Search for the Higgs Boson Using Neural Networks in Events with Missing Energy and b-Quark Jets in pp̅ Collisions at √s=1.96 TeV

We report on a search for the standard model Higgs boson produced in association with a $W$ or $Z$ boson in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV recorded by the CDF II experiment at the Tevatron in a data sample corresponding to an integrated luminosity of 2.1 fb$^{-1}$. We consider events which have no identified charged leptons, an imbalance in transverse momentum, and two or three jets where at least one jet is consistent with originating from the decay of a $b$ hadron. We find good agreement between data and predictions. We place 95% confidence level upper limits on the production cross section for several Higgs boson masses ranging from 110$\gevm$ to 150$\gevm$. For a mass of 115$\gevm$ the observed (expected) limit is 6.9 (5.6) times the standard model prediction.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Cow's milk allergy and the development of tolerance

Aims: To gain insight on the immunological processes behind cow’s milk allergy (CMA) and the development of oral tolerance. To furthermore investigate the associations of HLA II and filaggrin genotypes with humoral responses to early oral antigens. Methods: The study population was from a cohort of 6209 healthy, full-term infants who in a double-blind randomized trial received supplementary feeding at maternity hospitals (mean duration 4 days): cow’s milk (CM) formula, extensively hydrolyzed whey formula or donor breast milk. Infants who developed CM associated symptoms that subsided during elimination diet (n=223) underwent an open oral CM challenge (at mean age 7 months). The challenge was negative in 112, and in 111 it confirmed CMA, which was IgE-mediated in 83. Patients with CMA were followed until recovery, and 94 of them participated in a follow-up study at age 8-9 years. We investigated serum samples at diagnosis (mean age 7 months, n=111), one year later (19 months, n=101) and at follow-up (8.6 years, n=85). At follow-up, also 76 children randomly selected from the original cohort and without CM associated symptoms were included. We measured CM specific IgE levels with UniCAP (Phadia, Uppsala, Sweden), and β-lactoglobulin, α-casein and ovalbumin specific IgA, IgG1, IgG4 and IgG levels with enzyme-linked immunosorbent assay in sera. We applied a microarray based immunoassay to measure the binding of IgE, IgG4 and IgA serum antibodies to sequential epitopes derived from five major CM proteins at the three time points in 11 patients with active IgE-mediated CMA at age 8-9 years and in 12 patients who had recovered from IgE-mediated CMA by age 3 years. We used bioinformatic methods to analyze the microarray data. We studied T cell expression profile in peripheral blood mononuclear cell (PBMC) samples from 57 children aged 5-12 years (median 8.3): 16 with active CMA, 20 who had recovered from CMA by age 3 years, 21 non-atopic control subjects. Following in vitro β-lactoglobulin stimulation, we measured the mRNA expression in PBMCs of 12 T-cell markers (T-bet, GATA-3, IFN-γ, CTLA4, IL-10, IL-16, TGF-β, FOXP3, Nfat-C2, TIM3, TIM4, STIM-1) with quantitative real time polymerase chain reaction, and the protein expression of CD4, CD25, CD127, FoxP3 with flow cytometry. To optimally distinguish the three study groups, we performed artificial neural networks with exhaustive search for all marker combinations. For genetic associations with specific humoral responses, we analyzed 14 HLA class II haplotypes, the PTPN22 1858 SNP (R620W allele) and 5 known filaggrin null mutations from blood samples of 87 patients with CMA and 76 control subjects (age 8.0-9.3 years). Results: High IgG and IgG4 levels to β-lactoglobulin and α-casein were associated with the HLA (DR15)-DQB1*0602 haplotype in patients with CMA, but not in control subjects. Conversely, (DR1/10)-DQB1*0501 was associated with lower IgG and IgG4 levels to these CM antigens, and to ovalbumin, most significantly among control subjects. Infants with IgE-mediated CMA had lower β -lactoglobulin and α-casein specific IgG1, IgG4 and IgG levels (p<0.05) at diagnosis than infants with non-IgE-mediated CMA or control subjects. When CMA persisted beyond age 8 years, CM specific IgE levels were higher at all three time points investigated and IgE epitope binding pattern remained stable (p<0.001) compared with recovery from CMA by age 3 years. Patients with persisting CMA at 8-9 years had lower serum IgA levels to β-lactoglobulin at diagnosis (p=0.01), and lower IgG4 levels to β-lactoglobulin (p=0.04) and α-casein (p=0.05) at follow-up compared with patients who recovered by age 3 years. In early recovery, signal of IgG4 epitope binding increased while that of IgE decreased over time, and binding patterns of IgE and IgG4 overlapped. In T cell expression profile in response to β –lactoglobulin, the combination of markers FoxP3, Nfat-C2, IL-16, GATA-3 distinguished patients with persisting CMA most accurately from patients who had become tolerant and from non-atopic subjects. FoxP3 expression at both RNA and protein level was higher in children with CMA compared with non-atopic children. Conclusions: Genetic factors (the HLA II genotype) are associated with humoral responses to early food allergens. High CM specific IgE levels predict persistence of CMA. Development of tolerance is associated with higher specific IgA and IgG4 levels and lower specific IgE levels, with decreased CM epitope binding by IgE and concurrent increase in corresponding epitope binding by IgG4. Both Th2 and Treg pathways are activated upon CM antigen stimulation in patients with CMA. In the clinical management of CMA, HLA II or filaggrin genotyping are not applicable, whereas the measurement of CM specific antibodies may assist in estimating the prognosis.

Geant4 Hadronic Cascade Models and CMS Data Analysis : Computational Challenges in the LHC era

This work belongs to the field of computational high-energy physics (HEP). The key methods used in this thesis work to meet the challenges raised by the Large Hadron Collider (LHC) era experiments are object-orientation with software engineering, Monte Carlo simulation, the computer technology of clusters, and artificial neural networks. The first aspect discussed is the development of hadronic cascade models, used for the accurate simulation of medium-energy hadron-nucleus reactions, up to 10 GeV. These models are typically needed in hadronic calorimeter studies and in the estimation of radiation backgrounds. Various applications outside HEP include the medical field (such as hadron treatment simulations), space science (satellite shielding), and nuclear physics (spallation studies). Validation results are presented for several significant improvements released in Geant4 simulation tool, and the significance of the new models for computing in the Large Hadron Collider era is estimated. In particular, we estimate the ability of the Bertini cascade to simulate Compact Muon Solenoid (CMS) hadron calorimeter HCAL. LHC test beam activity has a tightly coupled cycle of simulation-to-data analysis. Typically, a Geant4 computer experiment is used to understand test beam measurements. Thus an another aspect of this thesis is a description of studies related to developing new CMS H2 test beam data analysis tools and performing data analysis on the basis of CMS Monte Carlo events. These events have been simulated in detail using Geant4 physics models, full CMS detector description, and event reconstruction. Using the ROOT data analysis framework we have developed an offline ANN-based approach to tag b-jets associated with heavy neutral Higgs particles, and we show that this kind of NN methodology can be successfully used to separate the Higgs signal from the background in the CMS experiment.