Contribution of grape berry lipids to wine aroma

Understanding how the composition of wine grapes influences the formation of wine volatiles is important for predicting and manipulating wine quality. The objective of this research was to develop an understanding of how non-varietal wine aroma compounds are affected by compounds sourced from grapes. Chemically defined grape juice musts were supplemented with material of interest and the volatile profile of the fermented wines analysed with head-space solid phase micro-extraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS). Supplementing model must with different grape tissues was carried out to determine where volatile-influencing compounds are located in the berries, and variations in amino acid and lipid profiles identified as probable influences on wine aromas made by yeast. In order to isolate and identify grape compounds that increase fatty acid ethyl ester (FAEE) concentrations in wine, fractions of grape extract were separated by liquid chromatography and used to supplement model musts, resulting in the identification of the poly-unsaturated triglyceride TG 54:6 as a major component of fractions that induced high FAEE production during fermentation. The effect of supplementing model must with glycero-lipids on FAEE production was investigated and a positive impact of exogenous poly-unsaturated glycero-lipids on yeast-mediated FAEE production confirmed. A lipidomic profiling study of grape tissues, and of berry and seed development, was carried out by positive-mode liquid chromatography quadrupole time-of-flight MS (LC-QTOFMS). This study indicated: that there are few differences in lipid profile between mature Riesling and Cabernet Sauvignon grapes; that seeds and berry tissues have distinctive lipid profiles; and that lipid profiles change during berry development in both seeds and berry tissues. The results of these studies highlight the need for research into establishing optimal grape lipid profiles to produce wines with targeted wine aroma profiles

Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18‐19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/1/lrh2-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/2/lrh210222.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/3/lrh210222_am.pd

Metabolic syndrome in rural Australia:An opportunity for primary health care

Objective: To measure the impact of a 6-month home-based behaviour change intervention on reducing the risk of chronic disease as determined by metabolic syndrome status and cardiovascular risk score, and discuss implications for primary care in rural areas. Design: A two-arm randomised controlled trial of rural adults. Setting: The rural town of Albany in the Great Southern region of Western Australia. Participants: Participants (n = 401) aged 50-69 years who were classified with or at risk of metabolic syndrome and randomly assigned to intervention (n = 201) or waitlisted control (n = 200) group. Interventions: A 6-month intervention program incorporating goal setting, self-monitoring and feedback, with motivational interviewing was conducted. Main outcome measures: Change in metabolic syndrome status and cardiovascular risk. Results: Significant improvements in metabolic syndrome status and cardiovascular disease risk score (−0.82) were observed for the intervention group relative to control group from baseline to post-test. Conclusion: This home-based physical activity and nutrition intervention reduced participants' risk of experiencing a cardiovascular event in the next 5 years by 1%. Incorporating such prevention orientated approaches in primary care might assist in reducing the burden of long-term chronic diseases. However, for realistic application in this setting, hurdles such as current national health billing system and availability of resources will need to be considered

Association of acute myeloid leukemias most immature phenotype with risk groups and outcomes

The precise phenotype and biology of acute myeloid leukemia stem cells remain controversial, in part because the “gold standard” immunodeficient mouse engraftment assay fails in a significant fraction of patients and identifies multiple cell-types in others. We sought to analyze the clinical utility of a novel assay for putative leukemia stem cells in a large prospective cohort. The leukemic clone’s most primitive hematopoietic cellular phenotype was prospectively identified in 109 newly-diagnosed acute myeloid leukemia patients, and analyzed against clinical risk groups and outcomes. Most (80/109) patients harbored CD34+CD38− leukemia cells. The CD34+CD38− leukemia cells in 47 of the 80 patients displayed intermediate aldehyde dehydrogenase expression, while normal CD34+CD38− hematopoietic stem cells expressed high levels of aldehyde dehydrogenase. In the other 33/80 patients, the CD34+CD38− leukemia cells exhibited high aldehyde dehydrogenase activity, and most (28/33, 85%) harbored poor-risk cytogenetics or FMS-like tyrosine kinase 3 internal tandem translocations. No CD34+ leukemia cells could be detected in 28/109 patients, including 14/21 patients with nucleophosmin-1 mutations and 6/7 acute promyelocytic leukemia patients. The patients with CD34+CD38− leukemia cells with high aldehyde dehydrogenase activity manifested a significantly lower complete remission rate, as well as poorer event-free and overall survivals. The leukemic clone’s most immature phenotype was heterogeneous with respect to CD34, CD38, and ALDH expression, but correlated with acute myeloid leukemia risk groups and outcomes. The strong clinical correlations suggest that the most immature phenotype detectable in the leukemia might serve as a biomarker for “clinically-relevant” leukemia stem cells. ClinicalTrials.gov: {"type":"clinical-trial","attrs":{"text":"NCT01349972","term_id":"NCT01349972"}}NCT01349972

