Studying Complex Adaptive Systems

Complex adaptive systems (cas) – systems that involve many components that adapt or learn as they interact – are at the heart of important contemporary problems. The study of cas poses unique challenges: Some of our most powerful mathematical tools, particularly methods involving fixed points, attractors, and the like, are of limited help in understanding the development of cas. This paper suggests ways to modify research methods and tools, with an emphasis on the role of computer-based models, to increase our understanding of cas.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41486/1/11424_2006_Article_1.pd

Concerning the emergence of tag-mediated lookahead in classifier systems

This paper, after a general introduction to the area, discusses the architecture and learning algorithms that permit automatic parallel, distributed lookahead to emerge in classifier systems. Simple additions to a "standard" classifier system suffice, principally a new register called the virtual strength register, and a provision to use the bucket brigade credit assignment algorithm in "virtual" mode to modify values in this register. With these additions, current actions are decided on the basis of the expected values associated with the "lookahead cones" of possible alternatives.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28531/1/0000328.pd

Cycles in logical nets

This paper investigates the influence of cycles in a logical net upon the complexity of its behavior. The investigation is mainly concerned with two questions: 1. 1. A logical net with a periodic input sequence produces a periodic output sequence; how is the spectrum of periodic outputs related to the level of cycle complexity?2. 2. Is there a level of complexity c (suitably defined) such that any behavior possible for a fixed logical net can be realized by a logical net constructed only of cycles of complexity c' [les] c? The first and more difficult question is fully answered only in the case of nets constructed of cycles having a feedback coefficient r = 1 (suitably defined). The second question is answered in the negative for individual cycles and it is conjectured that a similar answer holds for nets in general.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32404/1/0000479.pd

The programming of Laplace's equation for solution by iteration on Whirlwind I

Thesis (B.S.) Massachusetts Institute of Technology. Dept. of Physics, 1950.MIT copy bound with: Calibration and alignment of a soft x-ray spectograph / E. Michael Gyorgy. 1950.Bibliography: leaf 37.by John H. Holland.B.S

Genetic Algorithms and Machine Learning

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46947/1/10994_2005_Article_422926.pd

Artificial economic life: a simple model of a stockmarket

We describe a model of a stockmarket in which independent adaptive agents can buy and sell stock on a central market. The overall market behavior, such as the stock price time series, is an emergent property of the agents' behavior. This approach to modelling a market is contrasted with conventional rational expectations approaches. Our model does not necessarily converge to an equilibrium, and can show bubbles, crashes, and continued high trading volume.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31402/1/0000319.pd

23 High Redshift Supernovae from the IfA Deep Survey: Doubling the SN Sample at z>0.7

We present photometric and spectroscopic observations of 23 high redshift supernovae spanning a range of z=0.34-1.03, 9 of which are unambiguously classified as Type Ia. These supernovae were discovered during the IfA Deep Survey, which began in September 2001 and observed a total of 2.5 square degrees to a depth of approximately m=25-26 in RIZ over 9-17 visits, typically every 1-3 weeks for nearly 5 months, with additional observations continuing until April 2002. We give a brief description of the survey motivations, observational strategy, and reduction process. This sample of 23 high-redshift supernovae includes 15 at z>0.7, doubling the published number of objects at these redshifts, and indicates that the evidence for acceleration of the universe is not due to a systematic effect proportional to redshift. In combination with the recent compilation of Tonry et al. (2003), we calculate cosmological parameter density contours which are consistent with the flat universe indicated by the CMB (Spergel et al. 2003). Adopting the constraint that Omega_total = 1.0, we obtain best-fit values of (Omega_m, Omega_Lambda)=(0.33, 0.67) using 22 SNe from this survey augmented by the literature compilation. We show that using the empty-beam model for gravitational lensing does not eliminate the need for Omega_Lambda > 0. Experience from this survey indicates great potential for similar large-scale surveys while also revealing the limitations of performing surveys for z>1 SNe from the ground.Comment: 67 pages, 12 figures, 12 tables, accepted for publication in the Astrophysical Journa

