Poly(A)-binding protein is an ataxin-2 chaperone that regulates biomolecular condensates.

Biomolecular condensation underlies the biogenesis of an expanding array of membraneless assemblies, including stress granules (SGs), which form under a variety of cellular stresses. Advances have been made in understanding the molecular grammar of a few scaffold proteins that make up these phases, but how the partitioning of hundreds of SG proteins is regulated remains largely unresolved. While investigating the rules that govern the condensation of ataxin-2, an SG protein implicated in neurodegenerative disease, we unexpectedly identified a short 14 aa sequence that acts as a condensation switch and is conserved across the eukaryote lineage. We identify poly(A)-binding proteins as unconventional RNA-dependent chaperones that control this regulatory switch. Our results uncover a hierarchy of cis and trans interactions that fine-tune ataxin-2 condensation and reveal an unexpected molecular function for ancient poly(A)-binding proteins as regulators of biomolecular condensate proteins. These findings may inspire approaches to therapeutically target aberrant phases in disease.

Direct prediction of intrinsically disordered protein conformational properties from sequence.

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep-learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence-ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes.

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity.

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits. Spectral properties evolutionarily conserved in plants provide the opportunity to spatially aggregate species into lineages (interpreted as "lineage functional types" or LFT) with improved classification accuracy. In this study, we use Airborne Visible/Infrared Imaging Spectrometer data from the 2013 Hyperspectral Infrared Imager campaign over the southern Sierra Nevada, California flight box, to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from 1372 surveys and a phylogeny representing 1,572 species. Despite temporal and spatial differences in our training data, we classified plant lineages with moderate reliability (Kappa = 0.76) and overall classification accuracy of 80.9%. We present an assessment of classification error and detail study limitations to facilitate future LFT development. This work demonstrates that lineage-based methods may be a promising way to leverage the new-generation high-resolution and high return-interval hyperspectral data planned for the forthcoming satellite missions with sparsely sampled existing ground-based ecological data.

No evidence of canopy-scale leaf thermoregulation to cool leaves below air temperature across a range of forest ecosystems.

Understanding and predicting the relationship between leaf temperature (Tleaf) and air temperature (Tair) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime Tleaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below Tair at higher temperatures (i.e., > ∼25-30°C) leading to slopes <1 in Tleaf/Tair relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature (Tcan) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to Tcan/Tair slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the Tcan/Tair relationship. Canopy structure also plays an important role in Tcan dynamics. Future climate warming is likely to lead to even greater Tcan, with attendant impacts on forest carbon cycling and mortality risk.

Landscape-scale forest cover drives the predictability of forest regeneration across the Neotropics.

Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in nonlinear ways. Using a multi-scale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density and basal area are less predictable in landscapes (4 km radius) with intermediate (40-60%) forest cover than in landscapes with high (greater than 60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation of key ecological factors for forest recovery (e.g. seed dispersal and seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only species richness is positively related to forest cover in relatively small (1 km radius) landscapes. These findings highlight the importance of using a spatially explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1-4 km radius.

Evolutionary lineage explains trait variation among 75 coexisting grass species.

Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C3 vs C4 plant functional types (PFTs). Plant functional type groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity. We measured 11 structural and physiological traits in situ from 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species. Critically, we found evidence that grass traits varied among lineages, including independent origins of C4 photosynthesis. Using a rigorous model selection approach, tribe was included in the top models for five of nine traits for perennial species. Tribes were separable in a multivariate and phylogenetically controlled analysis of traits, owing to coordination of important structural and ecophysiological characteristics. Our findings suggest grouping grass species by photosynthetic pathway overlooks variation in several functional traits, particularly for C4 species. These results indicate that further assessment of lineage-based differences at other sites and across other grass species distributions may improve representation of C4 species in trait comparison analyses and modeling investigations.

Synthesis of 7-azabicyclo[4.3.1]decane ring systems from tricarbonyl(tropone)iron via intramolecular Heck reactions.

The 7-azabicyclo[4.3.1]decane ring system, common to a number of biologically active alkaloids, was accessed from tropone (via its η4-diene complex with Fe(CO)3) in a short sequence of steps: 1) nucleophilic amine addition and subsequent Boc-protection, 2) photochemical demetallation of the iron complex, and 3) an intramolecular Heck reaction. Minor modifications to the protocol enabled access to the related 2-azabicyclo[4.4.1]undecane system, albeit in lower yield.

Representing plant diversity in land models: An evolutionary approach to make "Functional Types" more functional.

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure.

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

Clustering of Aromatic Residues in Prion-like Domains Can Tune the Formation, State, and Organization of Biomolecular Condensates.

