Direct comparison of four methods to construct xylem vulnerability curves: Differences among techniques are linked to vessel network characteristics

During periods of dehydration, water transport through xylem conduits can become blocked by embolism formation. Xylem embolism compromises water supply to leaves and may lead to losses in productivity or plant death. Vulnerability curves (VCs) characterize plant losses in conductivity as xylem pressures decrease. VCs are widely used to characterize and predict plant water use at different levels of water availability. Several methodologies for constructing VCs exist and sometimes produce different results for the same plant material. We directly compared four VC construction methods on stems of black cottonwood (Populus trichocarpa), a model tree species: dehydration, centrifuge, X‐ray–computed microtomography (microCT), and optical. MicroCT VC was the most resistant, dehydration and centrifuge VCs were intermediate, and optical VC was the most vulnerable. Differences among VCs were not associated with how cavitation was induced but were related to how losses in conductivity were evaluated: measured hydraulically (dehydration and centrifuge) versus evaluated from visual information (microCT and optical). Understanding how and why methods differ in estimating vulnerability to xylem embolism is important for advancing knowledge in plant ecophysiology, interpreting literature data, and using accurate VCs in water flux models for predicting plant responses to drought.     

Hair Mineral and Trace Element Content in Children with Down’s Syndrome

Biological Trace Element Research - Chronic exposure to lead causes disruption to energy production mechanisms and tissue damage, in particular through its binding to thiol groups and competition...     

Correction to: Effect of Selenium on Lipid and Amino Acid Metabolism in Yeast Cells

Biological Trace Element Research - The authors forgot to include the following information in Materials and Methods.     

Prebiotic Syntheses Under Shock in the Water – Formamide – Potassium Bicarbonate – Sodium Hydroxide System

Origins of Life and Evolution of Biospheres - Syntheses under shock in nitrogen bubbled samples of the water – formamide – bicarbonate – sodium hydroxide system at...     

Stability and instability processes in the calli of Fagopyrum tataricum that have different morphogenic potentials

Plant Cell, Tissue and Organ Culture (PCTOC) - The morphogenic callus (MC) of Fagopyrum tataricum contains a large amount of flavonoids, especially rutin, and exhibits a high level of antioxidant...     

Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO)

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The “Continuous Automated Model EvaluatiOn (CAMEO)” platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4-day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross-validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO—either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo-oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods.