University of South Carolina Upstate

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.

The effects of transformational leadership on the outcomes of specific change initiatives are not well understood. Conversely, organizational change studies have examined leader behaviors during specific change implementations yet have failed to link these to broader leadership theories. In this study, the authors investigate the relationship between transformational and change leadership and followers' commitment to a particular change initiative as a function of the personal impact of the changes. Transformational leadership was found to be more strongly related to followers' change commitment than change-specific leadership practices, especially when the change had significant personal impact. For leaders who were not viewed as transformational, good change-management practices were found to be associated with higher levels of change commitment.

Absorptive capacity is a firm’s ability to identify, assimilate, transform, and apply valuable external knowledge. It is considered an imperative for business success. Modern information technologies perform a critical role in the development and maintenance of a firm’s absorptive capacity. We provide an assessment of absorptive capacity in the information systems literature. IS scholars have used the absorptive capacity construct in diverse and often contradictory ways. Confusion surrounds how absorptive capacity should be conceptualized, its appropriate level of analysis, and how it can be measured. Our aim in reviewing this construct is to reduce such confusion by improving our understanding of absorptive capacity and guiding its effective use in IS research. We trace the evolution of the absorptive capacity construct in the broader organizational literature and pay special attention to its conceptualization, assumptions, and relationship to organizational learning. Following this, we investigate how absorptive capacity has been conceptualized, measured, and used in IS research. We also examine how absorptive capacity fits into distinct IS themes and facilitates understanding of various IS phenomena. Based on our analysis, we provide a framework through which IS researchers can more fully leverage the rich aspects of absorptive capacity when investigating the role of information technology in organizations.

This paper investigates how information technology (IT) facilitates a firm's customer agility and, in turn, competitive activity. Customer agility captures the extent to which a firm is able to sense and respond quickly to customer-based opportunities for innovation and competitive action. Drawing from the dynamic capability and IT business value research streams, we propose that IT plays an important role in facilitating a "knowledge creating" synergy derived from the interaction between a firm's Web-based customer infrastructure and its analytical ability. This will enhance the firm's ability to sense customer-based opportunities. IT also plays an important role in "process enhancing" synergy obtained from the interaction between a firm's coordination efforts and its level of information systems integration, which facilitates the firm's ability to respond to those opportunities. We also leverage the competitive dynamics and strategic alignment literature to propose that the alignment between customer-sensing capability and customer-responding capability will impact the firm's competitive activity. We test our model with a two-stage research design in which we survey marketing executives of high-tech firms. Our results show that a Web-based customer infrastructure facilitates a firm's customer-sensing capability; furthermore, analytical ability positively moderates this relationship. We also find that internal systems integration positively moderates the relationship between interfunctional coordination and a firm's customer-responding capability. Finally, our results show that agility alignment affects the efficacy of a firm's competitive actions. In particular, action efficacy is higher when sensing and responding capabilities are both high.

Organizations are concerned with the impact organizational change can have on both individuals' response to the change itself and their ongoing relationship with the organization. This study investigated how organizational changes in 32 different organizations (public and private) affected individuals' commitment to the specific change and their broader commitment to the organization. The results indicate that both types of commitment may be best understood in terms of a 3‐way interaction between the overall favorableness (positive/negative) of the change for the work unit members, the extent of the change in the work unit, and the impact of the change on the individual's job. In addition, the fairness of the change process was found to interact with the effects of work unit change on organizational commitment. The implications of these results for future research and practice are discussed.

The extent to which attitudes toward organizational changes may be affected by contextual (other changes going on) and personal (self-efficacy) factors was investigated with a multilevel design involving 25 different changes. Even after aspects of the change itself were controlled, the interaction between the context and the individual difference explained significant variance in attitudes toward those specific changes. The positive relationship between self-efficacy and commitment to the change was stronger as the amount of simultaneous and overlapping change in the surroundings increased. The implications for research and practice are discussed.

The task of this study is to determine if certain political and socioeconomic variables have strong relationships with political repression conceptualized as disappearance, detention, torture, and political killings. The perspective of the study is from the question of why do people in power — with so many options available — choose repression as a method of rule. Repression is coded into numerical values from the State Department Country Reports, and then relationships with the degree of democracy, socioeconomic conditions, inequality, rate of economic change, and the level of economic development are tested in regression models. Significant relationships are found. The degree of democracy, the extent of inequality in society, and economic growth rate go a long way to explain and predict political repression in a parsimonious model.

