Integration of the End Cap TEC+ of the CMS Silicon Strip Tracker

The silicon strip tracker of the CMS experiment has been completed and inserted into the CMS detector in late 2007. The largest sub-system of the tracker is its end cap system, comprising two large end caps (TEC) each containing 3200 silicon strip modules. To ease construction, the end caps feature a modular design: groups of about 20 silicon modules are placed on sub-assemblies called petals and these self-contained elements are then mounted into the TEC support structures. Each end cap consists of 144 petals, and the insertion of these petals into the end cap structure is referred to as TEC integration. The two end caps were integrated independently in Aachen (TEC+) and at CERN (TEC--). This note deals with the integration of TEC+, describing procedures for end cap integration and for quality control during testing of integrated sections of the end cap and presenting results from the testing

Reception Test of Petals for the End Cap TEC+ of the CMS Silicon Strip Tracker

The silicon strip tracker of the CMS experiment has been completed and was inserted into the CMS detector in late 2007. The largest sub system of the tracker are its end caps, comprising two large end caps (TEC) each containing 3200 silicon strip modules. To ease construction, the end caps feature a modular design: groups of about 20 silicon modules are placed on sub-assemblies called petals and these self-contained elements are then mounted onto the TEC support structures. Each end cap consists of 144 such petals, which were built and fully qualified by several institutes across Europe. Fro

A biomathematical model of immune response and barrier function in mice with pneumococcal lung infection

Pneumonia is one of the leading causes of death worldwide. The course of the disease is often highly dynamic with unforeseen critical deterioration within hours in a relevant proportion of patients. Besides antibiotic treatment, novel adjunctive therapies are under development. Their additive value needs to be explored in preclinical and clinical studies and corresponding therapy schedules require optimization prior to introduction into clinical practice. Biomathematical modeling of the underlying disease and therapy processes might be a useful aid to support these processes. We here propose a biomathematical model of murine immune response during infection with Streptococcus pneumoniae aiming at predicting the outcome of different treatment schedules. The model consists of a number of non-linear ordinary differential equations describing the dynamics and interactions of the pulmonal pneumococcal population and relevant cells of the innate immune response, namely alveolar- and inflammatory macrophages and neutrophils. The cytokines IL-6 and IL-10 and the chemokines CCL2, CXCL1 and CXCL5 are considered as major mediators of the immune response. We also model the invasion of peripheral blood monocytes, their differentiation into macrophages and bacterial penetration through the epithelial barrier causing blood stream infections. We impose therapy effects on this system by modelling antibiotic therapy and treatment with the novel C5a-inactivator NOX-D19. All equations are derived by translating known biological mechanisms into equations and assuming appropriate response kinetics. Unknown model parameters were determined by fitting the predictions of the model to time series data derived from mice experiments with close-meshed time series of state parameters. Parameter fittings resulted in a good agreement of model and data for the experimental scenarios. The model can be used to predict the performance of alternative schedules of combined antibiotic and NOX-D19 treatment. We conclude that we established a comprehensive biomathematical model of pneumococcal lung infection, immune response and barrier function in mice allowing simulations of new treatment schedules. We aim to validate the model on the basis of further experimental data. We also plan the inclusion of further novel therapy principles and the translation of the model to the human situation in the near future

A biomathematical model of immune response and barrier function in mice with pneumococcal lung infection.

Pneumonia is one of the leading causes of death worldwide. The course of the disease is often highly dynamic with unforeseen critical deterioration within hours in a relevant proportion of patients. Besides antibiotic treatment, novel adjunctive therapies are under development. Their additive value needs to be explored in preclinical and clinical studies and corresponding therapy schedules require optimization prior to introduction into clinical practice. Biomathematical modeling of the underlying disease and therapy processes might be a useful aid to support these processes. We here propose a biomathematical model of murine immune response during infection with Streptococcus pneumoniae aiming at predicting the outcome of different treatment schedules. The model consists of a number of non-linear ordinary differential equations describing the dynamics and interactions of the pulmonal pneumococcal population and relevant cells of the innate immune response, namely alveolar- and inflammatory macrophages and neutrophils. The cytokines IL-6 and IL-10 and the chemokines CCL2, CXCL1 and CXCL5 are considered as major mediators of the immune response. We also model the invasion of peripheral blood monocytes, their differentiation into macrophages and bacterial penetration through the epithelial barrier causing blood stream infections. We impose therapy effects on this system by modelling antibiotic therapy and treatment with the novel C5a-inactivator NOX-D19. All equations are derived by translating known biological mechanisms into equations and assuming appropriate response kinetics. Unknown model parameters were determined by fitting the predictions of the model to time series data derived from mice experiments with close-meshed time series of state parameters. Parameter fittings resulted in a good agreement of model and data for the experimental scenarios. The model can be used to predict the performance of alternative schedules of combined antibiotic and NOX-D19 treatment. We conclude that we established a comprehensive biomathematical model of pneumococcal lung infection, immune response and barrier function in mice allowing simulations of new treatment schedules. We aim to validate the model on the basis of further experimental data. We also plan the inclusion of further novel therapy principles and the translation of the model to the human situation in the near future

Petal Integration for the CMS Tracker End Caps

This note describes the assembly and testing of the 292 petals built for the CMS Tracker End Caps from the beginning of 2005 until the summer of 2006. Due to the large number of petals to be assembled and the need to reach a throughput of 10 to 15 petals per week, a distributed integration approach was chosen. This integration was carried out by the following institutes: I. and III. Physikalisches Institut - RWTH Aachen University; IIHE, ULB \& VUB Universities, Brussels; Hamburg University; IEKP, Karlsruhe University; FYNU, Louvain University; IPN, Lyon University; and IPHC, Strasbourg University. Despite the large number of petals which needed to be reworked to cope with a late-discovered module issue, the quality of the petals is excellent with less than 0.2\% bad channels

Importance of Baseline Prognostic Factors With Increasing Time Since Initiation of Highly Active Antiretroviral Therapy: Collaborative Analysis of Cohorts of HIV-1-Infected Patients

Background: The extent to which the prognosis for AIDS and death of patients initiating highly active antiretroviral therapy (HAART) continues to be affected by their characteristics at the time of initiation (baseline) is unclear. Methods: We analyzed data on 20,379 treatment-naive HIV-1- infected adults who started HAART in 1 of 12 cohort studies in Europe and North America (61,798 person-years of follow-up, 1844 AIDS events, and 1005 deaths). Results: Although baseline CD4 cell count became less prognostic with time, individuals with a baseline CD4 count 350 cells/μL (hazard ratio for AIDS = 2.3, 95% confidence interval [CI]: 1.0 to 2.3; mortality hazard ratio = 2.5, 95% CI: 1.2 to 5.5, 4 to 6 years after starting HAART). Rates of AIDS were persistently higher in individuals who had experienced an AIDS event before starting HAART. Individuals with presumed transmission by means of injection drug use experienced substantially higher rates of AIDS and death than other individuals throughout follow-up (AIDS hazard ratio = 1.6, 95% CI: 0.8 to 3.0; mortality hazard ratio = 3.5, 95% CI: 2.2 to 5.5, 4 to 6 years after starting HAART). Conclusions: Compared with other patient groups, injection drug users and patients with advanced immunodeficiency at baseline experience substantially increased rates of AIDS and death up to 6 years after starting HAART