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There is evidence that mosses with miniature foliage elements have extremely large leaf area index (LAI) values, but it is unclear what canopy traits are responsible for these high LAI values in architecturally divergent mosses, and how the inherent trade-offs limiting maximum LAI in vascular plants can be overcome in mosses. To determine the quantitative significance of different traits in determining LAI, we developed a method to dissect LAI into underlying functionally dependent constituent traits at leaf, shoot and canopy scales. The suites of structural traits were studied altogether for 43 moss canopies from 11 species with contrasting light and water requirements along gap-understory gradients to obtain as large a range of variation in moss architecture as possible and evaluate the differentiation in moss LAI in relation to species ecology. Extensive variation in moss structural traits, 11- (shoot length) to 77-fold (shoot number per area, N S ¯ ), was observed at all structural scales from leaf to canopy. However, LAI only varied nine-fold, as the result of two key trade-offs: leaf size vs number trade-off and shoot leaf area vs shoot density trade-off. Owing to these negative relationships, and greater variability in N S ¯ , LAI primarily scaled with N S ¯ . N S ¯ and LAI increased with site light availability, and LAI was greater in open and dry habitat species. This study highlights a huge structural diversity among moss canopies, but indicates that canopies converge to a much narrower range of LAI due to trait trade-offs such that, counterintuitively, minute leaf size and densely leafed stems are not necessarily responsible for high LAI in mosses.

BACKGROUND: Broad scaling relationships between leaf size and function do not take into account that leaves of different size may contain different fractions of support in petiole and mid-rib. METHODS: The fractions of leaf biomass in petiole, mid-rib and lamina, and the differences in chemistry and structure among mid-ribs, petioles and laminas were investigated in 122 species of contrasting leaf size, life form and climatic distribution to determine the extent to which differences in support modify whole-lamina and whole-leaf structural and chemical characteristics, and the extent to which size-dependent support investments are affected by plant life form and site climate. KEY RESULTS: For the entire data set, leaf fresh mass varied over five orders of magnitude. The percentage of dry mass in mid-rib increased strongly with lamina size, reaching more than 40 % in the largest laminas. The whole-leaf percentage of mid-rib and petiole increased with leaf size, and the overall support investment was more than 60 % in the largest leaves. Fractional support investments were generally larger in herbaceous than in woody species and tended to be lower in Mediterranean than in cool temperate and tropical plants. Mid-ribs and petioles had lower N and C percentages, and lower dry to fresh mass ratio, but greater density (mass per unit volume) than laminas. N percentage of lamina without mid-rib was up to 40 % higher in the largest leaves than the total-lamina (lamina and mid-rib) N percentage, and up to 60 % higher than whole-leaf N percentage, while lamina density calculated without mid-rib was up to 80 % less than that with the mid-rib. For all leaf compartments, N percentage was negatively associated with density and dry to fresh mass ratio, while C percentage was positively linked to these characteristics, reflecting the overall inverse scaling between structural and physiological characteristics. However, the correlations between N and C percentages and structural characteristics differed among mid-ribs, petioles and laminas, implying that the mass-weighted average leaf N and C percentage, density, and dry to fresh mass ratio can have different functional values depending on the importance of within-leaf support investments. CONCLUSIONS: These data demonstrate that variation in leaf size is associated with major changes in within-leaf support investments and in large modifications in integrated leaf chemical and structural characteristics. These size-dependent alterations can importantly affect general leaf structure vs. function scaling relationships. These data further demonstrate important life-form effects on and climatic differentiation in foliage support costs.

$\\bullet$ The implications of extensive variation in leaf size for biomass distribution between physiological and support tissues and for overall leaf physiological activity are poorly understood. Here, we tested the hypotheses that increases in leaf size result in enhanced whole-plant support investments, especially in compound-leaved species, and that accumulation of support tissues reduces average leaf nitrogen (N) content per unit dry mass ($N_M$), a proxy for photosynthetic capacity. $\\bullet$ Leaf biomass partitioning among the lamina, mid-rib and petiole, and whole-plant investments in leaf support (within-leaf and stem) were studied in 33 simple-leaved and 11 compound-leaved species. $\\bullet$ Support investments in mid-ribs and petioles increased with leaf size similarly in simple leaves and leaflets of compound leaves, but the overall support mass fraction within leaves was larger in compound-leaved species as a result of prominent rachises. Within-leaf and within-plant support mass investments were negatively correlated. Therefore, the total plant support fraction was independent of leaf size and lamina dissection. Because of the lower $N_M$ of support biomass, the difference in $N_M$ between the entire leaf and the photosynthetic lamina increased with leaf size. $\\bullet$ We conclude that whole-plant support costs are weakly size-dependent, but accumulation of support structures within the leaf decreases whole-leaf average $N_M$, potentially reducing the integrated photosynthetic activity of larger leaves.