While information on multidimensional constructs and empirical methods has become more accessible, there remain substantial challenges to theorizing about their form and implications. There are at least two ostensible reasons for such difficulties. First is the issue of terminology; many different terms are currently used to represent the same structural concept, and there is no evidence of standardization taking place around a single set of terms. Second, many studies do not clearly explain the theoretical reasons for choosing the specific multidimensional form of their constructs. To address these deficiencies, we use concepts from the research methods literature, and illustrations from the information systems (IS) literature, to review definitions and issues related to conceptualizing and operationalizing structural models that include multidimensional constructs. Such advice is necessary if we are going to develop and test increasingly sophisticated theoretical models in IS research. We also offer guidelines about how to conceptualize specific forms of multidimensional constructs. By lending greater conceptual clarity to the literature, we believe that this paper provides a foundation for future research incorporating multidimensional constructs in empirical analysis.

BACKGROUND: Safe, effective interventions to improve cancer-related fatigue (CRF) are needed because it remains a prevalent, distressing, and activity-limiting symptom. Based on pilot data, a phase III trial was developed to evaluate the efficacy of American ginseng on CRF. METHODS: A multisite, double-blind trial randomized fatigued cancer survivors to 2000mg of American ginseng vs a placebo for 8 weeks. The primary endpoint was the general subscale of the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF) at 4 weeks. Changes from baseline at 4 and 8 weeks were evaluated between arms by a two-sided, two-sample t test. Toxicities were evaluated by self-report and the National Cancer Institute's Common Terminology Criteria for Adverse Events (CTCAE) provider grading. RESULTS: Three hundred sixty-four participants were enrolled from 40 institutions. Changes from baseline in the general subscale of the MFSI-SF were 14.4 (standard deviation [SD] = 27.1) in the ginseng arm vs 8.2 (SD = 24.8) in the placebo arm at 4 weeks (P = .07). A statistically significant difference was seen at 8 weeks with a change score of 20 (SD = 27) for the ginseng group and 10.3 (SD = 26.1) for the placebo group (P = .003). Greater benefit was reported in patients receiving active cancer treatment vs those who had completed treatment. Toxicities per self-report and CTCAE grading did not differ statistically significantly between arms. CONCLUSIONS: Data support the benefit of American ginseng, 2000mg daily, on CRF over an 8-week period. There were no discernible toxicities associated with the treatment. Studies to increase knowledge to guide the role of ginseng to improve CRF are needed.

Although abundant advice is available for how to develop and validate multi-item scales based on reflective constructs, scant attention has been directed to how to construct and validate formative constructs. Such advice is important because (1) theory suggests many constructs are formative and (2) recent advances in software render testing models with formative constructs more tractable. In this tutorial, our goal is to enhance understanding of formative constructs at the conceptual, statistical and methodological levels. Specifically, we (1) provide general principles for specifying whether a construct should be conceptually modeled as reflective or formative, (2) discuss the statistical logic behind formative constructs, and (3) illustrate how to model and evaluate formative constructs. In particular, we provide a tutorial in which we test and validate professional reward structure, a formative construct, in two popular structural equation modeling programs: EQS and PLS. We conclude with a summary of guidelines for how to conduct and evaluate research using formative constructs.

The search for the prosocial personality has been long and controversial. The current research explores the general patterns underlying prosocial decisions, linking personality, emotion, and overt prosocial behavior. Using a multimethod approach, we explored the links between the Big Five dimensions of personality and prosocial responding. Across three studies, we found that agreeableness was the dimension of personality most closely associated with emotional reactions to victims in need of help, and subsequent decisions to help those individuals. Results suggest that prosocial processes, including emotions, cognitions, and behaviors, may be part of a more general motivational process linked to personality.

Although scholars have provided advice regarding how to conceptualize multidimensional constructs, less attention has been directed on how to evaluate structural equation models that include multidimensional constructs. Further, the extant information systems literature has provided little, and sometimes contradictory, direction on how to operationalize multidimensional constructs. This gap in how we approach multidimensional constructs merits attention because: (1) establishing construct validity is critical to testing theory and (2) recent advances in software enable testing models with multidimensional constructs more readily. Therefore, this tutorial (1) describes different forms of multidimensional constructs and (2) illustrates how to integrate superordinate and aggregate multidimensional constructs in structural equation models. In doing so, we offer guidelines and examples for how to conduct and evaluate research using multidimensional constructs.

