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.

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.

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.

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.

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.

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.

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.

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.

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.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.

Monomeric M2e antigen in VesiVax® liposomes stimulates protection against type a strains of influenza comparable to liposomes with multimeric forms of M2e.

Given the interest in the ectodomain of the matrix 2 (M2e) channel protein as a target for development of a universal influenza vaccine, we examined the role of the antigen configuration of M2e in generating a protective immune response. A series of M2e mutations and a truncated M2e segment were prepared as a means of controlling the formation of monomer, dimer, and higher order multimeric forms of M2e. Each of these M2e peptides was incorporated into a liposome-based vaccine technology platform previously shown to stimulate a protective response to influenza A infection using M2e as a mixture of monomers, dimers and multimers (L-M2e1-HD/MPL). Our results using these modified forms of M2e produced 90-100% survival following lethal challenge with H1N1 (A/PR/8/34) in both inbred BALB/c and outbred Swiss Webster mice vaccinated with a truncated monomeric form of the M2 protein, M2e1-15 in liposomes. These observations show that a tetrameric configuration is not required to elicit significant protection when the M2e antigen is formulated in immunogenic liposomes and further, that the first 15 amino acids of M2e likely play a primary role in providing the protective immune response.

Underlying sources of cognitive-anatomical variation in multi-modal neuroimaging and cognitive testing.

Healthy adults have robust individual differences in neuroanatomy and cognitive ability not captured by demographics or gross morphology (Luders, Narr, Thompson, & Toga, 2009). We used a hierarchical independent component analysis (hICA) to create novel characterizations of individual differences in our participants (N=190). These components fused data across multiple cognitive tests and neuroanatomical variables. The first level contained four independent, underlying sources of phenotypic variance that predominately modeled broad relationships within types of data (e.g., "white matter," or "subcortical gray matter"), but were not reflective of traditional individual difference measures such as sex, age, or intracranial volume. After accounting for the novel individual difference measures, a second level analysis identified two underlying sources of phenotypic variation. One of these made strong, joint contributions to both the anatomical structures associated with the core fronto-parietal "rich club" network (van den Heuvel & Sporns, 2011), and to cognitive factors. These findings suggest that a hierarchical, data-driven approach is able to identify underlying sources of individual difference that contribute to cognitive-anatomical variation in healthy young adults.

Visualization of atherosclerosis as detected by coronary artery calcium and carotid intima-media thickness reveals significant atherosclerosis in a cross-sectional study of psoriasis patients in a tertiary care center.

BACKGROUND: Psoriasis is a chronic inflammatory disease of the skin and joints that may also have systemic inflammatory effects, including the development of cardiovascular disease (CVD). Multiple epidemiologic studies have demonstrated increased rates of CVD in psoriasis patients, although a causal link has not been established. A growing body of evidence suggests that sub-clinical systemic inflammation may develop in psoriasis patients, even from a young age. We aimed to evaluate the prevalence of atherosclerosis and identify specific clinical risk factors associated with early vascular inflammation. METHODS: We conducted a cross-sectional study of a tertiary care cohort of psoriasis patients using coronary artery calcium (CAC) score and carotid intima-media thickness (CIMT) to detect atherosclerosis, along with high sensitivity C-reactive protein (hsCRP) to measure inflammation. Psoriasis patients and controls were recruited from our tertiary care dermatology clinic. Presence of atherosclerosis was defined using validated numeric values within CAC and CIMT imaging. Descriptive data comparing groups was analyzed using Welch's t test and Pearson Chi square tests. Logistic regression was used to analyze clinical factors associated with atherosclerosis, and linear regression to evaluate the relationship between psoriasis and hsCRP. RESULTS: 296 patients were enrolled, with 283 (207 psoriatic and 76 controls) having all data for the hsCRP and atherosclerosis analysis. Atherosclerosis was found in 67.6 % of psoriasis subjects versus 52.6 % of controls; Psoriasis patients were found to have a 2.67-fold higher odds of having atherosclerosis compared to controls [95 % CI (1.2, 5.92); p = 0.016], after adjusting for age, gender, race, BMI, smoking, HDL and hsCRP. In addition, a non-significant trend was found between HsCRP and psoriasis severity, as measured by PASI, PGA, or BSA, again after adjusting for confounders. CONCLUSIONS: A tertiary care cohort of psoriasis patients have a high prevalence of early atherosclerosis, increased hsCRP, and psoriasis remains a risk factor for the presence of atherosclerosis even after adjustment of key confounding clinical factors. Psoriasis may contribute to an accelerated systemic inflammatory cascade resulting in increased risk of CVD and CV events.

