Biogeographic distribution of five Antarctic cyanobacteria using large-scale k-mer searching with sourmash branchwater.

Cyanobacteria form diverse communities and are important primary producers in Antarctic freshwater environments, but their geographic distribution patterns in Antarctica and globally are still unresolved. There are however few genomes of cultured cyanobacteria from Antarctica available and therefore metagenome-assembled genomes (MAGs) from Antarctic cyanobacteria microbial mats provide an opportunity to explore distribution of uncultured taxa. These MAGs also allow comparison with metagenomes of cyanobacteria enriched communities from a range of habitats, geographic locations, and climates. However, most MAGs do not contain 16S rRNA gene sequences, making a 16S rRNA gene-based biogeography comparison difficult. An alternative technique is to use large-scale k-mer searching to find genomes of interest in public metagenomes. This paper presents the results of k-mer based searches for 5 Antarctic cyanobacteria MAGs from Lake Fryxell and Lake Vanda, assigned the names Phormidium pseudopriestleyi FRX01, Microcoleus sp. MP8IB2.171, Leptolyngbya sp. BulkMat.35, Pseudanabaenaceae cyanobacterium MP8IB2.15, and Leptolyngbyaceae cyanobacterium MP9P1.79 in 498,942 unassembled metagenomes from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The Microcoleus sp. MP8IB2.171 MAG was found in a wide variety of environments, the P. pseudopriestleyi MAG was found in environments with challenging conditions, the Leptolyngbyaceae cyanobacterium MP9P1.79 MAG was only found in Antarctica, and the Leptolyngbya sp. BulkMat.35 and Pseudanabaenaceae cyanobacterium MP8IB2.15 MAGs were found in Antarctic and other cold environments. The findings based on metagenome matches and global comparisons suggest that these Antarctic cyanobacteria have distinct distribution patterns ranging from locally restricted to global distribution across the cold biosphere and other climatic zones.

Pharmacokinetics and effect on renal function and average daily gain in lambs after castration and tail docking, of firocoxib and meloxicam.

AIMS: To evaluate and compare the pharmacokinetics of IM and oral firocoxib, and IM meloxicam, and detect their effect on renal function and average daily gain (ADG) in lambs undergoing tail docking and castration. METHODS: Seventy-five male Romney lambs, aged 3-6 weeks, were randomised into five treatment groups (n = 15 per group): IM firocoxib (1 mg/kg); oral firocoxib (1 mg/kg); IM meloxicam (1 mg/kg); normal saline (approximately 2 mL, oral); or sham. Following the treatment administration, hot-iron tail docking and rubber ring castration were performed in all groups except the sham group, which did not undergo the procedures, but the animals were handled in the same manner as castrated and tail docked lambs. Blood samples were collected before and 1, 2, 4, 6, 8, 24, 48, 72, 96 and 120 hours after treatment administration, and drug concentrations in plasma were quantified by liquid chromatography and mass spectrometry. Plasma urea and creatinine concentrations were determined at a commercial laboratory. Lamb body weights were recorded before and 2, 4 and 8 weeks after tail docking and castration. The pharmacokinetic analysis was carried out using a non-compartmental approach. Between-group and between-time-point differences were compared using mixed model analyses. RESULTS: There was no evidence for a difference in plasma elimination half-life between firocoxib given IM (LSM 18.6 (SE 1.4) hours), firocoxib given orally (LSM 18.2 (SE 1.4) hours), and meloxicam given IM (LSM 17. 0 (SE 1.4) hours). Firocoxib (IM) had a significantly greater volume of distribution (LSM 3.7 (SE 0.2) L/kg) than IM meloxicam (LSM 0.2 (SE 0.2) L/kg). Lambs in the meloxicam group had higher (p < 0.05) plasma urea and creatinine concentrations than those in the firocoxib, saline and sham groups. Lambs' ADG was decreased (p < 0.01) compared to the other treatment groups in the 0-2 week period following meloxicam administration. CONCLUSIONS AND CLINICAL RELEVANCE: Both formulations of firocoxib had a long plasma elimination half-life and large volume of distribution. There was a transient reduction in ADG in the meloxicam group, possibly due to mild renal toxicity. Comparative studies on dose-response effects of firocoxib and meloxicam in lambs following the procedures are required.Abbreviations: ADG: Average daily gain; Cmax: Maximum concentration; COX: Cyclooxygenase; LOD: Limit of detection; NSAID: Non-steroidal anti-inflammatory drugs; CL: Plasma clearance; T1/2el: Plasma elimination half-life; Tmax: Time to achieve Cmax; Vd: Volume of distribution.

Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash.

Sketching methods offer computational biologists scalable techniques to analyze data sets that continue to grow in size. MinHash is one such technique to estimate set similarity that has enjoyed recent broad application. However, traditional MinHash has previously been shown to perform poorly when applied to sets of very dissimilar sizes. FracMinHash was recently introduced as a modification of MinHash to compensate for this lack of performance when set sizes differ. This approach has been successfully applied to metagenomic taxonomic profiling in the widely used tool sourmash gather. Although experimental evidence has been encouraging, FracMinHash has not yet been analyzed from a theoretical perspective. In this paper, we perform such an analysis to derive various statistics of FracMinHash, and prove that although FracMinHash is not unbiased (in the sense that its expected value is not equal to the quantity it attempts to estimate), this bias is easily corrected for both the containment and Jaccard index versions. Next, we show how FracMinHash can be used to compute point estimates as well as confidence intervals for evolutionary mutation distance between a pair of sequences by assuming a simple mutation model. We also investigate edge cases in which these analyses may fail to effectively warn the users of FracMinHash indicating the likelihood of such cases. Our analyses show that FracMinHash estimates the containment of a genome in a large metagenome more accurately and more precisely compared with traditional MinHash, and the point estimates and confidence intervals perform significantly better in estimating mutation distances.

Attaining freshwater and estuarine-water soil saturation in an ecosystem-scale coastal flooding experiment.

Coastal upland forests are facing widespread mortality as sea-level rise accelerates and precipitation and storm regimes change. The loss of coastal forests has significant implications for the coastal carbon cycle; yet, predicting mortality likelihood is difficult due to our limited understanding of disturbance impacts on coastal forests. The manipulative, ecosystem-scale Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Transient saturation (5 h) of the entire soil rooting zone (0-30 cm) across a 2000 m2 coastal forest was attained by delivering 300 m3 of water through a spatially distributed irrigation network at a rate just above the soil infiltration rate. Our water delivery approach also elevated the water table (typically ~ 2 m belowground) and achieved extensive, low-level inundation (~ 8 cm standing water). A TEMPEST simulation approximated a 15-cm rainfall event and based on historic records, was of comparable intensity to a 10-year storm for the area. This characterization was supported by showing that Hurricane Ida's (~ 5 cm rainfall) hydrologic impacts were shorter (40% lower duration) and less expansive (80% less coverage) than those generated through experimental manipulation. Future work will apply TEMPEST treatments to evaluate coastal forest resilience to changing hydrologic disturbance regimes and identify conditions that initiate ecosystem state transitions.

Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets.

BACKGROUND: Long-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates. RESULTS: Our results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (< 2 kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing. CONCLUSIONS: Our critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Could a Focus on the "Why" of Taxonomy Help Taxonomy Better Respond to the Needs of Science and Society?

Genomics has put prokaryotic rank-based taxonomy on a solid phylogenetic foundation. However, most taxonomic ranks were set long before the advent of DNA sequencing and genomics. In this concept paper, we thus ask the following question: should prokaryotic classification schemes besides the current phylum-to-species ranks be explored, developed, and incorporated into scientific discourse? Could such alternative schemes provide better solutions to the basic need of science and society for which taxonomy was developed, namely, precise and meaningful identification? A neutral genome-similarity based framework is then described that could allow alternative classification schemes to be explored, compared, and translated into each other without having to choose only one as the gold standard. Classification schemes could thus continue to evolve and be selected according to their benefits and based on how well they fulfill the need for prokaryotic identification.

