A Bayesian adaptive marker‐stratified design for molecularly targeted agents with customized hierarchical modeling

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker‐stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta‐binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a “customized” equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs

KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.Comment: 9 page

Land Use Effects On Energy And Water Balance-developing A Land Use Adapted Drought Index

Climate change is expected to increase the frequency, intensity and duration of droughts in all parts of the United States (US). Snow packs are disappearing earlier in the spring and summer, with reduced stream-flow. Lower reservoir levels, higher temperatures, and greater precipitation variability have been observed. Drought events in the US have threatened drinking water supplies for communities in Maryland and Chesapeake Bay as observed in 2001 through September 2002; Lake Mead in Las Vegas in 2000 through 2004; Peace River and Lake Okeechobee in South Florida in 2006; and Lake Lanier in Atlanta, Georgia in 2007. ENSO influences the climate of Florida; where El Niño years tend to be cooler and wetter, while La Niña years tend to be warmer and drier than normal in the fall through the spring, with the strongest effect in the winter. Both prolonged heavy rainfall and drought potentially have impacts on land uses and many aspects of Florida\u27s economy and quality of life. Drought indices could integrate various hydrological and meteorological parameters and quantify climate anomalies in terms of intensity, duration, and spatial extent, thus making it easier to communicate information to diverse users. Hence, understanding local ENSO patterns on regional scales and developing a new land use drought index in Florida are critical in agriculture and water resources planning and managements. Current drought indices have limitations and drawbacks such as calculation using climate data from meteorological stations, which are point measurements. In addition, weather stations are scarce in remote areas and are not uniformly distributed. Currently used drought indices like the iv PDSI and the Standardized Precipitation Index (SPI) could not fully demonstrate the land use effects. Other limitations include no single index that addresses universal drought impact. Hence, there is a renewed interest to develop a new “Regional Land Use Drought Index (RLDI) that could be applied for various land use areas and serve for short term water resources planning. In this study, the first and second research topics investigated water and energy budgets on the specific and important land use areas (urban, forest, agriculture and lake) in the State of Florida by using the North American Regional Reanalysis (NARR) reanalysis data. NARR data were used to understand how drought events, EI Niño, La Niña, and seasonal and inter-annual variations in climatic variables affect the hydrologic and energy cycle over different land use areas. The results showed that the NARR data could provide valuable, independent analysis of the water and energy budgets for various land uses in Florida. Finally, the high resolution land use (32km×32km) adapted drought indices were developed based on the NARR data from 1979 to 2002. The new regional land use drought indices were developed from normalized Bowen ratio and the results showed that they could reflect not only the level of severity in drought events resulting from land use effects, but also La Niña driven drought impacts

Developing Global Workforce: An Integrative Intercultural Effectiveness Model for International Human Resource Development

Globalization has a significant impact on the field of human resource development (HRD), especially on international HRD (IHRD). The challenge of developing global workforce with intercultural competencies has received extensive attention. It is necessary to reexamine the structure and content of IHRD programs that facilitate individual to be interculturally competent. This paper attempts to propose an integrative intercultural effectiveness (ICE) model, modified from Han‘s (2011) study, as the guidelines for IHRD to identify the process and content in developing intercultural competencies. Keywords: Intercultural Effectiveness (ICE) Competencies, Human Resource Development (HRD), International HRD (IHRD), Cross-cultural Learning, Transformative Learnin

Empirical studies on the network of social groups: the case of Tencent QQ

Participation in social groups are important but the collective behaviors of human as a group are difficult to analyze due to the difficulties to quantify ordinary social relation, group membership, and to collect a comprehensive dataset. Such difficulties can be circumvented by analyzing online social networks. In this paper, we analyze a comprehensive dataset obtained from Tencent QQ, an instant messenger with the highest market share in China. Specifically, we analyze three derivative networks involving groups and their members -- the hypergraph of groups, the network of groups and the user network -- to reveal social interactions at microscopic and mesoscopic level. Our results uncover interesting behaviors on the growth of user groups, the interactions between groups, and their relationship with member age and gender. These findings lead to insights which are difficult to obtain in ordinary social networks.Comment: 18 pages, 9 figure