North Dakota State University

To develop criteria for the classification of fibromyalgia, we studied 558 consecutive patients: 293 patients with fibromyalgia and 265 control patients. Interviews and examinations were performed by trained, blinded assessors. Control patients for the group with primary fibromyalgia were matched for age and sex, and limited to patients with disorders that could be confused with primary fibromyalgia. Control patients for the group with secondary-concomitant fibromyalgia were matched for age, sex, and concomitant rheumatic disorders. Widespread pain (axial plus upper and lower segment plus left- and right-sided pain) was found in 97.6% of all patients with fibromyalgia and in 69.1% of all control patients. The combination of widespread pain and mild or greater tenderness in greater than or equal to 11 of 18 tender point sites yielded a sensitivity of 88.4% and a specificity of 81.1%. Primary fibromyalgia patients and secondary-concomitant fibromyalgia patients did not differ statistically in any major study variable, and the criteria performed equally well in patients with and those without concomitant rheumatic conditions. The newly proposed criteria for the classification of fibromyalgia are 1) widespread pain in combination with 2) tenderness at 11 or more of the 18 specific tender point sites. No exclusions are made for the presence of concomitant radiographic or laboratory abnormalities. At the diagnostic or classification level, the distinction between primary fibromyalgia and secondary-concomitant fibromyalgia (as defined in the text) is abandoned.

The 6-31G* and 6-31G** basis sets previously introduced for first-row atoms have been extended through the second-row of the periodic table. Equilibrium geometries for one-heavy-atom hydrides calculated for the two-basis sets and using Hartree–Fock wave functions are in good agreement both with each other and with the experimental data. HF/6-31G* structures, obtained for two-heavy-atom hydrides and for a variety of hypervalent second-row molecules, are also in excellent accord with experimental equilibrium geometries. No large deviations between calculated and experimental single bond lengths have been noted, in contrast to previous work on analogous first-row compounds, where limiting Hartree–Fock distances were in error by up to a tenth of an angstrom. Equilibrium geometries calculated at the HF/6-31G level are consistently in better agreement with the experimental data than are those previously obtained using the simple split-valance 3-21G basis set for both normal- and hypervalent compounds. Normal-mode vibrational frequencies derived from 6-31G* level calculations are consistently larger than the corresponding experimental values, typically by 10%–15%; they are of much more uniform quality than those obtained from the 3-21G basis set. Hydrogenation energies calculated for normal- and hypervalent compounds are in moderate accord with experimental data, although in some instances large errors appear. Calculated energies relating to the stabilities of single and multiple bonds are in much better accord with the experimental energy differences.

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

BACKGROUND: Risk perceptions are central to many health behavior theories. However, the relationship between risk perceptions and behavior, muddied by instances of inappropriate assessment and analysis, often looks weak. METHOD: A meta-analysis of eligible studies assessing the bivariate association between adult vaccination and perceived likelihood, susceptibility, or severity was conducted. RESULTS: Thirty-four studies met inclusion criteria (N = 15,988). Risk likelihood (pooled r = .26), susceptibility (pooled r = .24), and severity (pooled r = .16) significantly predicted vaccination behavior. The risk perception-behavior relationship was larger for studies that were prospective, had higher quality risk measures, or had unskewed risk or behavior measures. CONCLUSIONS: The consistent relationships between risk perceptions and behavior, larger than suggested by prior meta-analyses, suggest that risk perceptions are rightly placed as core concepts in theories of health behavior.

A consensual, componential model of emotions conceptualises them as experiential, physiological, and behavioural responses to personally meaningful stimuli. The present review examines this model in terms of whether different types of emotion-evocative stimuli are associated with discrete and invariant patterns of responding in each response system, how such responses are structured, and if such responses converge across different response systems. Across response systems, the bulk of the available evidence favours the idea that measures of emotional responding reflect dimensions rather than discrete states. In addition, experiential, physiological, and behavioural response systems are associated with unique sources of variance, which in turn limits the magnitude of convergence across measures. Accordingly, the authors suggest that there is no "gold standard" measure of emotional responding. Rather, experiential, physiological, and behavioural measures are all relevant to understanding emotion and cannot be assumed to be interchangeable.

This review organizes a variety of phenomena related to emotional self-report. In doing so, the authors offer an accessibility model that specifies the types of factors that contribute to emotional self-reports under different reporting conditions. One important distinction is between emotion, which is episodic, experiential, and contextual, and beliefs about emotion, which are semantic, conceptual, and decontextualized. This distinction is important in understanding the discrepancies that often occur when people are asked to report on feelings they are currently experiencing versus those that they are not currently experiencing. The accessibility model provides an organizing framework for understanding self-reports of emotion and suggests some new directions for research.