Global evaluation of inhibitor impacts on ammonia and nitrous oxide emissions from agricultural soils: A meta-analysis.

Inhibitors are widely considered an efficient tool for reducing nitrogen (N) loss and improving N use efficiency, but their effectiveness is highly variable across agroecosystems. In this study, we synthesized 182 studies (222 sites) worldwide to evaluate the impacts of inhibitors (urease inhibitors [UI], nitrification inhibitors [NI] and combined inhibitors) on crop yields and gaseous N loss (ammonia [NH3 ] and nitrous oxide [N2 O] emissions) and explored their responses to different management and environmental factors including inhibitor application timing, fertilization regime, cropping system, water management, soil properties and climatic conditions using subgroup meta-analysis, meta-regression and multivariate analyses. The UI were most effective in enhancing crop yields (by 5%) and reducing NH3 volatilization (by 51%), whereas NI were most effective at reducing N2 O emissions (by 49%). The application of UI mitigates NH3 loss and increases crop yields especially in high NH3 -N loss scenarios, whereas NI application would minimize the net N2 O emissions and the resultant environmental impacts especially in low NH3 -N loss scenarios. Alternatively, the combined application of UI and NI enables producers to balance crop production and environmental conservation goals without pollution tradeoffs. The inhibitor efficacy for decreasing gaseous N loss was dependent upon soil and climatic conditions and management practices. Notably, both meta-regression and multivariate analyses suggest that inhibitors provide a greater opportunity for reducing fertilizer N inputs in high-N-surplus systems and presumably favor crop yield enhancement under soil N deficiency situations. The pursuit of an improved understanding of the interactions between plant-soil-climate-management systems and different types of inhibitors should continue to optimize the effectiveness of inhibitors for reducing environmental losses while increasing productivity.

Cognitive Motor Dual Task Costs in Older Adults with Motoric Cognitive Risk Syndrome.

The purpose of this study was to characterize Cognitive Motor Dual Task (CMDT) costs for a community-based sample of older adults with Motoric Cognitive Risk Syndrome (MCR), as well as investigate associations between CMDT costs and cognitive performance. Twenty-five community-dwelling older adults (ages 60-89 years) with MCR performed single and dual task complex walking scenarios, as well as a computerized cognitive testing battery. Participants with lower CMDT costs had higher scores on composite measures of Working Memory, Processing Speed, and Shifting, as well as an overall cognitive composite measure. In addition, participants with faster single task gait velocity had higher scores on composite measures of Working Memory, Processing Speed, and overall cognition. Taken together, these results suggest that CMDT paradigms can help to elucidate the interplay between cognitive and motor abilities for older adults with MCR.

Recovery from hybrid breakdown reveals a complex genetic architecture of mitonuclear incompatibilities.

Reproductive isolation is often achieved when genes that are neutral or beneficial in their genomic background become functionally incompatible in a foreign genomic background, causing inviability, sterility or other forms of low fitness in hybrids. Recent studies suggest that mitonuclear interactions are among the initial incompatibilities to evolve at early stages of population divergence across taxa. Yet, the genomic architecture of mitonuclear incompatibilities has rarely been elucidated. We employ an experimental evolution approach starting with low-fitness F2 interpopulation hybrids of the copepod Tigriopus californicus, in which frequencies of compatible and incompatible nuclear alleles change in response to an alternative mitochondrial background. After about nine generations, we observe a generalized increase in population size and in survivorship, suggesting efficiency of selection against maladaptive phenotypes. Whole genome sequencing of evolved populations showed some consistent allele frequency changes across three replicates of each reciprocal cross, but markedly different patterns between mitochondrial backgrounds. In only a few regions (~6.5% of the genome), the same parental allele was overrepresented irrespective of the mitochondrial background. About 33% of the genome showed allele frequency changes consistent with divergent selection, with the location of these genomic regions strongly differing between mitochondrial backgrounds. In 87% and 89% of these genomic regions, the dominant nuclear allele matched the associated mitochondrial background, consistent with mitonuclear co-adaptation. These results suggest that mitonuclear incompatibilities have a complex polygenic architecture that differs between populations, potentially generating genome-wide barriers to gene flow between closely related taxa.

