Bimaximal Mixing in an SO(10) Minimal Higgs Model

An SO(10) SUSY GUT model was previously presented based on a minimal set of Higgs fields. The quark and lepton mass matrices derived fitted the data extremely well and led to large mixing of muon- and tau-neutrinos in agreement with the atmospheric neutrino data and to the small-angle MSW solution for the solar neutrinos. Here we show how a slight modification leading to a non-zero up quark mass can result in bimaximal mixing for the atmospheric and solar neutrinos. The "just-so" vacuum solution is slightly favored over the large-angle MSW solution on the basis of the hierarchy required for the right-handed Majorana matrix and the more nearly-maximal mixing angles obtained.Comment: 10 pages, LaTeX, several references adde

Higher-dimensional operators in SUSY SO(10) GUT models

SO(10) GUT models with only small Higgs fields use higher-dimensional operators to generate realistic fermion mass matrices. In particular, a Higgs field in the spinor representation, 16^d_H, acquires a weak scale vev. We include the weak vev of the corresponding field \bar{16}^u_H and investigate the effect on two successful models, one by Albright and Barr (AB) and another by Babu, Pati and Wilczek (BPW). We find that the BPW model is a particular case within a class of models with identical fermion masses and mixings. In contrast, we expect corrections to the parameters of AB-type models.Comment: 3 page

Forecasting the Progression of Alzheimer's Disease Using Neural Networks and a Novel Pre-Processing Algorithm

Alzheimer's disease (AD) is the most common neurodegenerative disease in older people. Despite considerable efforts to find a cure for AD, there is a 99.6% failure rate of clinical trials for AD drugs, likely because AD patients cannot easily be identified at early stages. This project investigated machine learning approaches to predict the clinical state of patients in future years to benefit AD research. Clinical data from 1737 patients was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and was processed using the "All-Pairs" technique, a novel methodology created for this project involving the comparison of all possible pairs of temporal data points for each patient. This data was then used to train various machine learning models. Models were evaluated using 7-fold cross-validation on the training dataset and confirmed using data from a separate testing dataset (110 patients). A neural network model was effective (mAUC = 0.866) at predicting the progression of AD on a month-by-month basis, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.Comment: 10 pages; updated acknowledgement

A Bibliography of Community Analyses for Libraries

published or submitted for publicatio

SO(10) GUT Models and Their Present Success in Explaining Mass and Mixing Data

Some features of SO(10) GUT models are reviewed, and a number of such models in the literature are compared. While some have been eliminated by recent neutrino data, others are presently successful in explaining the quark and lepton mass and mixing data. A short description of one very predictive model is given which illustrates some of the features discussed. Future tests of the models are pointed out including one which contrasts sharply with those models based on an $L_e - L_{\mu} - L_{\tau}$ type symmetry.Comment: 9 pages, paper presented at the Neutrinos and Implications for Physics Beyond the Standard Model Conference, SUNY at Stony Brook, October 11-13, 200

Device separates hydrogen from solution in water at ambient temperatures

Separator decreases the partial pressure of hydrogen gas dissolved in the water produced by fuel cells containing an alkaline electrolyte. The unit eliminates the hazards associated with the release of hydrogen from water solution when the hydrostatic pressure is rapidly decreased