In immature oocytes, Balbiani bodies are conserved membraneless condensates implicated in oocyte polarization, the organization of mitochondria, and long-term organelle and RNA storage. In Xenopus laevis, Balbiani body assembly is mediated by the protein Velo1. Velo1 contains an N-terminal prion-like domain (PLD) that is essential for Balbiani body formation. PLDs have emerged as a class of intrinsically disordered regions that can undergo various different types of intracellular phase transitions and are often associated with dynamic, liquid-like condensates. Intriguingly, the Velo1 PLD forms solid-like assemblies. Here we sought to understand why Velo1 phase behavior appears to be biophysically distinct from that of other PLD-containing proteins. Through bioinformatic analysis and coarse-grained simulations, we predict that the clustering of aromatic residues and the amino acid composition of residues between aromatics can influence condensate material properties, organization, and the driving forces for assembly. To test our predictions, we redesigned the Velo1 PLD to test the impact of targeted sequence changes in vivo. We found that the Velo1 design with evenly spaced aromatic residues shows rapid internal dynamics, as probed by fluorescent recovery after photobleaching, even when recruited into Balbiani bodies. Our results suggest that Velo1 might have been selected in evolution for distinctly clustered aromatic residues to maintain the structure of Balbiani bodies in long-lived oocytes. In general, our work identifies several tunable parameters that can be used to augment the condensate material state, offering a road map for the design of synthetic condensates.

Imbalance of tyrosine by modulating TyrA arogenate dehydrogenases impacts growth and development of Arabidopsis thaliana.

l-Tyrosine is an essential aromatic amino acid required for the synthesis of proteins and a diverse array of plant natural products; however, little is known on how the levels of tyrosine are controlled in planta and linked to overall growth and development. Most plants synthesize tyrosine by TyrA arogenate dehydrogenases, which are strongly feedback-inhibited by tyrosine and encoded by TyrA1 and TyrA2 genes in Arabidopsis thaliana. While TyrA enzymes have been extensively characterized at biochemical levels, their in planta functions remain uncertain. Here we found that TyrA1 suppression reduces seed yield due to impaired anther dehiscence, whereas TyrA2 knockout leads to slow growth with reticulate leaves. The tyra2 mutant phenotypes were exacerbated by TyrA1 suppression and rescued by the expression of TyrA2, TyrA1 or tyrosine feeding. Low-light conditions synchronized the tyra2 and wild-type growth, and ameliorated the tyra2 leaf reticulation. After shifting to normal light, tyra2 transiently decreased tyrosine and subsequently increased aspartate before the appearance of the leaf phenotypes. Overexpression of the deregulated TyrA enzymes led to hyper-accumulation of tyrosine, which was also accompanied by elevated aspartate and reticulate leaves. These results revealed that TyrA1 and TyrA2 have distinct and overlapping functions in flower and leaf development, respectively, and that imbalance of tyrosine, caused by altered TyrA activity and regulation, impacts growth and development of Arabidopsis. The findings provide critical bases for improving the production of tyrosine and its derived natural products, and further elucidating the coordinated metabolic and physiological processes to maintain tyrosine levels in plants.

Lineage-based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models.

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3 grasses and (sub)tropical C4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

Synthetic Entry to the 2-Azatricyclo[4.3.2.04,9]undecane Ring System via Tropone.

A synthesis of the 2-azatricyclo[4.3.2.04,9]undecane ring system-a hitherto unreported bridged azatricyclic ring system-beginning from tricarbonyl(tropone)iron and allylamine was accomplished in three steps: (1) aza-Michael addition of allylamine to tricarbonyl(tropone)iron; (2) Boc-protection of the resulting secondary amine; and (3) oxidative demetallation leading to a spontaneous intramolecular Diels-Alder reaction. The effect of a variety of parameters on the intramolecular Diels-Alder reaction was investigated, including diene and dienophile substitution patterns and dienophile tether length.

Ancient DNA and bioarchaeological perspectives on European and African diversity and relationships on the colonial Delaware frontier.

OBJECTIVES: Ancient DNA (aDNA) and standard osteological analyses applied to 11 skeletons at a late 17th to early 18th century farmstead site in Delaware to investigate the biological and social factors of settlement and slavery in colonial America. MATERIALS AND METHODS: Osteological analysis and mitochondrial DNA (mtDNA) sequencing were conducted for all individuals and the resulting data contextualized with archaeological and documentary evidence. RESULTS: Individuals of European and African descent were spatially separated in this colonial cemetery. The skeletal remains exhibited differences in osteological features and maternal genetic ancestry. A specific mtDNA haplotype appeared in a subset of the European-descended individuals suggesting they were maternally related. Individuals of African descent were not maternally related, and instead showed a diversity of haplotypes affiliated with present-day Western, Central, and Eastern regions of Africa. DISCUSSION: Along with the bioarchaeological and documentary evidence, the aDNA findings contribute to our understanding of life on the colonial Delaware frontier. Evidence of maternal relatedness among European-descended individuals at the site demonstrates kin-based settlements in 17th century Delaware and provides preliminary identifications of individuals. The maternal genetic diversity of the individuals with African descent aligns with the routes of the trans-Atlantic slave trade but broadens our understanding of the ancestries of persons involved in it. Burial positioning, osteological pathology, and lack of maternal kinship among individuals of African descent provide tangible evidence for the emergence of racialized labor and society in Delaware during the late 17th century.