Exploring management strategies to improve yield and mitigate nitrate leaching in a typical radish field in northern China.

It is currently uncertain whether process-based models are capable of assessing crop yield and nitrogen (N) losses while helping to investigate best management practices from vegetable cropping systems. The objectives of this study were to (1) calibrate and evaluate the Denitrification-Decomposition (DNDC) model in simulating crop growth and nitrate leaching in a typical field radish system; (2) optimize management practices to improve radish yield and mitigate nitrate leaching under 20-year climate variability. A five-season in-situ field experiment of spring and autumn radish in northern China was established in the autumn of 2017 and measurements of radish yield, N uptake, soil temperature, soil moisture, drainage, and nitrate leaching were obtained under different N usage. DNDC overall demonstrated "good" to "excellent" performance in simulating radish yield, total biomass, N uptake, and soil temperature across all treatments (6.4% ≤ normalized root mean square error (nRMSE) ≤ 15.5%; 0.12 ≤ Nash-Sutcliffe efficiency (NSE) ≤ 0.88; 0.80 ≤ index of agreement (d) ≤ 0.97). DNDC generally exhibited "fair" performance in estimating soil moisture and drainage (10.9% ≤ nRMSE ≤ 27.2%; -0.18 ≤ NSE ≤ 0.37; 0.69 ≤ d ≤ 0.82) and "good" performance when predicting nitrate leaching (12.4% ≤ nRMSE ≤ 26.7%; -0.59 ≤ NSE ≤ 0.51; 0.68 ≤ d ≤ 0.90). Sensitivity analyses demonstrated that optimized management practices (planting dates, irrigation amount, fertilization rate and timing) could substantially reduce N usage by 40%-50%, irrigation amount by 33%-50%, and nitrate leaching by 86%-95% compared to farmers' practice in radish planting system. This study indicated that a modelling method is helpful for evaluating the biogeochemical effects of management alternatives and identifying optimal management practices in radish production systems of China.

Streamlining data-intensive biology with workflow systems.

As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these practices in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field.

Regional climate influences manure temperature and methane emissions - A pan-Canadian modelling assessment.

This study explores the variation of liquid manure temperature (Tm) and CH4 emissions associated with contrasting regional climates, inter-annual weather variation, and manure storage emptying. As a case-study, six regions across Canada were used, spanning 11°32' latitude and 58°30' longitude. Annual average air temperatures ranged from 3.9 °C (prairie climate) to 10.5 °C (maritime climate), with an overall average of 6.6 °C. A model predicted Tm over 30 years, using daily weather (1971-2000), and over one "normal" year (30-year average weather). Modelled Tm was then used in Manure-DNDC to model daily CH4 emissions. Two manure storage emptying scenarios were simulated: (i) early spring and autumn, or (ii) late spring and autumn. Regional differences were evident as average Tm ranged from 8.9 °C to 14.6 °C across the six locations. Early removal of stored manure led to warmer Tm in all regions, and the most warming occurred in colder regions. Regional climate had a large effect on CH4 emissions (e.g. 1.8× greater in the pacific maritime and great lakes regions than the prairie region). Inter-annual weather variability led to substantial variation in inter-annual CH4 emissions, with coefficient of variation being as high as 20%. The large inter-annual range suggests that field measurements of CH4 emissions need to compare the weather during measurements to historical normals. Early manure storage emptying reduced CH4 emissions (vs late removal) in some regions but had little effect or the opposite effect in other regions. Overall, the results from this modelling study suggest: i) Tm differs substantially from air temperature at all locations, ii) accurate estimates of manure storage CH4 emissions require region-specific calculations using Tm (e.g. in emission inventories), iii) field measurements of CH4 emissions need to consider weather conditions relative to climate normal, and iv) emission mitigation practices will require region-specific measurements to determine impacts.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Modifying fertilizer rate and application method reduces environmental nitrogen losses and increases corn yield in Ontario.