Twenty-Three High-Redshift Supernovae from the Institute for Astronomy Deep Survey: Doubling the Supernova Sample at z > 0.7

We present photometric and spectroscopic observations of 23 high-redshift supernovae (SNe) spanning a range of z = 0.34-1.03, nine of which are unambiguously classified as Type la. These SNe were discovered during the IfA Deep Survey, which began in 2001Partial support for this work was provided by NASA grants GO-08641 and GO-09118 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. Funding was also provided by NSF grant AST 02-06329. S. T. H. acknowledges support from the NASA LTSA grant NAG5-9364

Signal transducer and activator of transcription 1 (STAT1) gain-of-function mutations and disseminated coccidioidomycosis and histoplasmosis

Background: Impaired signaling in the IFN-g/IL-12 pathway causes susceptibility to severe disseminated infections with mycobacteria and dimorphic yeasts. Dominant gain-of-function mutations in signal transducer and activator of transcription 1 (STAT1) have been associated with chronic mucocutaneous candidiasis. Objective: We sought to identify the molecular defect in patients with disseminated dimorphic yeast infections. Methods: PBMCs, EBV-transformed B cells, and transfected U3A cell lines were studied for IFN-g/IL-12 pathway function. STAT1 was sequenced in probands and available relatives. Interferon-induced STAT1 phosphorylation, transcriptional responses, protein-protein interactions, target gene activation, and function were investigated. Results: We identified 5 patients with disseminated Coccidioides immitis or Histoplasma capsulatum with heterozygous missense mutations in the STAT1 coiled-coil or DNA-binding domains. These are dominant gain-of-function mutations causing enhanced STAT1 phosphorylation, delayed dephosphorylation, enhanced DNA binding and transactivation, and enhanced interaction with protein inhibitor of activated STAT1. The mutations caused enhanced IFN-g–induced gene expression, but we found impaired responses to IFN-g restimulation. Conclusion: Gain-of-function mutations in STAT1 predispose to invasive, severe, disseminated dimorphic yeast infections, likely through aberrant regulation of IFN-g–mediated inflammationFil: Sampaio, Elizabeth P.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Instituto Oswaldo Cruz. Laboratorio de Leprologia; BrasilFil: Hsu, Amy P.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Pechacek, Joseph. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Hannelore I.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Erasmus Medical Center. Department of Medical Microbiology and Infectious Disease; Países BajosFil: Dias, Dalton L.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Paulson, Michelle L.. Clinical Research Directorate/CMRP; Estados UnidosFil: Chandrasekaran, Prabha. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Rosen, Lindsey B.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Carvalho, Daniel S.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Instituto Oswaldo Cruz, Laboratorio de Leprologia; BrasilFil: Ding, Li. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Vinh, Donald C.. McGill University Health Centre. Division of Infectious Diseases; CanadáFil: Browne, Sarah K.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Datta, Shrimati. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Allergic Diseases. Allergic Inflammation Unit; Estados UnidosFil: Milner, Joshua D.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Allergic Diseases. Allergic Inflammation Unit; Estados UnidosFil: Kuhns, Douglas B.. Clinical Services Program; Estados UnidosFil: Long Priel, Debra A.. Clinical Services Program; Estados UnidosFil: Sadat, Mohammed A.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Host Defenses. Infectious Diseases Susceptibility Unit; Estados UnidosFil: Shiloh, Michael. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: De Marco, Brendan. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Alvares, Michael. University of Texas. Southwestern Medical Center. Division of Allergy and Immunology; Estados UnidosFil: Gillman, Jason W.. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Ramarathnam, Vivek. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: de la Morena, Maite. University of Texas. Southwestern Medical Center. Division of Allergy and Immunology; Estados UnidosFil: Bezrodnik, Liliana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutierrez"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Moreira, Ileana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutierrez"; ArgentinaFil: Uzel, Gulbu. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Johnson, Daniel. University of Chicago. Comer Children; Estados UnidosFil: Spalding, Christine. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Zerbe, Christa S.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Wiley, Henry. National Eye Institute. Clinical Trials Branch; Estados UnidosFil: Greenberg, David E.. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Hoover, Susan E.. University of Arizona. College of Medicine. Valley Fever Center for Excellence; Estados UnidosFil: Rosenzweig, Sergio D.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Host Defenses Infectious Diseases Susceptibility Unit; Estados Unidos. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Primary Immunodeficiency Clinic; Estados UnidosFil: Galgiani, John N.. University of Arizona. College of Medicine. Valley Fever Center for Excellence; Estados UnidosFil: Holland, Steven M.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unido