Genetic linkage maps based on restriction fragment length polymorphisms are useful for many purposes; however, different populations are required to fulfill different objectives. Clones from the linkage map(s) are subsequently probed onto populations developed for special purposes such as gene tagging. Therefore, clones contained on the initial map(s) must be polymorphic on a wide range of genotypes to have maximum utility. The objectives of this research were to (i) calculate polymorphism information content values of 51 low-copy DNA clones and (ii) use the resulting values to choose potential mapping parents. Polymorphism information content was calculated using gene diversity by classifying restriction fragment patterns on a diverse set of 18 wheat genotypes. Combinations of potential parents were then compared by examining both the proportion of polymorphic clones and the likelihood that those mapped clones would give a polymorphism when used on other populations. Genotype pairs were identified that would map more highly informative DNA clones compared with a population derived from the most polymorphic potential parents. The methodologies used to characterize clones and rank potential parents should be applicable to other species and types of markers as well.

Abstract A new correlation-based transition model has been developed, which is based strictly on local variables. As a result, the transition model is compatible with modern computational fluid dynamics (CFD) approaches, such as unstructured grids and massive parallel execution. The model is based on two transport equations, one for intermittency and one for the transition onset criteria in terms of momentum thickness Reynolds number. The proposed transport equations do not attempt to model the physics of the transition process (unlike, e.g., turbulence models) but form a framework for the implementation of correlation-based models into general-purpose CFD methods. Part I (this part) of this paper gives a detailed description of the mathematical formulation of the model and some of the basic test cases used for model validation, including a two-dimensional turbine blade. Part II (Langtry, R. B., Menter, F. R., Likki, S. R., Suzen, Y. B., Huang, P. G., and Völker, S., 2006, ASME J. Turbomach., 128(3), pp. 423–434) of the paper details a significant number of test cases that have been used to validate the transition model for turbomachinery and aerodynamic applications. The authors believe that the current formulation is a significant step forward in engineering transition modeling, as it allows the combination of correlation-based transition models with general purpose CFD codes.

A selective review of research highlights the emerging view of groups as information processors. In this review, the authors include research on processing objectives, attention, encoding, storage, retrieval, processing, response, feedback, and learning in small interacting task groups. The groups as information processors perspective underscores several characteristic dimensions of variability in group performance of cognitive tasks, namely, commonality-uniqueness of information, convergence-diversity of ideas, accentuation-attenuation of cognitive processes, and belongingness-distinctiveness of members. A combination of contributions framework provides an additional conceptualization of information processing in groups. The authors also address implications, caveats, and questions for future research and theory regarding groups as information processors.

Scott Jackson, Jeremy Schmutz, Phillip McClean and colleagues report the genome sequence of the common bean (Phaseolus vulgaris) and resequenced wild individuals and landraces from Mesoamerican and Andean gene pools, showing that common bean underwent two independent domestications. Common bean (Phaseolus vulgaris L.) is the most important grain legume for human consumption and has a role in sustainable agriculture owing to its ability to fix atmospheric nitrogen. We assembled 473 Mb of the 587-Mb genome and genetically anchored 98% of this sequence in 11 chromosome-scale pseudomolecules. We compared the genome for the common bean against the soybean genome to find changes in soybean resulting from polyploidy. Using resequencing of 60 wild individuals and 100 landraces from the genetically differentiated Mesoamerican and Andean gene pools, we confirmed 2 independent domestications from genetic pools that diverged before human colonization. Less than 10% of the 74 Mb of sequence putatively involved in domestication was shared by the two domestication events. We identified a set of genes linked with increased leaf and seed size and combined these results with quantitative trait locus data from Mesoamerican cultivars. Genes affected by domestication may be useful for genomics-enabled crop improvement.