Nitrogen (N) use in corn production is an important driver of nitrous oxide (N2O) emissions and 4R (Right source, Right rate, Right time and Right place) fertilizer practices have been proposed to mitigate emissions. However, combined 4R practices have not been assessed for their potential to reduce N2O emissions at the provincial-scale while also considering trade-offs with other N losses such as leaching or ammonia (NH3) volatilization. The objectives of this study were to develop, validate, and apply a Denitrification-Decomposition model framework at 270 distinct soil-climate regions in Ontario to simulate corn yield and N2O emissions across eleven fertilizer management scenarios during 1986-2015. The results show that broadcasting fertilizer at the surface without incorporation had the highest environmental N loss which was primarily caused by NH3 volatilization. When injected at planting or at sidedress, the NH3 loss was reduced considerably. However, because more N was left in the soil, injection and sidedressing induced more losses by nitrate leaching and N2O emissions. Reduction of N rate as proposed by the DNDC model did not affect crop yield but decreased leaching and N2O emissions. Addition of inhibitors promoted a further reduction in N2O emission (11-16%) although lesser than the reduction in N rate. Overall, our results emphasize that N rate adjustment following improvements in placement, use of inhibitors, and application timings can mitigate N2O emissions by 42-57% and result in 3-4% greater yields compared to baseline scenario in Ontario corn production.

NUTRITIONAL RISK STATUS, DIETARY INTAKE AND COGNITIVE PERFORMANCE IN OLDER ADULTS WITH MOTORIC COGNITIVE RISK SYNDROME.

Background: Modifiable lifestyle factors such as diet are associated with cognitive decline and dementia. Greater understanding of the nutritional intake of older adults who are at increased risk for cognitive decline may allow for the development of more effective dietary interventions to prevent or delay the onset of dementia. Objectives: The purpose of this study was to characterize the nutritional status, diet quality and individual nutritional components of older adults with motoric cognitive risk syndrome (MCR). MCR is a pre-dementia syndrome classified by slow gait speed and subjective memory impairments. Design: Cross-sectional analysis. Setting: A community-based senior center located in an urban setting. Participants: Twenty-five community-dwelling older adults with MCR aged 60-89 yrs. Measurements: Nutritional risk status was determined using the Nestle Mini Nutritional Assessment (MNA). A food frequency questionnaire was used to quantify: overall dietary quality using the Healthy Eating Index (HEI); adherence to the Mediterranean-DASH for Neurodegenerative Delay (MIND) dietary pattern; and intake of individual nutritional components shown to be protective or harmful for cognitive function in older adults. Participants completed a computerized cognitive testing battery to assess cognitive abilities. Results: More than one third (36%) of participants were at increased risk for malnutrition. Participants at lower risk for malnutrition had better working memory (r = 0.40, p = 0.04), executive functioning (r = 0.44, p = 0.03), and overall cognition (r = 0.44, p = 0.03). While participants generally consumed a reasonable quality diet (HEI = 65.15), 48% of participants had poor adherence to a neuroprotective MIND dietary pattern. Higher intake of B-complex vitamins was associated with better task switching (r = 0.40, p ≤ 0.05) and faster processing speeds (r = 0.39, p ≤ 0.05). Higher vitamin C intake was associated with better executive functioning (r = 0.40, p ≤ 0.05). Conclusions: Our findings suggest that a significant proportion of older adults with MCR may be at increased risk for malnutrition. While the diet quality of older adults with MCR appeared to need improvement, future studies should investigate the effects of more specific nutritional interventions, including the MIND diet, on cognition in at-risk older adults.