LEADER 02190nam 22004453u 450 001 9910462716403321 005 20210113114615.0 035 $a(CKB)2670000000315728 035 $a(EBL)909094 035 $a(OCoLC)818856615 035 $a(MiAaPQ)EBC909094 035 $a(EXLCZ)992670000000315728 100 $a20130418d2010|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 200 10$aCool Men and the Second Sex$b[electronic resource] 210 $aNew York $cColumbia University Press$d2010 215 $a1 online resource (239 p.) 225 1 $aGender and Culture Series 300 $aDescription based upon print version of record. 311 $a0-231-12963-7 327 $aContents; Preface: The Uncool Mother; 1. Quentin Tarantino: Anatomy of Cool; 2. Spike Lee and Brian De Palma: Scenarios of Race and Rape; 3. Edward Said: Gender, Culture, and Imperialism; 4. Andrew Ross: The Romance of the Bad Boy; 5. Henry Louis Gates Jr.: Figures in Black Masculinity; 6. Queer Theory and the Second Sex; Postscript: Doing the Right Thing; Notes; Works Cited; Index 330 $aAcademic superstars Andrew Ross, Edward Said, and Henry Louis Gates, Jr. Bad boy filmmakers Quentin Tarantino, Spike Lee, and Brian de Palma. What do these influential contemporary figures have in common? In Cool Men and the Second Sex, Susan Fraiman identifies them all with ""cool masculinity"" and boldly unpacks the gender politics of their work. According to Fraiman, ""cool men"" rebel against a mainstream defined as maternal. Bad boys resist the authority of women and banish mothers to the realm of the uncool. As a result, despite their hipness -- or because o 410 0$aGender and Culture Series 606 $aGender identity 606 $aMen - Attitudes 606 $aMen - Identity 608 $aElectronic books. 615 4$aGender identity. 615 4$aMen - Attitudes. 615 4$aMen - Identity. 676 $a305.31 700 $aFraiman$b Susan$0872993 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910462716403321 996 $aCool Men and the Second Sex$91948762 997 $aUNINA LEADER 00678cam 2200205-- 4500 001 991004402827807536 005 20251013152037.0 008 251013s2024----it a | b 001 0 ita d 020 $a9788800863070 040 $aBibl. Interfacoltà T. Pellegrino$bita 082 0 $a292.13 100 1 $aSantorelli, Biagio$0752643 245 10$aIntroduzione alla mitologia classica /$cBiagio Santorelli 260 $aMilano :$bLe Monnier Università,$c2024 300 $aX, 494 p., [32] p. di tav. :$bill. ;$c24 cm 650 4$aMitologia classica 830 0$aLe Monnier università 912 $a991004402827807536 996 $aIntroduzione alla mitologia classica$94452198 997 $aUNISALENTO LEADER 08284oam 2200553Mn 450 001 9910959099003321 005 20251116171015.0 010 $a1-351-98104-8 010 $a1-351-98103-X 010 $a1-315-27009-9 035 $a(CKB)4100000008871992 035 $a(MiAaPQ)EBC5846300 035 $a(OCoLC)1109972695$z(OCoLC)1111977079 035 $a(OCoLC-P)1109972695 035 $a(FlBoTFG)9781315270098 035 $a(EXLCZ)994100000008871992 100 $a20190725d2018 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDose-response Analysis With R 205 $a1st ed. 210 $cChapman & Hall$d2018 215 $a1 online resource (227 pages) 311 08$a1-138-03431-2 320 $aIncludes bibliographical references and index. 327 $aCover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1. Continuous data -- 1.1 Analysis of single dose-response curves -- 1.1.1 Inhibitory effect of secalonic acid -- 1.1.1.1 Fitting the model -- 1.1.1.2 Estimation of arbitrary ED values -- 1.1.2 Data from a fish test in ecotoxicology -- 1.1.3 Ferulic acid as an herbicide -- 1.1.4 Glyphosate in barley -- 1.1.5 Lower limits for dose-response data -- 1.1.6 A hormesis effect on lettuce growth -- 1.1.7 Nonlinear calibration -- 1.2 Analysis of multiple dose-response curves -- 1.2.1 Effect of an herbicide mixture on Galium aparine -- 1.2.2 Glyphosate and bentazone treatment of Sinapis alba -- 1.2.2.1 A joint dose-response model -- 1.2.2.2 Fitting separate dose-response models -- 2. Binary and binomial dose-response data -- 2.1 Analysis of single dose-response curves -- 2.1.1 Acute inhalation toxicity test -- 2.1.1.1 Link to ordinary logistic regression -- 2.1.2 Tumor incidence -- 2.1.3 Earthworm toxicity test: Abbott's formula -- 2.1.4 Another earthworms toxicity test: Estimating the upper limit -- 2.2 Analysis of multiple dose-response curves -- 2.2.1 Toxicity of fluoranthene under different ultraviolet radiation -- 2.2.2 Toxicity of different types of selenium -- 3. Count dose-response data -- 3.1 Analysis of single dose-response curves -- 3.1.1 Counting number of fronds -- 3.1.2 Counting offspring: Modeling hormesis -- 3.1.3 More counting offspring: Varying observation periods -- 3.2 Analysis of multiple dose-response curves -- 3.2.1 Counting bacteria colonies: Wadley's problem -- 4. Multinomial dose-response data -- 4.1 Trichotomous data -- 4.1.1 Insecticide residues -- 4.1.2 Effect of two arboviruses on chicken embryos -- 5. Time-to-event-response data -- 5.1 Analysis of a single germination curve -- 5.1.1 Germination of Stellaria media seeds. 