Developing country consumers’ acceptance of biofortified foods: a synthesis

The success of biofortified staple crops depends on whether they are accepted and consumed by target populations. In the past 8 years, several studies were undertaken to understand consumers’ acceptance of foods made with biofortified staple crops. Consumer acceptance is measured in terms of their sensory evaluation and economic valuation of biofortified varieties vis-à-vis conventional ones. These studies apply expert sensory panel and hedonic trait analyses methods adopted from food sciences literature, as well as various preference elicitation methods (including experimental auctions, revealed choice experiments, and stated choice experiments) adopted from experimental economics literature. These studies also test the impact of various levers on consumers’ evaluation and valuation for biofortified foods. These levers include (i) nutrition information and the media through which such information is conveyed; (ii) the length and content of nutrition information; (iii) different branding options; (iv) the nature (national or international) of the branding/certification agency that is endorsing the biofortified staple food; and (v) the nature (national or international) of the agency that is delivering the biofortified staple food. This paper brings together evidence on consumer acceptance of biofortified crops on 5 crops across 7 countries in Africa, Asia and Latin America. The results of these studies are expected to aid in the development of biofortified crops that consumers like, as well as in the development of appropriate marketing and consumer awareness or information campaigns to encourage the switch in consumption from traditional staples to biofortified ones

Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

<p>Abstract</p> <p>Background</p> <p>Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.</p> <p>Results</p> <p>In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent <it>hypotheses </it>that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.</p> <p>Conclusion</p> <p>Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.</p

Translating upwards: linking the neural and social sciences via neuroeconomics

The social and neural sciences share a common interest in understanding the mechanisms that underlie human behaviour. However, interactions between neuroscience and social science disciplines remain strikingly narrow and tenuous. We illustrate the scope and challenges for such interactions using the paradigmatic example of neuroeconomics. Using quantitative analyses of both its scientific literature and the social networks in its intellectual community, we show that neuroeconomics now reflects a true disciplinary integration, such that research topics and scientific communities with interdisciplinary span exert greater influence on the field. However, our analyses also reveal key structural and intellectual challenges in balancing the goals of neuroscience with those of the social sciences. To address these challenges, we offer a set of prescriptive recommendations for directing future research in neuroeconomics

Inferring cellular networks – a review

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations

How long do nosocomial pathogens persist on inanimate surfaces? A systematic review

BACKGROUND: Inanimate surfaces have often been described as the source for outbreaks of nosocomial infections. The aim of this review is to summarize data on the persistence of different nosocomial pathogens on inanimate surfaces. METHODS: The literature was systematically reviewed in MedLine without language restrictions. In addition, cited articles in a report were assessed and standard textbooks on the topic were reviewed. All reports with experimental evidence on the duration of persistence of a nosocomial pathogen on any type of surface were included. RESULTS: Most gram-positive bacteria, such as Enterococcus spp. (including VRE), Staphylococcus aureus (including MRSA), or Streptococcus pyogenes, survive for months on dry surfaces. Many gram-negative species, such as Acinetobacter spp., Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa, Serratia marcescens, or Shigella spp., can also survive for months. A few others, such as Bordetella pertussis, Haemophilus influenzae, Proteus vulgaris, or Vibrio cholerae, however, persist only for days. Mycobacteria, including Mycobacterium tuberculosis, and spore-forming bacteria, including Clostridium difficile, can also survive for months on surfaces. Candida albicans as the most important nosocomial fungal pathogen can survive up to 4 months on surfaces. Persistence of other yeasts, such as Torulopsis glabrata, was described to be similar (5 months) or shorter (Candida parapsilosis, 14 days). Most viruses from the respiratory tract, such as corona, coxsackie, influenza, SARS or rhino virus, can persist on surfaces for a few days. Viruses from the gastrointestinal tract, such as astrovirus, HAV, polio- or rota virus, persist for approximately 2 months. Blood-borne viruses, such as HBV or HIV, can persist for more than one week. Herpes viruses, such as CMV or HSV type 1 and 2, have been shown to persist from only a few hours up to 7 days. CONCLUSION: The most common nosocomial pathogens may well survive or persist on surfaces for months and can thereby be a continuous source of transmission if no regular preventive surface disinfection is performed

Combined Tevatron upper limit on gg->H->W+W- and constraints on the Higgs boson mass in fourth-generation fermion models

Report number: FERMILAB-PUB-10-125-EWe combine results from searches by the CDF and D0 collaborations for a standard model Higgs boson (H) in the process gg->H->W+W- in p=pbar collisions at the Fermilab Tevatron Collider at sqrt{s}=1.96 TeV. With 4.8 fb-1 of integrated luminosity analyzed at CDF and 5.4 fb-1 at D0, the 95% Confidence Level upper limit on \sigma(gg->H) x B(H->W+W-) is 1.75 pb at m_H=120 GeV, 0.38 pb at m_H=165 GeV, and 0.83 pb at m_H=200 GeV. Assuming the presence of a fourth sequential generation of fermions with large masses, we exclude at the 95% Confidence Level a standard-model-like Higgs boson with a mass between 131 and 204 GeV.We combine results from searches by the CDF and D0 collaborations for a standard model Higgs boson (H) in the process gg→H→W+W- in pp̅ collisions at the Fermilab Tevatron Collider at √s=1.96  TeV. With 4.8  fb-1 of integrated luminosity analyzed at CDF and 5.4  fb-1 at D0, the 95% confidence level upper limit on σ(gg→H)×B(H→W+W-) is 1.75 pb at mH=120  GeV, 0.38 pb at mH=165  GeV, and 0.83 pb at mH=200  GeV. Assuming the presence of a fourth sequential generation of fermions with large masses, we exclude at the 95% confidence level a standard-model-like Higgs boson with a mass between 131 and 204 GeV.Peer reviewe

ICAR: endoscopic skull‐base surgery

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