CCDC61/VFL3 Is a Paralog of SAS6 and Promotes Ciliary Functions.

Centrioles are cylindrical assemblies whose peripheral microtubule array displays a 9-fold rotational symmetry that is established by the scaffolding protein SAS6. Centriole symmetry can be broken by centriole-associated structures, such as the striated fibers in Chlamydomonas that are important for ciliary function. The conserved protein CCDC61/VFL3 is involved in this process, but its exact role is unclear. Here, we show that CCDC61 is a paralog of SAS6. Crystal structures of CCDC61 demonstrate that it contains two homodimerization interfaces that are similar to those found in SAS6, but result in the formation of linear filaments rather than rings. Furthermore, we show that CCDC61 binds microtubules and that residues involved in CCDC61 microtubule binding are important for ciliary function in Chlamydomonas. Together, our findings suggest that CCDC61 and SAS6 functionally diverged from a common ancestor while retaining the ability to scaffold the assembly of basal body-associated structures or centrioles, respectively

Coupled Networks of Permanent Protected Areas and Dynamic Conservation Areas for Biodiversity Conservation Under Climate Change

The complexity of climate change impacts on ecological processes necessitates flexible and adaptive conservation strategies that cross traditional disciplines. Current strategies involving protected areas are predominantly fixed in space, and may on their own be inadequate under climate change. Here, we propose a novel approach to climate adaptation that combines permanent protected areas with temporary conservation areas to create flexible networks. Previous work has tended to consider permanent and dynamic protection as separate actions, but their integration could draw on the strengths of both approaches to improve biodiversity conservation and help manage for ecological uncertainty in the coming decades. As there are often time lags in the establishment of new permanent protected areas, the inclusion of dynamic conservation areas within permanent networks could provide critical transient protection to mitigate land-use changes and biodiversity redistributions. This integrated approach may be particularly useful in highly human-modified and fragmented landscapes where areas of conservation value are limited and long-term place-based protection is unfeasible. To determine when such an approach may be feasible, we propose the use of a decision framework. Under certain scenarios, these coupled networks have the potential to increase spatio-temporal network connectivity and help maintain biodiversity and ecological processes under climate change. Implementing these networks would require multidisciplinary scientific evidence, new policies, creative funding solutions, and broader acceptance of a dynamic approach to biodiversity conservation

Coupled networks of permanent protected areas and dynamic conservation areas for biodiversity conservation under climate change

The complexity of climate change impacts on ecological processes necessitates flexible and adaptive conservation strategies that cross traditional disciplines. Current strategies involving protected areas are predominantly fixed in space, and may on their own be inadequate under climate change. Here, we propose a novel approach to climate adaptation that combines permanent protected areas with temporary conservation areas to create flexible networks. Previous work has tended to consider permanent and dynamic protection as separate actions, but their integration could draw on the strengths of both approaches to improve biodiversity conservation and help manage for ecological uncertainty in the coming decades. As there are often time lags in the establishment of new permanent protected areas, the inclusion of dynamic conservation areas within permanent networks could provide critical transient protection to mitigate land-use changes and biodiversity redistributions. This integrated approach may be particularly useful in highly human-modified and fragmented landscapes where areas of conservation value are limited and long-term place-based protection is unfeasible. To determine when such an approach may be feasible, we propose the use of a decision framework. Under certain scenarios, these coupled networks have the potential to increase spatio-temporal network connectivity and help maintain biodiversity and ecological processes under climate change. Implementing these networks would require multidisciplinary scientific evidence, new policies, creative funding solutions, and broader acceptance of a dynamic approach to biodiversity conservation

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p