Bar-Ilan University

About: Bar-Ilan University is a education organization based out in Ramat Gan, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 12835 authors who have published 34964 publications receiving 995648 citations. The organization is also known as: Bar Ilan University & BIU.

Cascade of failures in coupled network systems with multiple support-dependence relations.

Abstract: We study, both analytically and numerically, the cascade of failures in two coupled network systems A and B, where multiple support-dependence relations are randomly built between nodes of networks A and B. In our model we assume that each node in one network can function only if it has at least a single support link connecting it to a functional node in the other network. We assume that networks A and B have (i) sizes N{A} and N{B}, (ii) degree distributions of connectivity links P{A}(k) and P{B}(k), (iii) degree distributions of support links P{A}(k) and P{B}(k), and (iv) random attack removes (1-R{A})N{A} and (1-R{B})N{B} nodes form the networks A and B, respectively. We find the fractions of nodes μ{∞}{A} and μ{∞}{B} which remain functional (giant component) at the end of the cascade process in networks A and B in terms of the generating functions of the degree distributions of their connectivity and support links. In a special case of Erdős-Renyi networks with average degrees a and b in networks A and B, respectively, and Poisson distributions of support links with average degrees a and b in networks A and B, respectively, μ{∞}{A}=R{A}[1-exp(-aμ{∞}{B})][1-exp(-aμ{∞}{A})] and μ{∞}{B}=R{B}[1-exp(-bμ{∞}{A})][1-exp(-bμ{∞}{B})]. In the limit of a→∞ and b→∞, both networks become independent, and our model becomes equivalent to a random attack on a single Erdős-Renyi network. We also test our theory on two coupled scale-free networks, and find good agreement with the simulations.

Bias in dietary-report instruments and its implications for nutritional epidemiology.

Abstract: Objective To evaluate measurement error structure in dietary assessment instruments and to investigate its implications for nutritional studies, using urinary nitrogen excretion as a reference biomarker for protein intake. Design The dietary assessment methods included different food-frequency questionnaires (FFQs) and such conventional dietary-report reference instruments as a series of 24-hour recalls, 4-day weighed food records or 7-day diaries. Setting Six original pilot validation studies within the European Prospective Investigation of Cancer (EPIC), and two validation studies conducted by the British Medical Research Council (MRC) within the Norfolk cohort that later joined as a collaborative component cohort of EPIC. Subjects A sample of approximately 100 to 200 women and men, aged 35-74 years, from each of eight validation studies. Results In assessing protein intake, all conventional dietary-report reference methods violated the critical requirements for a valid reference instrument for evaluating, and adjusting for, dietary measurement error in an FFQ. They displayed systematic bias that depended partly on true intake and partly was person-specific, correlated with person-specific bias in the FFQ. Using the dietary-report methods as reference instruments produced substantial overestimation (up to 230%) of the FFQ correlation with true usual intake and serious underestimation (up to 240%) of the degree of attenuation of FFQ-based log relative risks. Conclusion The impact of measurement error in dietary assessment instruments on the design, analysis and interpretation of nutritional studies may be much greater than has been previously estimated, at least regarding protein intake.

Large-scale replicated field study of maize rhizosphere identifies heritable microbes

Abstract: Soil microbes that colonize plant roots and are responsive to differences in plant genotype remain to be ascertained for agronomically important crops. From a very large-scale longitudinal field study of 27 maize inbred lines planted in three fields, with partial replication 5 y later, we identify root-associated microbiota exhibiting reproducible associations with plant genotype. Analysis of 4,866 samples identified 143 operational taxonomic units (OTUs) whose variation in relative abundances across the samples was significantly regulated by plant genotype, and included five of seven core OTUs present in all samples. Plant genetic effects were significant amid the large effects of plant age on the rhizosphere microbiome, regardless of the specific community of each field, and despite microbiome responses to climate events. Seasonal patterns showed that the plant root microbiome is locally seeded, changes with plant growth, and responds to weather events. However, against this background of variation, specific taxa responded to differences in host genotype. If shown to have beneficial functions, microbes may be considered candidate traits for selective breeding.