25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.) (Book)

Linear models

Lipopeptides constitute a structurally diverse group of metabolites produced by various bacterial and fungal genera. In the past decades, research on lipopeptides has been fueled by their antimicrobial, antitumour, immunosuppressant and surfactant activities. However, the natural functions of lipopeptides in the lifestyles of the producing microorganisms have received considerably less attention. The substantial structural diversity of lipopeptides suggests that these metabolites have different natural roles, some of which may be unique to the biology of the producing organism. This review gives a detailed overview of the versatile functions of lipopeptides in the biology of Pseudomonas and Bacillus species, and highlights their role in competitive interactions with coexisting organisms, including bacteria, fungi, oomycetes, protozoa, nematodes and plants. Their functions in cell motility, leading to colonization of novel habitats, and in the formation and development of highly structured biofilms are discussed in detail. Finally, this review provides an update on lipopeptide detection and discovery as well as on novel regulatory mechanisms and genes involved in lipopeptide biosynthesis in these two bacterial genera.

Natural functions of lipopeptides from Bacillus and Pseudomonas: more than surfactants and antibiotics

GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves

Indicators of good soil physical quality: density and storage parameters

EXPERIMENTAL DESIGN AND DATA COLLECTION, M. Rasch Experimental Design Data Collection Conclusion References PRIMARY MODELS, R.C. McKellar and X. Lu Growth Models Survival Models References SECONDARY MODELS, T. Ross and P. Dalgaard Introduction Secondary Models for Growth Rate and Lag Time Secondary Models for Inactivation Probability Models Characterization of Environmental Parameters Affecting Microbial Kinetics in Food References MODEL FITTING AND UNCERTAINTY, D.A. Ratkowsky Overview Model Fitting Uncertainty in Lag Times, Generation Times, and Its Consequences Epilogue Appendix References CHALLENGE OF FOOD AND THE ENVIRONMENT, T. Brocklehurst Role of Food Heterogeneity Modeling the Food Environment Hurdle Concept Competition with Other Microorganisms Adaptation and Injury Validation in Foods References SOFTWARE PROGRAMS TO INCREASE THE UTILITY OF PREDICTIVE MICROBIOLOGY INFORMATION, M. Tamplin, J. Baranyi, and G. Paoli Introduction Model Interfaces Databases Expert Systems Conclusions References MODELING MICROBIAL DYNAMICS UNDER TIME-VARYING CONDITIONS, K. Bernaerts, E. Dens, K. Vereecken, A. Geeraerd, F. Devlieghere, J. Debevere, and J.F. Van Impe Introduction General Dynamic Modeling Methodology Example I: Individual-Based Modeling of Microbial Lag Example II. Modeling Microbial Interaction with Product Inhibition Conclusions Acknowledgments References PREDICTIVE MICROBIOLOGY IN QUANTITATIVE RISK ASSESSMENT, A.M. Lammerding and R.C. McKellar Introduction Assessing Microbial Risks Role of Predictive Microbiology in QRA Scope of Risk Assessments Process Risk Modeling Examples of Risk Modeling Modifying Risk: Concentration vs. Prevalence What is the Right Model to Use? Future Directions Conclusions References MODELING THE HISTORY EFFECT ON MICROBIAL GROWTH AND SURVIVAL: DETERMINISTIC AND STOCHASTIC APPROACHES, J. Baranyi and C. Pin Introduction Modeling the History Effect at Population Level (Deterministic Modeling) Modeling the History Effect at Single-Cell Level (Stochastic Modeling) Concluding Remarks Acknowledgment References MODELS - WHAT COMES AFTER THE NEXT GENERATION? D.W. Schaffner Introduction Cross-Contamination Inoculum Size Modeling Cross-Contamination and Inoculum Size Summary References PREDICTIVE MYCOLOGY, P. Dantigny Introduction Concerns Mold Specificities Models Perspectives References AN ESSAY ON THE UNREALIZED POTENTIAL OF PREDICTIVE MICROBIOLOGY, T. McMeekin Introduction A Short History and the Philosophy of Predictive Microbiology The Basics of Predictive Modeling Addressing Concerns in Predictive Modeling Modeling Fungal Growth Application of Predictive Microbiology Concluding Remarks Acknowledgments References Index

Modeling Microbial Responses in Food

Anthocyanins in ripe fruit of four Vaccinium species and genotypes within these species were compared, revealing substantial inter- and intra-species variability among these commercial and non-comm...

Anthocyanin content and profile within and among blueberry species

In a small target region, it may be possible to exploit local adaptation to increase gains from selection. However, in a large region more extensive testing is usually possible, resulting in more precise estimation of genotype means. A correlated response model was adapted to determine if division of a large target region is likely to increase gains. Genotypic value in a large region and constituent subregions are considered correlated traits. Correlated response in a subregion to indirect selection across the undivided region, relative to direct response to selection within the subregion, is expressed as a function of heritability in the undivided region (H) and in the subregion (H(i)), and of the genotypic correlation between region and subregion means (r(G'). r(G') depends on the magnitude of the genotype x subregion interaction (sigma2GS)) relative to the genotypic variance (sigma2(G)). sigma2(GS) is the portion of the genotype x location interaction (sigma2(GL)) caused by local adaptation, rather than by random site-to-site variability in genotype means. Subdivision can increase heritability through the addition of sigma2(GS) to the numerator of H(i), but this may be offset by reduced replication across locations within the subregion. Modeling using variance estimates from several cereal programs indicated that, unless sigma2(GL) is large relative to sigma2(G) and at least 30% of sigma2(GL) is due to sigma2(GS), subdivision is unlikely to increase response. These results help explain the success of breeding programs that test broadly.

Selection response in subdivided target regions

Whole-brain dynamic time-resolved computed tomography angiography (CTA) is a technique developed on the new 320-detector row CT scanner capable of generating time-resolved cerebral angiograms from skull base to vertex. Unlike a conventional cerebral angiogram, this technique visualizes pial arterial filling in all vascular territories, thereby providing additional hemodynamic information. Ours was a retrospective study of consecutive patients with ischemic stroke and M1 middle cerebral artery +/−intracranial internal carotid artery occlusions presenting to our center from June 2010 and undergoing dynamic time-resolved CTA and perfusion CT within 6 hours of symptom onset. Leptomeningeal collateral status was assessed by determining relative prominence of pial arteries in the ischemic region, rate and extent of retrograde flow, and various topographical patterns of pial arterial filling. Twenty-five patients were included in the study. We demonstrate the existence of the following novel properties of leptomeningeal collaterals in humans: (a) posterior (posterior cerebral artery (PCA)–MCA) dominant collateralization, (b) intra-territorial ‘within MCA region' leptomeningeal collaterals, and (c) significant variability in size, extent, and retrograde filling time in pial arteries. We also describe a simple and reliable collateral grading template that, for the first time on dynamic CTA, incorporates back-filling time as well as size and extent of collateral filling.

https://journals.sagepub.com/doi/pdf/10.1038/jcbfm.2012.171

Xuewen Lu

Papers

Indicators of good soil physical quality: density and storage parameters

Modeling Microbial Responses in Food

Anthocyanin content and profile within and among blueberry species

Selection response in subdivided target regions

Assessment of leptomeningeal collaterals using dynamic CT angiography in patients with acute ischemic stroke