327 $a5.2 Analysis of data from multiple germination curves -- 5.2.1 Time to death of daphnias -- 5.2.1.1 Step 1 -- 5.2.1.2 Step 2 -- 5.2.2 A hierarchical three-way factorial design -- 5.2.2.1 Step 1 -- 5.2.2.2 Step 2 -- 6. Benchmark dose estimation -- 6.1 Binomial dose-response data -- 6.1.1 Pathogens in food -- 6.1.2 Chromosomal damage -- 6.1.3 Tumor incidence continued: Integration of historical data -- 6.2 Continuous dose-response data -- 6.2.1 Toxicity of copper in an ecosystem with giant kelp -- 6.2.2 Toxicity of an antituberculosis drug -- 6.3 Model averaging -- 6.3.1 Pathogens in food revisited -- 6.3.2 Toxicity of an antituberculosis drug revisited -- 7. Hierarchical nonlinear models -- 7.1 Normally distributed dose-response data -- 7.2 The R package medrc -- 7.2.1 In vitro effects of the fungicide vinclozolin -- 7.2.2 Inhibition of photosynthesis in spinach -- 7.2.3 Herbicides with auxin effects -- 7.2.4 Drought stress resistance in Brassica oleracea -- Appendix A: Estimation -- A.1 Nonlinear least squares -- A.2 Maximum likelihood estimation -- A.2.1 Binomial dose-response data -- A.2.2 Count dose-response data -- A.2.2.1 The Poisson distribution -- A.2.2.2 The negative-binomial distribution -- A.2.3 Time-to-event-response data -- A.3 The transform-both-sides approach -- A.4 Robust estimation -- A.5 Sandwich variance estimators -- A.6 Constrained estimation -- A.7 Two-stage estimation for hierarchical models -- A.7.1 Technical replicates -- A.7.2 Two-stage approaches -- A.7.3 Lindstrom-Bates algorithm -- A.8 Starting values and self-starter functions -- A.9 Confidence intervals -- A.10 Prediction and inverse regression -- A.10.1 Effective dose -- A.10.2 Relative potency -- Appendix B: Dose-response model functions -- B.1 Log-logistic models -- B.1.1 Four-parameter log-logistic models -- B.1.1.1 Three-parameter version. 327 $aB.1.1.2 Two-parameter version -- B.1.1.3 E-max and Michaelis-Menten models -- B.1.2 Extensions -- B.1.2.1 Generalized log-logistic models -- B.1.2.2 A model with two slope parameters -- B.1.2.3 Hormesis models -- B.1.2.4 Two- and three-phase models -- B.1.2.5 Fractional polynomial models -- B.2 Log-normal models -- B.3 Weibull models -- B.3.1 Weibull type 1 models -- B.3.1.1 Exponential decay model -- B.3.1.2 Other special cases -- B.3.2 Weibull type 2 models -- B.3.2.1 Asymptotic regression -- B.3.2.2 Other special cases -- B.3.2.3 Generalized Weibull-2 model -- B.4 Other types of models -- B.4.1 Gamma models -- B.4.2 Multistage models -- B.4.3 NEC -- B.4.4 Biphasic models with a peak -- B.5 Fixing parameters -- Appendix C: R code for plots -- C.1 Continuous dose-response data -- C.1.1 Ferulic acid as an herbicide -- C.2 Estimation of BMD -- C.2.1 Pathogens in food -- C.2.2 Toxicity of an antituberculosis drug -- C.3 Hierarchical nonlinear models -- C.3.1 Inhibition of photosynthesis in spinach -- C.3.2 Herbicides with auxin effects -- C.3.3 Drought stress resistance in Brassica oleracea -- Bibliography -- Index. 330 $aNowadays the term dose-response is used in many different contexts and many different scientific disciplines including agriculture, biochemistry, chemistry, environmental sciences, genetics, pharmacology, plant sciences, toxicology, and zoology. In the 1940 and 1950s, dose-response analysis was intimately linked to evaluation of toxicity in terms of binary responses, such as immobility and mortality, with a limited number of doses of a toxic compound being compared to a control group (dose 0). Later, dose-response analysis has been extended to other types of data and to more complex experimental designs. Moreover, estimation of model parameters has undergone a dramatic change, from struggling with cumbersome manual operations and transformations with pen and paper to rapid calculations on any laptop. Advances in statistical software have fueled this development. Key Features: Provides a practical and comprehensive overview of dose-response analysis. Includes numerous real data examples to illustrate the methodology. R code is integrated into the text to give guidance on applying the methods. Written with minimal mathematics to be suitable for practitioners. Includes code and datasets on the book's GitHub: https://github.com/DoseResponse. This book focuses on estimation and interpretation of entirely parametric nonlinear dose-response models using the powerful statistical environment R. Specifically, this book introduces dose-response analysis of continuous, binomial, count, multinomial, and event-time dose-response data. The statistical models used are partly special cases, partly extensions of nonlinear regression models, generalized linear and nonlinear regression models, and nonlinear mixed-effects models (for hierarchical dose-response data). Both simple and complex dose-response experiments will be analyzed. 606 $aDrugs$xDose-response relationship 606 $aDrugs$xTesting$xComputer simulation 606 $aR (Computer program language) 615 0$aDrugs$xDose-response relationship. 615 0$aDrugs$xTesting$xComputer simulation. 615 0$aR (Computer program language) 676 $a615.1 700 $aRitz$b Christian$0513581 701 $aStreibig$b Jens C$0513582 701 $aJensen$b Signe Marie$01875254 701 $aGerhard$b Daniel$01875255 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910959099003321 996 $aDose-response Analysis With R$94486215 997 $aUNINA