Shoots and foliage elements of shade‐intolerant conifers strongly vary in size, but the effects of size on shoot light‐harvesting efficiency have not been quantified. We investigated shoot adaptation to seasonal average integrated quantum flux density in gymnospermsPinus palustrisMill.,P. patulaSchlect. & Cham., andP. radiataD. Don. and angiospermCasuarina glaucaSieb. ex Spreng. In addition,P. sylvestrisL., sampled from infertile and fertile sites, andP. taedaL. were included to test for general correlations among shoot architecture and size. All studied species possess needle‐like photosynthetic elements. A shoot model was fitted to the data to separate the determinants of shoot light‐harvesting efficiency. The model estimated shoot light harvesting on the basis of angular distribution of foliage surface areas, degree of spatial clumping, foliage area density in shoot volume, and beam path length in shoot volume. Increases in irradiance primarily led to greater foliage aggregation, greater foliage area density in shoot volume, and to a minor degree, to changes in foliage area angular distribution. Greater foliage aggregation resulted in lower efficiency of light harvesting but greater investment of foliar biomass in high light where the photosynthetic returns are greater. The species behave similarly except that the light‐harvesting characteristics ofP. patula, which has long, strongly bending needles, were independent of light. In all species, the shoots were larger in higher irradiance, and the fraction of biomass in shoot axis increased with increasing irradiance, indicating greater costs for light harvesting in high light. There were further significant species differences in light‐harvesting efficiency that were linked to differences in foliage and shoot size. Foliage element length varied between 1.1 and 31.4 cm and shoot axis length between 1.2 and 36.5 cm among the species, leading to 4 orders of magnitude variation in shoot cylinder volume and three orders of magnitude variation in foliage area density (ρ); ρ decreased with increasing foliage element length and shoot volume. The degree of foliage clumping scaled positively with ρ and negatively with foliage element length. Foliage clumping was positively associated with foliage dry mass to shoot silhouette area ratio, signifying a trade‐off between efficient light harvesting and large photosynthetic biomass accumulation. These data demonstrate a general increase of light‐harvesting efficiency with increasing length of foliage elements, but they also demonstrate that this increase is limited by enhanced bending of longer foliage elements and by augmented support costs.

Aarno Palotie and colleagues present results of a large genome-wide association study of migraine. They identified significant associations at 38 distinct loci and found enrichment for genes expressed in vascular and smooth muscle tissues. Migraine is a debilitating neurological disorder affecting around one in seven people worldwide, but its molecular mechanisms remain poorly understood. There is some debate about whether migraine is a disease of vascular dysfunction or a result of neuronal dysfunction with secondary vascular changes. Genome-wide association (GWA) studies have thus far identified 13 independent loci associated with migraine. To identify new susceptibility loci, we carried out a genetic study of migraine on 59,674 affected subjects and 316,078 controls from 22 GWA studies. We identified 44 independent single-nucleotide polymorphisms (SNPs) significantly associated with migraine risk ( P < 5 × 10 −8 ) that mapped to 38 distinct genomic loci, including 28 loci not previously reported and a locus that to our knowledge is the first to be identified on chromosome X. In subsequent computational analyses, the identified loci showed enrichment for genes expressed in vascular and smooth muscle tissues, consistent with a predominant theory of migraine that highlights vascular etiologies.

Antibiotic exposure at the beginning of life can lead to increased antimicrobial resistance and perturbations of the developing microbiome. Early-life microbiome disruption increases the risks of developing chronic diseases later in life. Fear of missing evolving neonatal sepsis is the key driver for antibiotic overtreatment early in life. Bias (a systemic deviation towards overtreatment) and noise (a random scatter) affect the decision-making process. In this perspective, we advocate for a factual approach quantifying the burden of treatment in relation to the burden of disease balancing antimicrobial stewardship and effective sepsis management. Fear of missing neonatal sepsis has led to early in life antibiotic administration, even without culture-proven sepsis. Here, the authors discuss the potential impact on antimicrobial resistance, and chronic disease later in life, due to effect on the developing microbiome, suggesting a factual based approach in quantifying burden of treatment in relation to the burden of disease.

Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. Polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present WhatsHap polyphase , a novel two-stage approach that addresses these challenges by (i) clustering reads and (ii) threading the haplotypes through the clusters. Our method outperforms the state-of-the-art in terms of phasing quality. Using a real tetraploid potato dataset, we demonstrate how to assemble local genomic regions of interest at the haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap.

Potato is one of the world’s major staple crops, and like many important crop plants, it has a polyploid genome. Polyploid haplotype assembly poses a major computational challenge. We introduce a novel strategy for the assembly of polyploid genomes and present an assembly of the autotetraploid potato cultivar Altus. Our method uses low-depth sequencing data from an offspring population to achieve chromosomal clustering and haplotype phasing on the assembly graph. Our approach generates high-quality assemblies of individual chromosomes with haplotype-specific sequence resolution of whole chromosome arms and can be applied in common breeding scenarios where collections of offspring are available.

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud-clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (−7.4 ppb versus −5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since 21 November 2023, the RFC approach is part of the operational processing chain of the European Space Agency (ESA). For now, the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data are unavailable do we resort to the RFC cloud mask.