Herbivory and eutrophication mediate grassland plant nutrient responses across a global climatic gradient.

Plant stoichiometry, the relative concentration of elements, is a key regulator of ecosystem functioning and is also being altered by human activities. In this paper we sought to understand the global drivers of plant stoichiometry and compare the relative contribution of climatic vs. anthropogenic effects. We addressed this goal by measuring plant elemental (C, N, P and K) responses to eutrophication and vertebrate herbivore exclusion at eighteen sites on six continents. Across sites, climate and atmospheric N deposition emerged as strong predictors of plot-level tissue nutrients, mediated by biomass and plant chemistry. Within sites, fertilization increased total plant nutrient pools, but results were contingent on soil fertility and the proportion of grass biomass relative to other functional types. Total plant nutrient pools diverged strongly in response to herbivore exclusion when fertilized; responses were largest in ungrazed plots at low rainfall, whereas herbivore grazing dampened the plant community nutrient responses to fertilization. Our study highlights (1) the importance of climate in determining plant nutrient concentrations mediated through effects on plant biomass, (2) that eutrophication affects grassland nutrient pools via both soil and atmospheric pathways and (3) that interactions among soils, herbivores and eutrophication drive plant nutrient responses at small scales, especially at water-limited sites.

Vitamin and mineral supplementation for preventing dementia or delaying cognitive decline in people with mild cognitive impairment.

BACKGROUND: Vitamins and minerals have many functions in the nervous system which are important for brain health. It has been suggested that various different vitamin and mineral supplements might be useful in maintaining cognitive function and delaying the onset of dementia. In this review, we sought to examine the evidence for this in people who already had mild cognitive impairment (MCI). OBJECTIVES: To evaluate the effects of vitamin and mineral supplementation on cognitive function and the incidence of dementia in people with mild cognitive impairment. SEARCH METHODS: We searched ALOIS, the Cochrane Dementia and Cognitive Improvement Group's (CDCIG) specialised register, as well as MEDLINE, Embase, PsycINFO, CENTRAL, CINAHL, LILACs, Web of Science Core Collection, ClinicalTrials.gov, and the WHO Portal/ICTRP, from inception to 25 January 2018. SELECTION CRITERIA: We included randomised or quasi-randomised, placebo-controlled trials which evaluated orally administered vitamin or mineral supplements in participants with a diagnosis of mild cognitive impairment and which assessed the incidence of dementia or cognitive outcomes, or both. We were interested in studies applicable to the general population of older people and therefore excluded studies in which participants had severe vitamin or mineral deficiencies. DATA COLLECTION AND ANALYSIS: We sought data on our primary outcomes of dementia incidence and overall cognitive function and on secondary outcomes of episodic memory, executive function, speed of processing, quality of life, functional performance, clinical global impression, adverse events, and mortality. We conducted data collection and analysis according to standard Cochrane systematic review methods. We assessed the risk of bias of included studies using the Cochrane 'Risk of bias' assessment tool. We grouped vitamins and minerals according to their putative mechanism of action and, where we considered it to be clinically appropriate, we pooled data using random-effects methods. We used GRADE methods to assess the overall quality of evidence for each comparison and outcome. MAIN RESULTS: We included five trials with 879 participants which investigated B vitamin supplements. In four trials, the intervention was a combination of vitamins B6, B12, and folic acid; in one, it was folic acid only. Doses varied. We considered there to be some risks of performance and attrition bias and of selective outcome reporting among these trials. Our primary efficacy outcomes were the incidence of dementia and scores on measures of overall cognitive function. None of the trials reported the incidence of dementia and the evidence on overall cognitive function was of very low-quality. There was probably little or no effect of B vitamins taken for six to 24 months on episodic memory, executive function, speed of processing, or quality of life. The evidence on our other secondary clinical outcomes, including harms, was very sparse or very low-quality. There was evidence from one study that there may be a slower rate of brain atrophy over two years in participants taking B vitamins. The same study reported subgroup analyses based on the level of serum homocysteine (tHcy) at baseline and found evidence that B vitamins may improve episodic memory in those with tHcy above the median at baseline.We included one trial (n = 516) of vitamin E supplementation. Vitamin E was given as 1000 IU of alpha-tocopherol twice daily. We considered this trial to be at risk of attrition and selective reporting bias. There was probably no effect of vitamin E on the probability of progression from MCI to Alzheimer's dementia over three years (HR 1.02; 95% CI 0.74 to 1.41; n = 516; 1 study, moderate-quality evidence). There was also no evidence of an effect at intermediate time points. The available data did not allow us to conduct analyses, but the authors reported no significant effect of three years of supplementation with vitamin E on overall cognitive function, episodic memory, speed of processing, clinical global impression, functional performance, adverse events, or mortality (five deaths in each group). We considered this to be low-quality evidence.We included one trial (n = 256) of combined vitamin E and vitamin C supplementation and one trial (n = 26) of supplementation with chromium picolinate. In both cases, there was a single eligible cognitive outcome, but we considered the evidence to be very low-quality and so could not be sure of any effects. AUTHORS' CONCLUSIONS: The evidence on vitamin and mineral supplements as treatments for MCI is very limited. Three years of treatment with high-dose vitamin E probably does not reduce the risk of progression to dementia, but we have no data on this outcome for other supplements. Only B vitamins have been assessed in more than one RCT. There is no evidence for beneficial effects on cognition of supplementation with B vitamins for six to 24 months. Evidence from a single study of a reduced rate of brain atrophy in participants taking vitamin B and a beneficial effect of vitamin B on episodic memory in those with higher tHcy at baseline warrants attempted replication.