Abstract Rituximab, a chimeric antibody that targets CD20+ B cells, produces a 48% response rate in patients with refractory low-grade non-Hodgkin lymphoma. In this phase II trial, patients with low-grade non-Hodgkin lymphoma who had previously received no systemic therapy were treated with rituximab, 375 mg/m2, administered by IV infusion for 4 consecutive weeks. Patients with objective response or stable disease received repeat 4-week courses of rituximab at 6-month intervals. At the time of initial reevaluation at 6 weeks, 21 of 39 patients (54%) had objective response to treatment, and an additional 14 patients (36%) had stable disease or minor response. Response rates were similar in patients with follicular and small lymphocytic (CLL-type) lymphoma (52% versus 57%, respectively). At present, follow-up is short and only 13 patients have undergone a second course of rituximab treatment. However, 4 additional responses were documented either prior to the second course of rituximab (2 patients) or following the second course (2 patients) and 4 patients improved from partial to complete response. The current response rate is 64%, with 6 complete responses (15%). Treatment with rituximab was well tolerated, with only 1 patient experiencing grade 3/4 infusion-related toxicity. Rituximab is well tolerated and highly active in patients with low-grade non-Hodgkin lymphoma previously untreated with systemic therapy. Although further follow-up is required, the demonstration of minimal toxicity and considerable activity of this new biologic agent represents an important beginning of more specific, less toxic treatment for this important group of cancer patients.

Sixteen students, randomly selected from a pool of 91 fourth-graders in a midsize elementary school, were interviewed about their reading choices. The interviews revealed that children had different degrees of motivation, following several patterns. Children chose narrative literature for these reasons: The books related to their personal interests The characteristics of the books appealed to them The students were given choices Expository books were chosen for these reasons: The knowledge gained from books The books related to personal interests The students were given choices The main source of book referrals was the school library. Children also reported being motivated to read by family members, teachers, and peers. Receiving books as gifts was frequently mentioned as another source of motivation. Classroom implications are suggested, based on the findings from the interviews. Teachers can increase student motivation by allowing self-selection, giving attention to characteristics of books, identifying the personal interests of students, providing access to a variety of books, and actively involving others in sharing books with children.

The pursuit of happiness has a long history as a primary political end in Western political thought. Along with traditional economic indicators, policy makers are increasingly concerned with the subjective well-being of a society as a measure for its success. It is important to understand the nature of happiness and ask what can be done to improve it. This article builds upon existing literature that consistently identifies health, wealth, and social connectedness as key predictors of happiness. We find that the design and conditions of cities are associated with the happiness of residents in 10 urban areas. Cities that provide easy access to convenient public transportation and to cultural and leisure amenities promote happiness. Cities that are affordable and serve as good places to raise children also have happier residents. We suggest that such places foster the types of social connections that can improve happiness and ultimately enhance the attractiveness of living in the city.

Battered women seeking shelter were surveyed at intake about their experiences with pet abuse and the roles of pets in their abusive relationships. Of the women with pets, 46.5% reported that their batterers had threatened to harm or actually harmed their pets. Pets often served as important sources of emotional support during the relationship, particularly for women reporting pet abuse. Women continued to worry about the safety of their pets, especially given that many pets remained with the abusive partner. Implications of the findings are discussed and recommendations are presented for domestic violence and other professionals.

Four age groups of Fischer 344 rats (6-, 12-, 18-, and 24-months of age) were compared on a battery of reflexive and locomotor tasks. The simple reflexive tasks such as placing, hopping, negative geotaxis and surface and mid-air righting showed little or no change as a function of age. In contrast, tasks requiring more coordinated control of motor and reflexive responses such as suspension from a horizontal wire, descent of a wire mesh pole, traversal of an elevated platform and rotorod performance showed significant declines with age, with some declines noticeable early in the lifespan. When these results are compared with the results of tasks conducted with infant rats, it is noted that the reflexive and motor skills that emerge early in development are the least affected by the aging process. This observation suggests a first-in, last-out sequence for reflexive and motor behaviors.

In this study, we examine trust in information technology's (IT) relationship with postadoption exploration of knowledge management systems (KMS). We introduce and distinguish between trust in IT and trust in IT support staff as object-specific beliefs that influence technology's infusion into organizations. We suggest that these object-specific beliefs' influence on intention to explore KMS is mediated by behavioral beliefs about IT (e.g., perceived usefulness and perceived ease of use). To test the model, we completed two studies. Study 1 examined users' perceptions of a knowledge portal. Study 2 examined IT professionals' perceptions of KMS. Across studies, our analysis suggests that trust in IT exerts direct effects on behavioral beliefs leading to intentions to explore KMS. Further, when compared to trust in IT support, we found that trust in IT played a more central role in shaping behavioral beliefs leading to exploration of IT. Implications for research and practice are offered.

With vast amounts of data being generated daily and the ever increasing interconnectivity of the world's internet infrastructures, a machine learning based Intrusion Detection Systems (IDS) has become a vital component to protect our economic and national security. Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection. The single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion patterns. Particularly, the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples. In order to further enhance the performance of machine learning based IDS, we propose the Big Data based Hierarchical Deep Learning System (BDHDLS). BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload. Each deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one cluster. This strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches. Based on parallel training strategy and big data techniques, the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.