Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Search for time-dependent B0s - B0s-bar oscillations using a vertex charge dipole technique

We report a search for B0s - B0s-bar oscillations using a sample of 400,000 hadronic Z0 decays collected by the SLD experiment. The analysis takes advantage of the electron beam polarization as well as information from the hemisphere opposite that of the reconstructed B decay to tag the B production flavor. The excellent resolution provided by the pixel CCD vertex detector is exploited to cleanly reconstruct both B and cascade D decay vertices, and tag the B decay flavor from the charge difference between them. We exclude the following values of the B0s - B0s-bar oscillation frequency: Delta m_s < 4.9 ps-1 and 7.9 < Delta m_s < 10.3 ps-1 at the 95% confidence level.Comment: 18 pages, 3 figures, replaced by version accepted for publication in Phys.Rev.D; results differ slightly from first versio

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs

Measurement of J/psi production in continuum e(+)e(-) annihilations near square root of s = 10.6 GeV.

The production of J/psi mesons in continuum e(+)e(-) annihilations has been studied with the BABAR detector at energies near the Upsilon(4S) resonance. The mesons are distinguished from J/psi production in B decays through their center-of-mass momentum and energy. We measure the cross section e(+)e(-)-->J/psi X to be 2.52+/-0.21+/-0.21 pb. We set a 90% C.L. upper limit on the branching fraction for direct Upsilon(4S)-->J/psi X decays at 4.7 x 10(-4)

Measurement of the branching fractions for ψ(2s)→e+e- and ψ(2s)→μ+μ-

We measure the branching fractions of the ψ(2S) meson to the leptonic final states e+e- and μ+μ- relative to that for ψ(2S)→J/ψπ+π-. The method uses ψ(2S) mesons produced in the decay of B mesons at the Υ(4S) resonance in a data sample collected with the BABAR detector at the Stanford Linear Accelerator Center. Using previous measurements for the ψ(2S)→J/ψπ+π- branching fraction, we determine the e+e- and μ+μ- branching fractions to be 0.0078±0.0009±0.0008 and 0.0067±0.0008±0.0007, respectively

Measurement of J/psi production in continuum e(+)e(-) annihilations near square root of s = 10.6 GeV.

The production of J/psi mesons in continuum e(+)e(-) annihilations has been studied with the BABAR detector at energies near the Upsilon(4S) resonance. The mesons are distinguished from J/psi production in B decays through their center-of-mass momentum and energy. We measure the cross section e(+)e(-)--&gt;J/psi X to be 2.52+/-0.21+/-0.21 pb. We set a 90% C.L. upper limit on the branching fraction for direct Upsilon(4S)--&gt;J/psi X decays at 4.7 x 10(-4)

Measurement of the decays B--&gt; phiK and B--&gt; phiK*.

We have observed the decays B--&gt; phiK and phiK(*) in a sample of over 45 million B mesons collected with the BABAR detector at the PEP-II collider. The measured branching fractions are B(B+--&gt; phiK+) = (7.7(+1.6)(-1.4)+/-0.8)x10(-6), B(B0--&gt; phiK0) = (8.1(+3.1)(-2.5)+/-0.8)x10(-6), B(B+--&gt; phiK(*+)) = (9.7(+4.2)(-3.4)+/-1.7)x10(-6), and B(B0--&gt; phiK(*0)) = (8.7(+2.5)(-2.1)+/-1.1)x10(-6). We also report the upper limit B(B+--&gt; phipi(+))&lt;1.4x10(-6) ( 90% C.L.)