LEADER 01732oam 2200493 a 450 001 9910696955303321 005 20230902162042.0 035 $a(CKB)5470000002383338 035 $a(OCoLC)491496326 035 $a(EXLCZ)995470000002383338 100 $a20091221d2009 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBiosurveillance$b[electronic resource] $edeveloping a collaboration strategy is essential to fostering interagency data and resource sharing : report to congressional committees 210 1$a[Washington, D.C.] :$cU.S. Govt. Accountability Office,$d[2009] 215 $a1 online resource (ii, 33 pages) $cillustrations 300 $aTitle from PDF title screen (GAO, viewed Dec. 21, 2009). 300 $a"December 2009." 300 $a"GAO-10-171." 320 $aIncludes bibliographical references. 517 $aBiosurveillance 606 $aPublic health surveillance$zUnited States 606 $aInteragency coordination$zUnited States 606 $aEmergency management$zUnited States 606 $aBioterrorism$zUnited States$xPrevention 606 $aCommunicable diseases$zUnited States$xPrevention 606 $aNational security$zUnited States 615 0$aPublic health surveillance 615 0$aInteragency coordination 615 0$aEmergency management 615 0$aBioterrorism$xPrevention. 615 0$aCommunicable diseases$xPrevention. 615 0$aNational security 801 0$bDID 801 1$bDID 801 2$bDID 801 2$bGPO 906 $aBOOK 912 $a9910696955303321 996 $aBiosurveillance$93454680 997 $aUNINA LEADER 05023nam 22008055 450 001 9910299986903321 005 20251230065039.0 010 $a1-4939-1793-5 024 7 $a10.1007/978-1-4939-1793-8 035 $a(CKB)3710000000277373 035 $a(EBL)1966851 035 $a(SSID)ssj0001386599 035 $a(PQKBManifestationID)11766804 035 $a(PQKBTitleCode)TC0001386599 035 $a(PQKBWorkID)11374962 035 $a(PQKB)11121042 035 $a(MiAaPQ)EBC1966851 035 $a(DE-He213)978-1-4939-1793-8 035 $a(PPN)183092473 035 $a(EXLCZ)993710000000277373 100 $a20141106d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMathematical Models of Tumor-Immune System Dynamics /$fedited by Amina Eladdadi, Peter Kim, Dann Mallet 205 $a1st ed. 2014. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2014. 215 $a1 online resource (282 p.) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v107 300 $aDescription based upon print version of record. 311 08$a1-4939-1792-7 320 $aIncludes bibliographical references. 327 $aIncorporating Asymmetric Stem Cell Division into the Roeder Model for Chronic Myeloid Leukemia -- A Cellular Automata and a Partial Differential Equation Model of Tumor-Immune Dynamics and Chemotaxis -- A Structured Population Model of Competition between Cancer Cells and T Cells under Immunotherapy -- Modeling Tumor-Immune Dynamics -- The Mathematics of Drug Delivery -- The Role of the miR-451-AMPK Signaling Pathway in Regulation of Cell Migration and Proliferation in Glioblastoma -- An Optimal Control Approach to Cancer Chemotherapy with Tumor-Immune System Interactions -- Negative feedback regulation in hierarchically organized tissues: Exploring the dynamics of tissue regeneration and the role of feedback escape in tumor development -- A Cellular Automata Model to Investigate Immune Cell-Tumor Cell Interactions in Growing Tumors in Two Spatial Dimensions -- Differential Equation Techniques for Modeling a Cycle-Specific Oncolytic Virotherapeutic. 330 $aThis collection of papers offers a broad synopsis of state-of-the-art mathematical methods used in modeling the interaction between tumors and the immune system. These papers were presented at the four-day workshop on Mathematical Models of Tumor-Immune System Dynamics held in Sydney, Australia from January 7th to January 10th, 2013. The workshop brought together applied mathematicians, biologists, and clinicians actively working in the field of cancer immunology to share their current research and to increase awareness of the innovative mathematical tools that are applicable to the growing field of cancer immunology. Recent progress in cancer immunology and advances in immunotherapy suggest that the immune system plays a fundamental role in host defense against tumors and could be utilized to prevent or cure cancer. Although theoretical and experimental studies of tumor-immune system dynamics have a long history, there are still many unanswered questions about the mechanisms that govern the interaction between the immune system and a growing tumor. The multidimensional nature of these complex interactions requires a cross-disciplinary approach to capture more realistic dynamics of the essential biology. The papers presented in this volume explore these issues and the results will be of interest to graduate students and researchers in  a variety of fields within mathematical and biological sciences. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v107 606 $aMathematical models 606 $aCancer 606 $aDynamical systems 606 $aMathematical physics 606 $aMathematical Modeling and Industrial Mathematics 606 $aCancer Biology 606 $aDynamical Systems 606 $aMathematical Physics 615 0$aMathematical models. 615 0$aCancer. 615 0$aDynamical systems. 615 0$aMathematical physics. 615 14$aMathematical Modeling and Industrial Mathematics. 615 24$aCancer Biology. 615 24$aDynamical Systems. 615 24$aMathematical Physics. 676 $a003.3 676 $a510 676 $a515.39 676 $a515.48 676 $a519 676 $a614.5999 702 $aEladdadi$b Amina$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKim$b Peter$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMallet$b Dann$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aUS-Sydney International Workshop on Mathematical Modeling of Tumor-Immune Dynamics 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299986903321 996 $aMathematical models of tumor-immune system dynamics$91410679 997 $aUNINA