Diagnosis Across the Spectrum of Progressive Supranuclear Palsy and Corticobasal Syndrome

IMPORTANCE: Patients with atypical parkinsonian syndromes (APS), including progressive supranuclear palsy (PSP), corticobasal syndrome (CBS) and multiple system atrophy (MSA), may be difficult to distinguish in early stages and are often misdiagnosed as Parkinson’s disease (PD). The diagnostic criteria for PSP have been updated to encompass a range of clinical subtypes, but have not been prospectively studied. OBJECTIVE: To define the distinguishing features of PSP and CBS, and to assess their usefulness in facilitating early diagnosis and separation from PD. DESIGN, SETTING, PARTICIPANTS: Cohort study which recruited APS and PD patients from movement disorder clinics across the UK from September 2015 to December 2018, and will follow up patients over 5 years. APS patients were stratified into PSP-Richardson syndrome, PSP-subcortical (including PSP-parkinsonism and PSP-progressive gait freezing cases), PSP-cortical (including PSP-frontal and PSP/CBS overlap cases), MSA-parkinsonism, MSA-cerebellar, CBS-Alzheimer’s and CBS-non-Alzheimer’s groups. MAIN OUTCOME MEASURES: Baseline group comparisons were conducted using: 1) Clinical trajectory; 2) Cognitive screening scales; 3) Serum neurofilament light chain (NF-L); 4) TRIM11, ApoE and MAPT genotypes; 5) Volumetric MRI. RESULTS: 222 APS cases (101 PSP, 55 MSA, 40 CBS and 26 indeterminate) were recruited (58% male; mean age at recruitment, 68.3 years). Age-matched controls (n=76) and PD cases (n=1967) were also included. Concordance between the ante-mortem clinical diagnosis and pathological diagnosis was achieved in 12/13 (92%) of PSP and CBS cases coming to post-mortem. Applying the MDS PSP diagnostic criteria almost doubled the number of patients diagnosed with PSP. 49/101 (49%) of reclassified PSP patients did not have classical PSP-Richardson syndrome. PSP-subcortical patients had a longer diagnostic latency and a more benign clinical trajectory than PSP-Richardson syndrome and PSP-cortical (p<0.05). PSP-subcortical was distinguished from PSP-cortical and PSP-Richardson syndrome by cortical volumetric MRI measures (AUC 0.84-0.89), cognitive profile (AUC 0.80-0.83), serum NF-L (AUC 0.75-0.83) and TRIM11 rs564309 genotype. Midbrain atrophy was a common feature of all PSP subtypes. 8/17 (47%) of CBS patients with CSF analysis were identified as having CBS-Alzheimer’s. CBS-Alzheimer’s patients had a longer diagnostic latency, relatively benign clinical trajectory, greater cognitive impairment and higher APOE-ε4 allele frequency than CBS-non-Alzheimer’s (p<0.05, AUC 0.80-0.87). Serum NF-L levels distinguished PD from PSP and CBS (p<0.05, AUC 0.80). CONCLUSIONS AND RELEVANCE: Clinical, therapeutic and epidemiological studies focusing on PSP-Richardson syndrome are likely to miss a large number of patients with underlying PSP-tau pathology. CSF analysis defines a distinct CBS-Alzheimer’s subgroup. PSP and CBS subtypes have distinct characteristics that may enhance their early diagnosis