a平均花粉浓度是42.66粒/m3,最高年是2005年,花粉浓度可达99.54粒/m3,最低年2003年,仅为2.13粒/m3;3)雪岭云杉大气花粉高峰期出现在5月22至6月2日,高峰日出现在5月28至6月6日,结束日是在6月18至6月25日,平均持续时间为27 d.观测时段雪岭云杉大气花粉高峰期出现日、高峰日逐年提前,2006年出现日期比2002年提前了7d、高峰日提前9d,结束日期滞后,2006年比2002年滞后6d,花粉高峰期持续时间逐年延长,2006年比2002年延长了12d.分析显示,影响雪岭云杉大气花粉高峰期变化的主要因素是春季气温的升高;4)粗略估算每年新疆的雪岭云杉林带内由大气中降落到表土的花粉量达61 kg/hm2,新疆现有雪岭云杉52.84×104hm2,全年由大气降落到林带内表土的花粉多达3223 t,一部分降落到戈壁、荒漠以及沙漠等一些极端气候区的花粉为一些先锋种植物提供必要的营养物质,具有重要的生态意义.     

A set of polymorphic EST‐derived markers for Picea species

A pooled DNA method was used to produce fully informative EST (expressed sequence tag)‐derived markers for the Picea genus. Nine markers were produced from 10 cDNA identified as candidates for cold tolerance or embryogenesis. Indels and SNPs (single nucleotide polymorphisms) were characterized from sequences obtained from pools of 10 individuals for each of the three species: Picea glauca (white spruce), Picea mariana (black spruce) and Picea abies (Norway spruce). Indels were present in 28% of the sequences and SNPs with a frequency greater than 10% were present on average in 1.2% of the positions.     

Pollination of Picea orientalis (Pinaceae): saccus morphology governs pollen

Sacci of conifer pollen do not function primarily to increase the efficiency of wind pollination as is widely thought. Rather, they are bladders and cause pollen to float upwards in a liquid drop into the ovules. This observation is seemingly unsupported in the case of oriental spruce (Picea orientalis (L.) Link), which has saccate pollen. Ovulate cones are pendant at the time of pollination, which requires that pollen sink into the ovules. Pollen of oriental spruce floats at first but within 1-2 min sinks into the ovule. As sinking does not occur in saccate pollen of other Pinaceae, a variety of techniques was used to determine anatomical differences leading to this uncharacteristic tendency. Light, scanning electron, and confocal microscopy of the pollen surface yielded no significant appearing difference between pollen of oriental spruce and white spruce. However, transmission electron microscopy of freeze-fixed/freeze-substituted hydrated pollen revealed that the ektexine of oriental spruce pollen sacci is porous compared to that of white spruce. Confocal microscopy allowed examination of pollen hydration dynamics. Water enters pollen at the distal pole between sacci, and resulting rapid expansion of the tube cell forces air out of the saccate space. White spruce pollen remains buoyant because of enclosed air pockets in the saccus ektexine. Evolutionary change in pollen wall anatomy with resultant loss of saccus function is correlated with a change in ovulate strobilus orientation at pollination in oriental spruce. A suite of characters interact in the conifer pollination mechanism, and concerted change in these characters may lead to speciation.     

Why is Real-World Visual Object Recognition Hard?

Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, “natural” images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled “natural” images in guiding that progress. In particular, we show that a simple V1-like model—a neuroscientist's “null” model, which should perform poorly at real-world visual object recognition tasks—outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a “simpler” recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition—real-world image variation.     

The establishment patterns of tree seedlings are determined immediately after wildfire in a black spruce (Picea mariana) forest

Fire severity is predicted to increase in boreal regions due to global warming. We hypothesized that these extreme events will alter regeneration patterns of black spruce (Picea mariana). To test this hypothesis, we monitored seed dispersal and seedling emergence, survival and growth for 6 years from 2005 to 2010 after the 2004 wildfire on Poker Flat, interior Alaska, using 96 1 × 1 m plots. A total of 1,300 seedlings of black spruce and three broad-leaved deciduous trees (Populus tremuloides, Betula papyrifera, and Salix spp.) were recorded. Black spruce seedlings colonized burned and unburned ground surfaces for the first 2 years after the wildfire and established on any topographical surface, while the broad-leaved trees emerged less in areas of lower elevation, slope gradient and canopy openness and only on burned surfaces. Vascular plant cover on the ground floor increased the seedling establishment of black spruce and broad-leaved trees, most likely because of seed-trap effects. Black spruce grew faster on burned surface than on unburned surfaces. However, broad-leaved trees grew faster than black spruce on burned surfaces. Black spruce regenerates even after severe wildfire when the microtopography restricts the colonization of broad-leaved trees. The regeneration trajectories are determined soon after wildfire by a combination of seed limitation for black spruce and habitat preference for broad-leaved trees.     

Species-specific nuclear and chloroplast single nucleotide polymorphisms to distinguish Picea glauca, P. mariana and P. rubens

 Picea rubens (red spruce) and P. mariana (black spruce) are closely related species which are difficult to differentiate morphologically. They are sympatric with P. glauca (white spruce) in the northern portion of their ranges. In order to identify potential interspecific polymorphisms, the chloroplast trnK intron and rpl33-psaJ-trnP region were sequenced, and the nuclear-encoded ITS region of the rDNA repeat was partially sequenced. Thirteen chloroplast and 12 nuclear candidate interspecific single nucleotide polymorphisms (SNPs) were identified. The species-specificity of several SNPs was determined by surveying DNAs amplified from trees representing range-wide provenance tests; these included 46 red spruce from 11 provenances, 84 black spruce from 30 provenances and 90 white spruce from 22 provenances. Two SNPs (1 chloroplast and 1 nuclear), which distinguish black spruce from red and white spruce, were consistent among 96–100% of the trees surveyed. Five SNPs (4 chloroplast and 1 nuclear), which distinguish white spruce from red and black spruce, were consistent among 100% of surveyed trees. These species-specific SNPs were used to identify anonymous spruce samples in a blind test, and their utility for small amounts of tissue, as little as single needles, was demonstrated. Scoring these SNPs is much less labor intensive than previous molecular methods for taxa differentiation (restriction fragment length polymorphisms or random amplified polymorphic DNAs), therefore they can be applied to large population studies. Received: 16 December 1998 / Accepted: 5 January 1999     

A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.     

Self-incompatibility in Papaver: A MAP kinase signals to trigger programmed cell death

Self-incompatibility (SI) in higher plants prevents inbreeding through specific recognition and rejection of incompatible (“self”) pollen. In Papaver rhoeas, S proteins encoded by the pistil component of the S-locus interact with incompatible pollen, triggering a Ca²⁺-dependent signaling network resulting in programmed cell death (PCD). We recently showed that a mitogen-activated protein kinase (MAPK) is involved in loss of pollen viability, stimulation of caspase-3-like (DEVDase) activity and later DNA fragmentation in incompatible pollen. As p56 appears to be the only MAPK activated by SI, our data suggest that p56 could be the MAPK responsible for mediating SI-induced PCD.Key words: MAPK, self-incompatibility, PCD, caspase-3-like activity, Papaver rhoeas