Finite Theories and the SUSY Flavor Problem

We study a finite SU(5) grand unified model based on the non-Abelian discrete symmetry A_4. This model leads to the democratic structure of the mass matrices for the quarks and leptons. In the soft supersymmetry breaking sector, the scalar trilinear couplings are aligned and the soft scalar masses are degenerate, thus solving the SUSY flavor problem.Comment: 17 pages, LaTeX, 1 figur

Report of Third Annual Seminar on Estate Planning

Reports from the UK/CLE Third Annual Seminar on Estate Planning held July 23-24, 1976

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Cross-sectional study of availability and pharmaceutical quality of antibiotics requested with or without prescription (Over The Counter) in Surabaya, Indonesia

Contains fulltext : 87881.pdf (publisher's version ) (Open Access

Development of Epitope-Blocking ELISA for Universal Detection of Antibodies to Human H5N1 Influenza Viruses

10.1371/journal.pone.0004566PLoS ONE4

Constrained SUSY seesaws with a 125 GeV Higgs

Motivated by the ATLAS and CMS discovery of a Higgs-like boson with a mass around 125 GeV, and by the need of explaining neutrino masses, we analyse the three canonical SUSY versions of the seesaw mechanism (type I, II and III) with CMSSM boundary conditions. In type II and III cases, SUSY particles are lighter than in the CMSSM (or the constrained type I seesaw), for the same set of input parameters at the universality scale. Thus, to explain $m_{h^0} \simeq 125 GeV$ at low energies, one is forced into regions of parameter space with very large values of $m_0$, $M_{1/2}$ or $A_0$. We compare the squark and gluino masses allowed by the ATLAS and CMS ranges for $m_{h^0}$ (extracted from the 2011-2012 data), and discuss the possibility of distinguishing seesaw models in view of future results on SUSY searches. In particular, we briefly comment on the discovery potential of LHC upgrades, for squark/gluino mass ranges required by present Higgs mass constraints. A discrimination between different seesaw models cannot rely on the Higgs mass data alone, therefore we also take into account the MEG upper limit on BR$(\mu \to e \gamma)$ and show that, in some cases, this may help to restrict the SUSY parameter space, as well as to set complementary limits on the seesaw scale.Comment: 28 pages, 7 figures. v2: comments and references added. Final version to appear in JHE

Prognostic factors in prostate cancer

Prognostic factors in organ confined prostate cancer will reflect survival after surgical radical prostatectomy. Gleason score, tumour volume, surgical margins and Ki-67 index have the most significant prognosticators. Also the origins from the transitional zone, p53 status in cancer tissue, stage, and aneuploidy have shown prognostic significance. Progression-associated features include Gleason score, stage, and capsular invasion, but PSA is also highly significant. Progression can also be predicted with biological markers (E-cadherin, microvessel density, and aneuploidy) with high level of significance. Other prognostic features of clinical or PSA-associated progression include age, IGF-1, p27, and Ki-67. In patients who were treated with radiotherapy the survival was potentially predictable with age, race and p53, but available research on other markers is limited. The most significant published survival-associated prognosticators of prostate cancer with extension outside prostate are microvessel density and total blood PSA. However, survival can potentially be predicted by other markers like androgen receptor, and Ki-67-positive cell fraction. In advanced prostate cancer nuclear morphometry and Gleason score are the most highly significant progression-associated prognosticators. In conclusion, Gleason score, capsular invasion, blood PSA, stage, and aneuploidy are the best markers of progression in organ confined disease. Other biological markers are less important. In advanced disease Gleason score and nuclear morphometry can be used as predictors of progression. Compound prognostic factors based on combinations of single prognosticators, or on gene expression profiles (tested by DNA arrays) are promising, but clinically relevant data is still lacking

Rare coding variants in ten genes confer substantial risk for schizophrenia

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3-50, PPeer reviewe

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]