Buckwheat (Fagopyrum esculentum) Hulls Are a Rich Source of Fermentable Dietary Fibre and Bioactive Phytochemicals.

Pseudo-cereals such as buckwheat (Fagopyrum esculentum) are valid candidates to promote diet biodiversity and nutrition security in an era of global climate change. Buckwheat hulls (BHs) are currently an unexplored source of dietary fibre and bioactive phytochemicals. This study assessed the effects of several bioprocessing treatments (using enzymes, yeast, and combinations of both) on BHs' nutrient and phytochemical content, their digestion and metabolism in vitro (using a gastrointestinal digestion model and mixed microbiota from human faeces). The metabolites were measured using targeted LC-MS/MS and GC analysis and 16S rRNA gene sequencing was used to detect the impact on microbiota composition. BHs are rich in insoluble fibre (31.09 ± 0.22% as non-starch polysaccharides), protocatechuic acid (390.71 ± 31.72 mg/kg), and syringaresinol (125.60 ± 6.76 mg/kg). The bioprocessing treatments significantly increased the extractability of gallic acid, vanillic acid, p-hydroxybenzoic acid, syringic acid, vanillin, syringaldehyde, p-coumaric acid, ferulic acid, caffeic acid, and syringaresinol in the alkaline-labile bound form, suggesting the bioaccessibility of these phytochemicals to the colon. Furthermore, one of the treatments, EC_2 treatment, increased significantly the in vitro upper gastrointestinal release of bioactive phytochemicals, especially for protocatechuic acid (p < 0.01). The BH fibre was fermentable, promoting the formation mainly of propionate and, to a lesser extent, butyrate formation. The EM_1 and EC_2 treatments effectively increased the content of insoluble fibre but had no effect on dietary fibre fermentation (p > 0.05). These findings promote the use of buckwheat hulls as a source of dietary fibre and phytochemicals to help meet dietary recommendations and needs.

Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes.

Demographic events shape a population's genetic diversity, a process described by the coalescent-with-recombination model that relates demography and genetics by an unobserved sequence of genealogies along the genome. As the space of genealogies over genomes is large and complex, inference under this model is challenging. Formulating the coalescent-with-recombination model as a continuous-time and -space Markov jump process, we develop a particle filter for such processes, and use waypoints that under appropriate conditions allow the problem to be reduced to the discrete-time case. To improve inference, we generalise the Auxiliary Particle Filter for discrete-time models, and use Variational Bayes to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization. Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models. Code is available at https://github.com/luntergroup/smcsmc.

Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization.

Expectation maximization (EM) is a technique for estimating maximum-likelihood parameters of a latent variable model given observed data by alternating between taking expectations of sufficient statistics, and maximizing the expected log likelihood. For situations where sufficient statistics are intractable, stochastic approximation EM (SAEM) is often used, which uses Monte Carlo techniques to approximate the expected log likelihood. Two common implementations of SAEM, Batch EM (BEM) and online EM (OEM), are parameterized by a "learning rate", and their efficiency depend strongly on this parameter. We propose an extension to the OEM algorithm, termed Introspective Online Expectation Maximization (IOEM), which removes the need for specifying this parameter by adapting the learning rate to trends in the parameter updates. We show that our algorithm matches the efficiency of the optimal BEM and OEM algorithms in multiple models, and that the efficiency of IOEM can exceed that of BEM/OEM methods with optimal learning rates when the model has many parameters. Finally we use IOEM to fit two models to a financial time series. A Python implementation is available at https://github.com/luntergroup/IOEM.git.

Estimation of the serial interval of pertussis in Dutch households.

Increasing incidence has led to the re-appearance of pertussis as a public health problem in developed countries. Pertussis infection is usually mild in vaccinated children and adults, but it can be fatal in infants who are too young for effective vaccination (≤3 months). Tailoring of control strategies to prevent infection of the infant hinges on the availability of estimates of key epidemiological quantities. Here we estimate the serial interval of pertussis, i.e., the time between symptoms onset in a case and its infector, using data from a household-based study carried out in the Netherlands in 2007-2009. We use statistical methodology to tie infected persons to probable infector persons, and obtain statistically supported stratifications of the data by person-type (infant, mother, father, sibling). The analyses show that the mean serial interval is 20 days (95% CI: 16-23 days) when the mother is the infector of the infant, and 28 days (95% CI: 23-33 days) when the infector is the father or a sibling. These time frames offer opportunities for early mitigation of the consequences of infection of an infant once a case has been detected in a household. If preventive measures such as social distancing or antimicrobial treatment are taken promptly they could decrease the probability of infection of the infant.

A collaborative, systems-level approach to eliminating healthcare-associated MRSA, central-line-associated bloodstream infections, ventilator-associated pneumonia, and respiratory virus infections.

