HIGH-TECH INDUSTRIALIZATION IN CHINA: AN ANALYSIS OF THE CURRENT STATUS
In: Asian survey: a bimonthly review of contemporary Asian affairs, Band 32, Heft 12, S. 1124-1136
ISSN: 0004-4687
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In: Asian survey: a bimonthly review of contemporary Asian affairs, Band 32, Heft 12, S. 1124-1136
ISSN: 0004-4687
Behavioural modelling of physical systems from observations of their input/output behaviour is an important task in engineering. Such models are needed for fault monitoring as well as intelligent control of these systems. The paper addresses one subtask of behavioural modelling, namely the selection of input variables to be used in predicting the behaviour of an output variable. A technique that is well suited for qualitative behavioural modelling and simulation of physical systems is Fuzzy Inductive Reasoning (FIR), a methodology based on General System Theory. Yet, the FIR modelling methodology is of exponential computational complexity, and therefore, it may be useful to consider other approaches as booster techniques for FIR. Different variable selection algorithms: the method of the unreconstructed variance for the best reconstruction, methods based on regression coefficients (OLS, PCR and PLS) and other methods as Multiple Correlation Coefficients (MCC), Principal Components Analysis (PCA) and Cluster analysis are discussed and compared to each other for use in predicting the behaviour of a steam generator. The different variable selection algorithms previously named are then used as booster techniques for FIR. Some of the used linear techniques have been found to be non-effective in the task of selecting variables in order to compute a posterior FIR model. Methods based on clustering seem particularly well suited for pre-selecting subsets of variables to be used in a FIR modelling and simulation effort. ; The research reported in this article was made possible, thanks to a Ph.D. fellowship of the Ministry for Education and Culture from the Spanish Government funded within the frame of the TAP96-0882 project. ; Peer Reviewed
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In: Reproductive sciences: RS : the official journal of the Society for Reproductive Investigation, Band 19, Heft 12, S. 1302-1314
ISSN: 1933-7205
Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.
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In: Fox , BA , Schendel , D J , Butterfield , L H , Aamdal , S , Allison , J P , Ascierto , P A , Atkins , M B , Bartunkova , J , Bergmann , L , Berinstein , N , Bonorino , C C , Borden , E , Bramson , J L , Britten , C M , Cao , X , Carson , W E , Chang , A E , Characiejus , D , Choudhury , A R , Coukos , G , de Gruijl , T D , Dillman , R O , Dolstra , H , Dranoff , G , Durrant , L G , Finke , J H , Galon , J , Gollob , J A , Gouttefangeas , C , Grizzi , F , Guida , M , Hakansson , L , Hege , K , Herberman , R B , Hodi , F S , Hoos , A , Huber , C , Hwu , P , Imai , K , Jaffee , E M , Janetzki , S , June , C H , Kalinski , P , Kaufmann , H L , Kawakami , K , Kawakami , Y , Keilholtz , U , Khleif , S N , Kiessling , R , Kotlan , B , Kroemer , G , Lapointe , R , Levitsky , H I , Lotze , M T , Di Maio , M , Marschner , J P , Mastrangelo , M J , Masucci , G , Melero , I , Nelief , C , Murphy , W J , Nelson , B , Nicolini , A , Nishimura , M I , Odunsi , K , Ohashi , P S , O'Donnell-Tormey , J , Old , L J , Ottensmeier , C , Papamichail , M , Parmiani , G , Pawelec , G , Proietti , E , Qin , S , Rees , R , Ribas , A , Ridolfi , R , Ritter , G , Rivoltini , L , Romero , P J , Salem , M L , Scheper , R J , Seliger , B , Sharma , P , Shiku , H , Singh-Jasuja , H , Song , W , Straten , P T , Tahara , H , Tian , Z , van der Burg , S H , von Hoegen , P , Wang , E , Welters , M J , Winter , H , Withington , T , Wolchok , J D , Xiao , W , Zitvogel , L , Zwierzina , H , Marincola , F M , Gajewski , T F , Wigginton , J M & Disis , M L A 2011 , ' Defining the Critical Hurdles in Cancer Immunotherapy ' , Journal of Translational Medicine , vol. 9 , no. 1 , 214 . https://doi.org/10.1186/1479-5876-9-214
Scientific discoveries that provide strong evidence of antitumor effects in preclinical models often encounter significant delays before being tested in patients with cancer. While some of these delays have a scientific basis, others do not. We need to do better. Innovative strategies need to move into early stage clinical trials as quickly as it is safe, and if successful, these therapies should efficiently obtain regulatory approval and widespread clinical application. In late 2009 and 2010 the Society for Immunotherapy of Cancer (SITC), convened an "Immunotherapy Summit" with representatives from immunotherapy organizations representing Europe, Japan, China and North America to discuss collaborations to improve development and delivery of cancer immunotherapy. One of the concepts raised by SITC and defined as critical by all parties was the need to identify hurdles that impede effective translation of cancer immunotherapy. With consensus on these hurdles, international working groups could be developed to make recommendations vetted by the participating organizations. These recommendations could then be considered by regulatory bodies, governmental and private funding agencies, pharmaceutical companies and academic institutions to facilitate changes necessary to accelerate clinical translation of novel immune-based cancer therapies. The critical hurdles identified by representatives of the collaborating organizations, now organized as the World Immunotherapy Council, are presented and discussed in this report. Some of the identified hurdles impede all investigators; others hinder investigators only in certain regions or institutions or are more relevant to specific types of immunotherapy or first-in-humans studies. Each of these hurdles can significantly delay clinical translation of promising advances in immunotherapy yet if overcome, have the potential to improve outcomes of patients with cancer. © 2011 Fox et al; licensee BioMed Central Ltd.
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