Abstract Linear mixed‐effects models are powerful tools for analysing complex datasets with repeated or clustered observations, a common data structure in ecology and evolution. Mixed‐effects models involve complex fitting procedures and make several assumptions, in particular about the distribution of residual and random effects. Violations of these assumptions are common in real datasets, yet it is not always clear how much these violations matter to accurate and unbiased estimation. Here we address the consequences of violations in distributional assumptions and the impact of missing random effect components on model estimates. In particular, we evaluate the effects of skewed, bimodal and heteroscedastic random effect and residual variances, of missing random effect terms and of correlated fixed effect predictors. We focus on bias and prediction error on estimates of fixed and random effects. Model estimates were usually robust to violations of assumptions, with the exception of slight upward biases in estimates of random effect variance if the generating distribution was bimodal but was modelled by Gaussian error distributions. Further, estimates for (random effect) components that violated distributional assumptions became less precise but remained unbiased. However, this particular problem did not affect other parameters of the model. The same pattern was found for strongly correlated fixed effects, which led to imprecise, but unbiased estimates, with uncertainty estimates reflecting imprecision. Unmodelled sources of random effect variance had predictable effects on variance component estimates. The pattern is best viewed as a cascade of hierarchical grouping factors. Variances trickle down the hierarchy such that missing higher‐level random effect variances pool at lower levels and missing lower‐level and crossed random effect variances manifest as residual variance. Overall, our results show remarkable robustness of mixed‐effects models that should allow researchers to use mixed‐effects models even if the distributional assumptions are objectively violated. However, this does not free researchers from careful evaluation of the model. Estimates that are based on data that show clear violations of key assumptions should be treated with caution because individual datasets might give highly imprecise estimates, even if they will be unbiased on average across datasets.

List of Tables and Figures Acknowledgments Part I: The Origins of ESM Chapter 1: Epistemological Foundations for the Measurement of Experience A Systematic Phenomenology The Experience Sampling Method A Brief History How Trustworthy Are Subjective Self-Reports? What Can We Learn From ESM? Chapter 2: Theoretical Foundations of ESM Biology, Culture, and Daily Behavior Subjective Experience in Context: The Interplay of Psychological Processes and Cognitive Functions Interaction of Individuals and Environments Experience Fluctuations, Well-Being, and Development A Theoretical Compass for Exploring Experience Part II: How to Measure the Quality of Everyday Life Chapter 3: Collecting the Data Designing a Study Using ESM Equipment and Signaling Schedules Designing the Form Other Design Decisions Implementing the Study Documentation Chapter 4: Dealing With the Data: Coding, Entry, Cleaning, and Data Management Developing a Codebook Coding the External Coordinates of Experience Conding the Internal Coordinates of Experience What to Do With the Codes Once They Are Developed: Physically Coding and Entering the Data Setup, Cleaning, and Manipulation of Data Files Response-Level Data and Person-Level Data Postentry Data Manipulation Data File Management and Documentation Chapter 5: Types of Analyses Qualitative Approaches Graphic and Numeric Descriptive Information Planning for Statistical Analyses OLS Statistical Techniques Multilevel and Other Complex Statistical Techniques Chapter 6: Psychometrics of ESM Data Validity of Method Validity of ESM Measurements Reliability of ESM Measurements Part III: Uses of ESM in Social Science Research Chapter 7: Samples of Experience The Who, What, Where, When, and How of Daily Experiences Quality of Experience in Selected Activities Quality of Experience of Selected Groups of People Emotions, Well-Being, and Flow Chapter 8: The Experience of Males and Females Differences in Activities Differences in Companionship Similiarities and Differences in Emotional Experience Other Gender Differences in Adolescence Chapter 9: The Experience of Family Life Methodological Concerns and Variations The Couple Relationship The Arrival of the First Child Juggling Work and Family Roles The Adolescent's Experience of Family Transmission of Emotions Between Family Members Comparisons Between Families: Optimal Conditions for Adolescent Development Chapter 10: The Experience of Work Methodological Concerns and Variations Time and Work The Quality of Experience at Work: General Trends The Quality of Experience Across Workers The Quality of Experience Across Work Activities The Intersection of Work and Family The Experience of Unemployment Adolescent Work Chapter 11: Examining Cross-Cultural Differences Methodological Concerns and Variations Culture and Time Use Cross-Cultural Variation in General Affective Experience Culture and Subjective Experience in Various Activities Cross-Cultural Examination of Flow Studies of American Subcultures Chapter 12: Educational Applications Methodological Concerns and Variations Time Use and Structure of Classrooms The Quality of Students' Classroom Experiences Comparing Students' Classroom Experiences After-School Programs Studies of Adult Learners The Experience of Teachers Chapter 13: Clinical Applications Methodological Concerns and Variations Use of ESM for Describing and Contextualizing Experiences of Disorder Use of ESM in Therapy and in Treatment Evaluation Concluding Thoughts Ten Major Issues ESM Illuminates Appendix A: Sample ESM Data Collection Forms (ESFs) Appendix B: ESM Coding Scheme Used in the Sloan Study of Youth and Social Development References Index About the Authors