To achieve sustainable reductions in healthcare-associated infections (HAIs), the University of Washington Medical Center (UWMC) deployed a collaborative, systems-level initiative. With the sponsorship of senior leadership, multidisciplinary teams were established to address healthcare-associated methicillin-resistant Staphylococcus aureus (MRSA), central-line-associated bloodstream infections (CLABSI), ventilator-associated pneumonia (VAP), and respiratory virus infections. The goal of the initiative was to eliminate these four HAIs among medical center inpatients by 2012. In the first 24 months of the project, the number of healthcare-associated MRSA cases decreased 58%; CLABSI cases decreased 54%. Staff and provider compliance with infection prevention measures improved and remained strong, for example, 96% compliance with hand hygiene, 98% compliance with the recommended influenza vaccination program, and 100% compliance with the VAP bundle. Achieving these results required an array of coordinated, systems-level interventions. Critical project success factors were believed to include creating organizational alignment by declaring eliminating HAIs as an organizational breakthrough goal, having the organization's executive leadership highly engaged in the project, coordination by an experienced and effective project leader and manager, collaboration by multidisciplinary project teams, and promoting transparency of results across the organization.

Forecasting Late Blight in Potato Crops of Southern Idaho Using Logistic Regression Analysis.

Previously published late blight forecasts which predict the threat of disease based on the presence or absence of favorable weather have not been effective in semi-arid potato-producing areas of the Pacific Northwest (Idaho, Oregon, and Washington). Research was conducted to identify weather variables useful for forecasting late blight in southern Idaho. The objectives of this research were to (i) determine if regional weather variables could be related to the occurrence of late blight in southern Idaho, (ii) determine if disease severity (scale of 0 to 4) could be predicted using variables found to be correlated with the annual occurrence of late blight, and (iii) validate the efficacy of this model in predicting disease incidence in regions of the Columbia Basin. Weather data were collected from five locations over a 9-year period (1995 to 2003). A binary logistic regression model (0 = no disease and 1 = disease) indicated that the number of hours with favorable conditions (10°C ≤ temperature ≤ 27°C, relative humidity ≥ 80%) in April and May (HF80m) was a significant disease predictor. Logistic regression analysis using an ordinal disease scale (0 = no disease and 4 = severe disease) indicated amount of precipitation (APj) and favorable hours (HF80j) with extended periods from April to June as significant disease predictors. The binary model predicted disease occurrence more accurately, with 67.5% accuracy (27/40 years correctly predicted), 75% sensitivity (12/16 late-blight years predicted), and 62.5% specificity (15/24 non-late-blight years predicted) using a leave-1-year-out error estimate. The binary model was validated with data (1995 to 2003) from the semi-arid Columbia Basin regions, predicting disease with 80.8% accuracy (21/26 years predicted), 84% sensitivity (21/25 outbreak years predicted), and 0% specificity (0/1 non-outbreak years predicted).

A novel class of CoA-transferase involved in short-chain fatty acid metabolism in butyrate-producing human colonic bacteria.

Bacterial butyryl-CoA CoA-transferase activity plays a key role in butyrate formation in the human colon, but the enzyme and corresponding gene responsible for this activity have not previously been identified. A novel CoA-transferase gene is described from the colonic bacterium Roseburia sp. A2-183, with similarity to acetyl-CoA hydrolase as well as 4-hydroxybutyrate CoA-transferase sequences. The gene product, overexpressed in an Escherichia coli lysate, showed activity with butyryl-CoA and to a lesser degree propionyl-CoA in the presence of acetate. Butyrate, propionate, isobutyrate and valerate competed with acetate as the co-substrate. Despite the sequence similarity to 4-hydroxybutyrate CoA-transferases, 4-hydroxybutyrate did not compete with acetate as the co-substrate. Thus the CoA-transferase preferentially uses butyryl-CoA as substrate. Similar genes were identified in other butyrate-producing human gut bacteria from clostridial clusters IV and XIVa, while other candidate CoA-transferases for butyrate formation could not be detected in Roseburia sp. A2-183. This suggests strongly that the newly identified group of CoA-transferases described here plays a key role in butyrate formation in the human colon.

Children and adolescents with developmental disabilities. The GP's role.

BACKGROUND: General practitioners play a central role in the health care for families of children with disabilities, but may feel inadequately trained or resourced for this role. OBJECTIVE: This article highlights the important issues arising for children and their families as the child grows--from birth through to adolescence and adulthood. It also provides some key resources for the GP, patient and their family during this transition. DISCUSSION: Families need GPs to provide primary and preventive health care, and referral to and coordination of other health care services. The families of children with disabilities are no different. They need their GP to provide care to all family members and to work as a part of a health care team for children with complex needs relating to developmental disabilities.