A nutritional memory effect counteracts the benefits of dietary restriction in old mice

Dietary restriction (DR) during adulthood can greatly extend lifespan and improve metabolic health in diverse species. However, whether DR in mammals is still effective when applied for the first time at old age remains elusive. Here, we report results of a late-life DR-switch experiment using 800 mice. Female mice aged 24 months were switched from an ad libitum (AL) diet to DR or vice versa. Strikingly, the switch from DR to AL acutely increases mortality, whereas the switch from AL to DR causes only a weak and gradual increase in survival, suggesting the body has a memory of earlier nutrition. RNA sequencing in liver and brown and white adipose tissue (BAT and WAT, respectively) demonstrates a largely refractory transcriptional and metabolic response in fat tissue to DR after an AL diet, particularly in WAT, and a proinflammatory signature in aged preadipocytes, which is prevented by chronic DR feeding. Our results provide evidence for a ‘nutritional memory’ as a limiting factor for DR-induced longevity and metabolic remodelling of WAT in mammals

A three dimensional model of the photosynthetic membranes of Ectothiorhodospira halochloris

The three dimensional organization of the complete photosynthetic apparatus of the extremely halophilic, bacteriochlorophyll b containing Ectothiorhodospira halochloris has been elaborated by several techniques of electron microscopy. Essentially all thylakoidal sacs are disc shaped and connected to the cytoplasmic membrane by small membraneous ldquobridgesrdquo. In sum, the lumina of all thylakoids (intrathylakoidal space) form one common periplasmic space. Thin sections confirm a paracrystalline arrangement of the photosynthetic complexes in situ. The ontogenic development of the photosynthetic apparatus is discussed based on a structural model derived from serial thin sections

Engaging with community researchers for exposure science: lessons learned from a pesticide biomonitoring study

A major challenge in biomonitoring studies with members of the general public is ensuring their continued involvement throughout the necessary length of the research. The paper presents evidence on the use of community researchers, recruited from local study areas, as a mechanism for ensuring effective recruitment and retention of farmer and resident participants for a pesticides biomonitoring study. The evidence presented suggests that community researchers' abilities to build and sustain trusting relationships with participants enhanced the rigour of the study as a result of their on-the-ground responsiveness and flexibility resulting in data collection beyond targets expected

Multijet production in neutral current deep inelastic scattering at HERA and determination of α_{s}

Multijet production rates in neutral current deep inelastic scattering have been measured in the range of exchanged boson virtualities 10 5 GeV and –1 < η_{LAB}^{jet} < 2.5. Next-to-leading-order QCD calculations describe the data well. The value of the strong coupling constant α_{s} (M_{z}), determined from the ratio of the trijet to dijet cross sections, is α_{s} (M_{z}) = 0.1179 ± 0.0013 (stat.)_{-0.0046}^{+0.0028}(exp.)_{-0.0046}^{+0.0028}(th.)

Regression applied to protein binding site prediction and comparison with classification

<p>Abstract</p> <p>Background</p> <p>The structural genomics centers provide hundreds of protein structures of unknown function. Therefore, developing methods enabling the determination of a protein function automatically is imperative. The determination of a protein function can be achieved by studying the network of its physical interactions. In this context, identifying a potential binding site between proteins is of primary interest. In the literature, methods for predicting a potential binding site location generally are based on classification tools. The aim of this paper is to show that regression tools are more efficient than classification tools for patches based binding site predictors. For this purpose, we developed a patches based binding site localization method usable with either regression or classification tools.</p> <p>Results</p> <p>We compared predictive performances of regression tools with performances of machine learning classifiers. Using leave-one-out cross-validation, we showed that regression tools provide better predictions than classification ones. Among regression tools, Multilayer Perceptron ranked highest in the quality of predictions. We compared also the predictive performance of our patches based method using Multilayer Perceptron with the performance of three other methods usable through a web server. Our method performed similarly to the other methods.</p> <p>Conclusion</p> <p>Regression is more efficient than classification when applied to our binding site localization method. When it is possible, using regression instead of classification for other existing binding site predictors will probably improve results. Furthermore, the method presented in this work is flexible because the size of the predicted binding site is adjustable. This adaptability is useful when either false positive or negative rates have to be limited.</p