Bread wheat (Triticum aestivum) is a globally important crop, accounting for 20 per cent of the calories consumed by humans. Major efforts are underway worldwide to increase wheat production by extending genetic diversity and analysing key traits, and genomic resources can accelerate progress. But so far the very large size and polyploid complexity of the bread wheat genome have been substantial barriers to genome analysis. Here we report the sequencing of its large, 17-gigabase-pair, hexaploid genome using 454 pyrosequencing, and comparison of this with the sequences of diploid ancestral and progenitor genomes. We identified between 94,000 and 96,000 genes, and assigned two-thirds to the three component genomes (A, B and D) of hexaploid wheat. High-resolution synteny maps identified many small disruptions to conserved gene order. We show that the hexaploid genome is highly dynamic, with significant loss of gene family members on polyploidization and domestication, and an abundance of gene fragments. Several classes of genes involved in energy harvesting, metabolism and growth are among expanded gene families that could be associated with crop productivity. Our analyses, coupled with the identification of extensive genetic variation, provide a resource for accelerating gene discovery and improving this major crop. Sequencing of the hexaploid bread wheat genome shows that it is highly dynamic, with significant loss of gene family members on polyploidization and domestication, and an abundance of gene fragments. Two groups in this issue report the compilation and analysis of the genome sequences of major cereal crops — bread wheat and barley — providing important resources for future crop improvement. Bread wheat accounts for one-fifth of the calories consumed by humankind. It has a very large and complex hexaploid genome of 17 Gigabases. Michael Bevan and colleagues have analysed the genome using 454 pyrosequencing and compared it with diploid ancestral and progenitor genomes. The authors discovered significant loss of gene family members upon polyploidization and domestication, and expansion of gene classes that may be associated with crop productivity. Barley is one of the earliest domesticated plant crops. Although diploid, it has a very large genome of 5.1 Gigabases. Nils Stein and colleagues describe a physical map anchored to a high-resolution genetic map, on top of which they have overlaid a deep whole-genome shotgun assembly, cDNA and RNA-seq data to provide the first in-depth genome-wide survey of the barley genome.

UNLABELLED: SUMMARY Sclerotinia sclerotiorum (Lib.) de Bary is a necrotrophic fungal pathogen causing disease in a wide range of plants. This review summarizes current knowledge of mechanisms employed by the fungus to parasitize its host with emphasis on biology, physiology and molecular aspects of pathogenicity. In addition, current tools for research and strategies to combat S. sclerotiorum are discussed. TAXONOMY: Sclerotinia sclerotiorum (Lib.) de Bary: kingdom Fungi, phylum Ascomycota, class Discomycetes, order Helotiales, family Sclerotiniaceae, genus Sclerotinia. IDENTIFICATION: Hyphae are hyaline, septate, branched and multinucleate. Mycelium may appear white to tan in culture and in planta. No asexual conidia are produced. Long-term survival is mediated through the sclerotium; a pigmented, multi-hyphal structure that can remain viable over long periods of time under unfavourable conditions for growth. Sclerotia can germinate to produce mycelia or apothecia depending on environmental conditions. Apothecia produce ascospores, which are the primary means of infection in most host plants. HOST RANGE: S. sclerotiorum is capable of colonizing over 400 plant species found worldwide. The majority of these species are dicotyledonous, although a number of agriculturally significant monocotyledonous plants are also hosts. Disease symptoms: Leaves usually have water-soaked lesions that expand rapidly and move down the petiole into the stem. Infected stems of some species will first develop dark lesions whereas the initial indication in other hosts is the appearance of water-soaked stem lesions. Lesions usually develop into necrotic tissues that subsequently develop patches of fluffy white mycelium, often with sclerotia, which is the most obvious sign of plants infected with S. sclerotiorum. USEFUL WEBSITES: http://www.whitemoldresearch.com; http://www.broad.mit.edu/annotation/fungi/sclerotinia_sclerotiorum.

