Proceedings of the I ndo‐ U.S. bilateral workshop on accelerating botanicals/biologics agent development research for cancer chemoprevention, treatment, and survival

With the evolving evidence of the promise of botanicals/biologics for cancer chemoprevention and treatment, an Indo‐U.S. collaborative Workshop focusing on “Accelerating Botanicals Agent Development Research for Cancer Chemoprevention and Treatment” was conducted at the Moffitt Cancer Center, 29–31 May 2012. Funded by the Indo‐U.S. Science and Technology Forum, a joint initiative of Governments of India and the United States of America and the Moffitt Cancer Center, the overall goals of this workshop were to enhance the knowledge (agents, molecular targets, biomarkers, approaches, target populations, regulatory standards, priorities, resources) of a multinational, multidisciplinary team of researcher's to systematically accelerate the design, to conduct a successful clinical trials to evaluate botanicals/biologics for cancer chemoprevention and treatment, and to achieve efficient translation of these discoveries into the standards for clinical practice that will ultimately impact cancer morbidity and mortality. Expert panelists were drawn from a diverse group of stakeholders, representing the leadership from the National Cancer Institute's Office of Cancer Complementary and Alternative Medicine (OCCAM), NCI Experimental Therapeutics (NExT), Food and Drug Administration, national scientific leadership from India, and a distinguished group of population, basic and clinical scientists from the two countries, including leaders in bioinformatics, social sciences, and biostatisticians. At the end of the workshop, we established four Indo‐U.S. working research collaborative teams focused on identifying and prioritizing agents targeting four cancers that are of priority to both countries. Presented are some of the key proceedings and future goals discussed in the proceedings of this workshop. With the evolving evidence of the promise of botanicals/biologics for cancer chemoprevention and treatment, the proceedings of the Indo‐U.S. collaborative Workshop represent one of the most contemporary issues in Cancer Medicine .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96353/1/cam442.pd

Proceedings of the I ndo‐ U.S. bilateral workshop on accelerating botanicals/biologics agent development research for cancer chemoprevention, treatment, and survival

With the evolving evidence of the promise of botanicals/biologics for cancer chemoprevention and treatment, an Indo‐U.S. collaborative Workshop focusing on “Accelerating Botanicals Agent Development Research for Cancer Chemoprevention and Treatment” was conducted at the Moffitt Cancer Center, 29–31 May 2012. Funded by the Indo‐U.S. Science and Technology Forum, a joint initiative of Governments of India and the United States of America and the Moffitt Cancer Center, the overall goals of this workshop were to enhance the knowledge (agents, molecular targets, biomarkers, approaches, target populations, regulatory standards, priorities, resources) of a multinational, multidisciplinary team of researcher's to systematically accelerate the design, to conduct a successful clinical trials to evaluate botanicals/biologics for cancer chemoprevention and treatment, and to achieve efficient translation of these discoveries into the standards for clinical practice that will ultimately impact cancer morbidity and mortality. Expert panelists were drawn from a diverse group of stakeholders, representing the leadership from the National Cancer Institute's Office of Cancer Complementary and Alternative Medicine (OCCAM), NCI Experimental Therapeutics (NExT), Food and Drug Administration, national scientific leadership from India, and a distinguished group of population, basic and clinical scientists from the two countries, including leaders in bioinformatics, social sciences, and biostatisticians. At the end of the workshop, we established four Indo‐U.S. working research collaborative teams focused on identifying and prioritizing agents targeting four cancers that are of priority to both countries. Presented are some of the key proceedings and future goals discussed in the proceedings of this workshop. With the evolving evidence of the promise of botanicals/biologics for cancer chemoprevention and treatment, the proceedings of the Indo‐U.S. collaborative Workshop represent one of the most contemporary issues in Cancer Medicine .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96353/1/cam442.pd

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements

Proceedings of the Strategy Meeting for the Development of an International Consortium for Chinese Medicine and Cancer

On November 3, 2014, in Bethesda, MD, the Office of Cancer Complementary and Alternative Medicine of the National Cancer Institute held a meeting to examine the potential utility and feasibility of establishing an international consortium for Chinese medicine and cancer. There is significant interest in the West in using components of Chinese medicine (CM) —such as botanicals and herbal medicines, acupuncture and acupressure, and qigong—in the field of oncology, as potential anticancer agents, for symptom management, and to improve quality of life. The proposal for a consortium on CM came from the Chinese Academy of Chinese Medical Sciences, with the aims of improving scientific communications and collaborations and modernizing the studies of CM for cancer. The US National Cancer Institute’s Office of Cancer Complementary and Alternative Medicine agreed to work with Chinese Academy of Chinese Medical Sciences to explore the feasibility of establishing an international consortium for Chinese medicine and cancer. At the meeting, participants from the United States, China, Canada, Australia, and Korea discussed issues in CM and cancer research, treatment, and management, including potential mechanisms of action, proof of efficacy, adverse effects, regulatory issues, and the need for improving the quality of randomized clinical trials of CM treatments and supportive care interventions. Presented in these proceedings are some of the main issues and opportunities discussed by workshop participants

Discussions on the Path of Precise Poverty Alleviation in Dongxiang County

By means of questionnaire survey, household interview, conference exchange and field investigation, this paper makes a detailed investigation on the development status and existing problems of industry poverty alleviation and science and technology poverty alleviation in Dongxiang County. This paper puts forward the precise poverty alleviation path of Dongxiang County from the aspects of industry poverty alleviation, science and technology poverty alleviation, education poverty alleviation, poverty alleviation through ecology, relocation poverty alleviation and so on