Error propagation in spatial modeling of public health data: a simulation approach using pediatric blood lead level data for Syracuse, New York.

Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results.

Efficacy and Safety of Glutamine-supplemented Parenteral Nutrition in Surgical ICU Patients: An American Multicenter Randomized Controlled Trial.

OBJECTIVE: To determine whether glutamine (GLN)-supplemented parenteral nutrition (PN) improves clinical outcomes in surgical intensive care unit (SICU) patients. SUMMARY BACKGROUND DATA: GLN requirements may increase with critical illness. GLN-supplemented PN may improve clinical outcomes in SICU patients. METHODS: A parallel-group, multicenter, double-blind, randomized, controlled clinical trial in 150 adults after gastrointestinal, vascular, or cardiac surgery requiring PN and SICU care. Patients were without significant renal or hepatic failure or shock at entry. All received isonitrogenous, isocaloric PN [1.5 g/kg/d amino acids (AAs) and energy at 1.3× estimated basal energy expenditure]. Controls (n = 75) received standard GLN-free PN (STD-PN); the GLN group (n = 75) received PN containing alanyl-GLN dipeptide (0.5 g/kg/d), proportionally replacing AA in PN (GLN-PN). Enteral nutrition (EN) was advanced and PN weaned as indicated. Hospital mortality and infections were primary endpoints. RESULTS: Baseline characteristics, days on study PN and daily macronutrient intakes via PN and EN, were similar between groups. There were 11 hospital deaths (14.7%) in the GLN-PN group and 13 deaths in the STD-PN group (17.3%; difference, -2.6%; 95% confidence interval, -14.6% to 9.3%; P = 0.66). The 6-month cumulative mortality was 31.4% in the GLN-PN group and 29.7% in the STD-PN group (P = 0.88). Incident bloodstream infection rate was 9.6 and 8.4 per 1000 hospital days in the GLN-PN and STD-PN groups, respectively (P = 0.73). Other clinical outcomes and adverse events were similar. CONCLUSIONS: PN supplemented with GLN dipeptide was safe, but did not alter clinical outcomes among SICU patients.

Leaf thickness controls variation in leaf mass per area (LMA) among grazing-adapted grasses in Serengeti.

Leaf mass per area (LMA) is a primary plant functional trait that represents the cost of constructing a leaf. Ultimately, plants modify LMA by altering leaf thickness (LT), leaf dry matter content (LDMC), or both. While LMA can be modified through both of these constituents, studies of LMA have found that there is variation in whether LT or LDMC changes are responsible for LMA-and the relationships change depending on the species or functional groups being compared. In this study, we used a phylogenetic framework to determine that evolutionary shifts in LMA are driven by LT, and not LDMC, among 45 Serengeti grass species. We considered two alternative hypotheses that could result in evolutionary correlation of LMA on LT but not LDMC: either (1) LT is more labile than LDMC-and is therefore a less costly means to change LMA or (2) LDMC is tightly coupled to a different dimension of leaf variation (e.g., leaf hydraulics), leaving LT as the source of variation in LMA. LT was not more labile than LDMC, leading us to conclude that the evolution of LMA has been shaped by LT because LDMC is responding to other demands on leaf physiology. We speculate that leaf hydraulics provide this constraint on LDMC. The decoupling of LDMC from LT may allow plants to better optimize resource allocation in ecosystems where gradients in light competition, herbivory, and aridity place competing demands on leaf economics.