Military special operators, elite athletes, and others requiring uninterrupted optimal performance currently lack options for sleep and mood support without performance-inhibiting effects. ...Kavalactones, derived from the root of the kava plant (Piper methysticum Forst), have been shown to elevate mood and wellbeing by producing a feeling of relaxation without addiction or cognitive impairment.BACKGROUNDMilitary special operators, elite athletes, and others requiring uninterrupted optimal performance currently lack options for sleep and mood support without performance-inhibiting effects. Kavalactones, derived from the root of the kava plant (Piper methysticum Forst), have been shown to elevate mood and wellbeing by producing a feeling of relaxation without addiction or cognitive impairment.In this placebo-controlled, crossover study (NCT05381025), we investigated the effects of 2 weeks of kavalactones use on cortisol (diurnal salivary), sleep (RSQ-W; Restorative Sleep Questionnaire, Weekly), mood (DASS-21; Depression Anxiety Stress Scale-21), and motivation state to expend (Move) or conserve (Rest) energy (CRAVE; Cravings for Rest and Volitional Energy Expenditure, Right Now) in a cohort of 15 healthy, physically fit young males engaged in a rigorous, two-a-day preparation class for special operations forces qualification.METHODSIn this placebo-controlled, crossover study (NCT05381025), we investigated the effects of 2 weeks of kavalactones use on cortisol (diurnal salivary), sleep (RSQ-W; Restorative Sleep Questionnaire, Weekly), mood (DASS-21; Depression Anxiety Stress Scale-21), and motivation state to expend (Move) or conserve (Rest) energy (CRAVE; Cravings for Rest and Volitional Energy Expenditure, Right Now) in a cohort of 15 healthy, physically fit young males engaged in a rigorous, two-a-day preparation class for special operations forces qualification.Cortisol, sleep, and mood were within normal, healthy parameters in this cohort at baseline. This remained unchanged with kavalactones use with no significant findings of clinical interest. However, a statistically similar, positive slope for within-group Move scores was seen in both groups during kavalactones loading (first group Move slope 2.25, second group Move slope 3.29, p = 0.299). This trend was seen regardless of order and with no apparent effects on the Rest metric (all p ≥ 0.05). Moreover, a significant between-group difference appeared after 1 week of kavalactones use in the first phase (p = 0.044) and persisted through the end of the first loading period (p = 0.022). Following the 10-day washout, this between-groups divergence remained significant (p = 0.038) but was reversed by 1 week after the crossover (p = 0.072), with Move scores once again statistically similar between groups and compared to baseline at study end. Furthermore, the group taking kavalactones first never experienced a significant decrease in Move motivation state (lowest mean score 21.0, highest 28.6, all p ≥ 0.05), while the group receiving kavalactones in the last 2 weeks of the study had Move scores that were statistically lower than baseline (lowest mean score 8.6, highest 25.9, all p ≤ 0.05) at all time points but the last (p = 0.063) after 2 weeks of kavalactones exposure.RESULTSCortisol, sleep, and mood were within normal, healthy parameters in this cohort at baseline. This remained unchanged with kavalactones use with no significant findings of clinical interest. However, a statistically similar, positive slope for within-group Move scores was seen in both groups during kavalactones loading (first group Move slope 2.25, second group Move slope 3.29, p = 0.299). This trend was seen regardless of order and with no apparent effects on the Rest metric (all p ≥ 0.05). Moreover, a significant between-group difference appeared after 1 week of kavalactones use in the first phase (p = 0.044) and persisted through the end of the first loading period (p = 0.022). Following the 10-day washout, this between-groups divergence remained significant (p = 0.038) but was reversed by 1 week after the crossover (p = 0.072), with Move scores once again statistically similar between groups and compared to baseline at study end. Furthermore, the group taking kavalactones first never experienced a significant decrease in Move motivation state (lowest mean score 21.0, highest 28.6, all p ≥ 0.05), while the group receiving kavalactones in the last 2 weeks of the study had Move scores that were statistically lower than baseline (lowest mean score 8.6, highest 25.9, all p ≤ 0.05) at all time points but the last (p = 0.063) after 2 weeks of kavalactones exposure.We report a novel finding that kavalactones may support performance by maintaining or rescuing the desire to expend energy in the context of significant physical and mental strain in well-conditioned individuals, even in a context of already normal cortisol, sleep, and mood.CONCLUSIONSWe report a novel finding that kavalactones may support performance by maintaining or rescuing the desire to expend energy in the context of significant physical and mental strain in well-conditioned individuals, even in a context of already normal cortisol, sleep, and mood.
Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing ...genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.
In 2017, influenza seasonal activity was high in the southern hemisphere. We present interim influenza vaccine effectiveness (VE) estimates from Australia. Adjusted VE was low overall at 33% (95% ...confidence interval (CI): 17 to 46), 50% (95% CI: 8 to 74) for A(H1)pdm09, 10% (95% CI: -16 to 31) for A(H3) and 57% (95% CI: 41 to 69) for influenza B. For A(H3), VE was poorer for those vaccinated in the current and prior seasons.
A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through ...the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.
Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not ...straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-the-art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply models perform best at different spatial scales – the first step towards bridging the stakeholder–academia gap in modelling ecosystem service delivery under ecological intensification.
Most serpins are associated with protease inhibition, and their ability to form loop–sheet polymers is linked to conformational disease and the human serpinopathies. Here we describe the structural ...and functional dissection of how a unique serpin, the non‐histone architectural protein, MENT (Myeloid and Erythroid Nuclear Termination stage‐specific protein), participates in DNA and chromatin condensation. Our data suggest that MENT contains at least two distinct DNA‐binding sites, consistent with its simultaneous binding to the two closely juxtaposed linker DNA segments on a nucleosome. Remarkably, our studies suggest that the reactive centre loop, a region of the MENT molecule essential for chromatin bridging in vivo and in vitro, is able to mediate formation of a loop–sheet oligomer. These data provide mechanistic insight into chromatin compaction by a non‐histone architectural protein and suggest how the structural plasticity of serpins has adapted to mediate physiological, rather than pathogenic, loop–sheet linkages.
A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the ...integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with
Gas7
,
Me1
and
Gpx3
being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes and identification of involved pathways and networks.
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