Growing interest in proximate and ultimate causes and consequences of between- and within-individual variation in labile components of the phenotype - such as behaviour or physiology - characterizes current research in evolutionary ecology. The study of individual variation requires tools for quantification and decomposition of phenotypic variation into between- and within-individual components. This is essential as variance components differ in their ecological and evolutionary implications. We provide an overview of how mixed-effect models can be used to partition variation in, and correlations among, phenotypic attributes into between- and within-individual variance components. Optimal sampling schemes to accurately estimate (with sufficient power) a wide range of repeatabilities and key (co)variance components, such as between- and within-individual correlations, are detailed. Mixed-effect models enable the usage of unambiguous terminology for patterns of biological variation that currently lack a formal statistical definition (e.g. 'animal personality' or 'behavioural syndromes'), and facilitate cross-fertilisation between disciplines such as behavioural ecology, ecological physiology and quantitative genetics.

The objective of this study was to investigate how 6 commonly used solvent systems affected the yields of phenolic substances and the antioxidant capacity of extracts from 8 major classes of food legumes. Several antioxidant-related phytochemical compositions, namely, total phenolic content (TPC), total flavonoids content (TFC), and condensed tannins content (CTC), were investigated. In addition, antioxidant activities were tested using 2,2-diphenyl-1-picryhydrazyl (DPPH) free radical scavenging, ferric-reducing antioxidant power (FRAP), and the oxygen radical absorbance capacity (ORAC). The results showed that the 50% acetone extracts exhibited the highest TPC for yellow pea, green pea, chickpea, and yellow soybean. Acidic 70% acetone (+0.5% acetic acid) extracts exhibited the highest TPC, TFC, and FRAP values for black bean, lentil, black soybean, and red kidney bean. The 80% acetone extracts exhibited the highest TFC, CTC, and DPPH-free radical scavenging activity for yellow pea, green pea, chickpea, and yellow soybean. The 70% ethanol extracts exhibited the greatest ORAC value for all selected legumes. These results indicated that solvents with different polarity had significant effects on total phenolic contents, extracted components, and antioxidant activities. High correlations between phenolic compositions and antioxidant activities of legume extracts were observed. The information is of interest to the nutraceutical food/ingredient industries since legumes are a rich source of antioxidants.

BEHAVIOR MODIFICATION: PRINCIPLES AND PROCEDURES, Fifth Edition, uses a precise, step-by-step, scientific approach to explain human behavior. Case studies and examples illustrate key principles.

Both cellulose nanocrystals (CNCs) and cellulose nanofibrils (CNFs) are nanoscale cellulose fibers that have shown reinforcing effects in polymer nanocomposites. CNCs and CNFs are different in shape, size and composition. This study systematically compared their morphologies, crystalline structure, dispersion properties in polyethylene oxide (PEO) matrix, interactions with matrix, and the resulting reinforcing effects on the matrix polymer. Transparent PEO/CNC and PEO/CNF nanocomposites comprising up to 10 wt % nanofibers were obtained via solution casting. Scanning electron microscopy (SEM), wide-angle X-ray diffraction (WXRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), dynamic mechanical analyzer (DMA), and tensile testing were used to examine the above-mentioned properties of nanocellulose fibers and composites. At the same nanocellulose concentration, CNFs led to higher strength and modulus than did CNCs due to CNFs' larger aspect ratio and fiber entanglement, but lower strain-at-failure because of their relatively large fiber agglomerates. The Halpin-Kardos and Ouali models were used to simulate the modulus of the composites and good agreements were found between the predicted and experimental values. This type of systematic comparative study can help to develop the criteria for selecting proper nanocellulose as a biobased nano-reinforcement material in polymer nanocomposites.

During the past decade, the plant disease called scab or Fusarium head blight of wheat and barley has reached epidemic proportions in North America and elsewhere in the world. Scab is an economically devastating plant disease, not only because it causes significant reduction in seed yields and quality, but also because infested seeds are often contaminated with trichothecene and estrogenic mycotoxins that pose a serious threat to animal health and food safety. To test whether the primary etiological agent of scab, the fungus Fusarium graminearum, is panmictic throughout its range, allelic genealogies were constructed from six single-copy nuclear genes from strains selected to represent the global genetic diversity of this pathogen. Excluding one hybrid strain, all six genealogies recovered the same seven biogeographically structured lineages, suggesting that they represent phylogenetically distinct species among which gene flow has been very limited during their evolutionary history. Parsimony analysis of the combined data set comprising 7,120 aligned nucleotide characters resolved most relationships among the seven lineages of the F. graminearum clade and related fusaria included in the study. Phylogenetic evidence is also presented for introgressive hybridization and intragenic recombination among lineages of the F. graminearum clade in nature.