PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 17211583

A method for quantitative analysis of spatially variable physiological processes across leaf surfaces.

Mihai Aldea·Thomas D Frank·Evan H DeLucia
View original source ↗Export metadata ↓

Abstract

Many physiological processes are spatially variable across leaf surfaces. While maps of photosynthesis, stomatal conductance, gene expression, water transport, and the production of reactive oxygen species (ROS) for individual leaves are readily obtained, analytical methods for quantifying spatial heterogeneity and combining information gathered from the same leaf but with different instruments are not widely used. We present a novel application of tools from the field of geographical imaging to the multivariate analysis of physiological images. Procedures for registration and resampling, cluster analysis, and classification provide a general framework for the analysis of spatially resolved physiological data. Two experiments were conducted to illustrate the utility of this approach. Quantitative analysis of images of chlorophyll fluorescence and the production of ROS following simultaneous exposure of soybean leaves to atmospheric O3 and soybean mosaic virus revealed that areas of the leaf where the operating quantum efficiency of PSII was depressed also experienced an accumulation of ROS. This correlation suggests a causal relationship between oxidative stress and inhibition of photosynthesis. Overlaying maps of leaf surface temperature and chlorophyll fluorescence following a photoinhibition treatment indicated that areas with low operating quantum efficiency of PSII also experienced reduced stomatal conductance (high temperature). While each of these experiments explored the covariance of two processes by overlaying independent images gathered with different instruments, the same procedures can be used to analyze the covariance of information from multiple images. The application of tools from geographic image analysis to physiological processes occurring over small spatial scales will help reveal the mechanisms generating spatial variation across leaves.

Explore related subjects

AlgorithmsLightPhotosynthesisPhotosystem II Protein ComplexPlant LeavesReactive Oxygen SpeciesGlycine maxTemperatureJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mihai Aldea, Thomas D Frank, Evan H DeLucia. 2007-01-09. A method for quantitative analysis of spatially variable physiological processes across leaf surfaces.. https://doi.org/10.1007/s11120-006-9119-z

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗
PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗
PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