Prevention of antibiotic-associated metabolic syndrome in mice by intestinal alkaline phosphatase.

AIMS: To examine whether co-administration of intestinal alkaline phosphatase (IAP) with antibiotics early in life may have a preventive role against metabolic syndrome (MetS) in mice. METHODS: A total of 50 mice were allocated to four treatment groups after weaning. Mice were treated with azithromycin (AZT) ± IAP, or with no AZT ± IAP, for three intermittent 7-day cycles. After the last treatment course, the mice were administered a regular chow diet for 5 weeks and subsequently a high-fat diet for 5 weeks. Body weight, food intake, water intake, serum lipids, glucose levels and liver lipids were compared. 16S rRNA gene pyrosequencing was used to determine the differences in microbiome composition. RESULTS: Exposure to AZT early in life rendered mice susceptible to MetS in adulthood. Co-administration of IAP with AZT completely prevented this susceptibility by decreasing total body weight, serum lipids, glucose levels and liver lipids to the levels of control mice. These effects of IAP probably occur as a result of changes in the composition of specific bacterial taxa at the genus and species levels (e.g. members of Anaeroplasma and Parabacteroides). CONCLUSIONS: Co-administration of IAP with AZT early in life prevents mice from susceptibility to the later development of MetS. This effect is associated with alterations in the composition of the gut microbiota. IAP may represent a novel treatment against MetS in humans.

Post-stroke fatigue: a problem of altered corticomotor control?

OBJECTIVES: We recently showed that diminished motor cortical excitability is associated with high levels of post-stroke fatigue. Motor cortex excitability impacts movement parameters such as reaction and movement times. We predicted that one or both would be influenced by the presence of post-stroke fatigue. METHODS: 41 first-time stroke survivors (high fatigue n=21, Fatigue Severity Scale 7 (FSS-7) score >5; low fatigue n=20, FSS-7 score <3) participated in the study. Movement times, choice and simple reaction times were measured in all participants. RESULTS: A three way ANOVA with fatigue (high and low), task (movement time, simple reaction time and choice reaction time) and hand (affected and unaffected) as the three factors, revealed a significant difference between affected (but not unaffected) hand movement times in the high compared to low fatigue groups. Reaction times, however, were not different between the high-fatigue and low-fatigue groups in either the affected or unaffected hand. CONCLUSIONS: Previously, we showed that motor cortex excitability is lower in patients with high post-stroke fatigue. Our current findings suggest that post-stroke fatigue (1) is a problem of movement speed (possibly a consequence of diminished motor cortex excitability) and not movement preparation, and (2) may have a focal origin confined to the lesioned hemisphere. We suggest that low motor cortex excitability in the lesioned hemisphere is a viable therapeutic target in post-stroke fatigue.

Green coffee polyphenols do not attenuate features of the metabolic syndrome and improve endothelial function in mice fed a high fat diet.

We have investigated the effects of the major polyphenol in coffee, chlorogenic acid (CGA), on obesity, glucose intolerance, insulin resistance, systemic oxidative stress and endothelial dysfunction in a mouse model of the metabolic syndrome. Thirty C57BL6 mice were randomly divided into (n=10/group) (i) normal diet (ND), (ii) high fat diet (HFD), or (iii) high fat diet supplemented with 0.5% w/w green coffee bean extract (GCE) rich in chlorogenic acid (HFD+GCE). The high fat diet consisted of 28% fat and all animals were maintained on their diets for 12 weeks. The mice fed a HFD and HFD+GCE displayed symptoms of the metabolic syndrome compared to their normal fed counterparts, although no endothelial dysfunction was detected in the abdominal aortas after 12 weeks. GCE did not attenuate HFD-induced obesity, glucose intolerance, insulin resistance or systemic oxidative stress. Furthermore, GCE did not protect against ex vivo oxidant (hypochlorous acid)-induced endothelial dysfunction.