Boston University

About: Boston University is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 48688 authors who have published 119622 publications receiving 6276020 citations. The organization is also known as: BU & Boston U.

Solar 8B and hep Neutrino Measurements from 1258 Days of Super-Kamiokande Data

Abstract: Solar neutrino measurements from 1258days of data from the Super-Kamiokande detector are presented. The measurements are based on recoil electrons in the energy range 5.0{endash}20.0MeV. The measured solar neutrino flux is 2.32{+-}0.03(stat){sup +0.08}{sub {minus}0.07}(syst){times}10{sup 6} cm{sup {minus}2}s{sup {minus}1} , which is 45.1{+-}0.5(stat ){sup +1.6}{sub {minus}1.4}(syst) % of that predicted by the BP2000 SSM. The day vs night flux asymmetry ({Phi}{sub n}{minus}{Phi}{sub d})/ {Phi}{sub average} is 0.033{+-}0.022(stat){sup +0.013}{sub {minus}0.012}(syst) . The recoil electron energy spectrum is consistent with no spectral distortion. For the hep neutrino flux, we set a 90% C.L.upper limit of 40{times}10{sup 3} cm{sup {minus}2}s{sup {minus}1} , which is 4.3times the BP2000 SSM prediction.

Loss-of-function mutations in APOC3, triglycerides, and coronary disease

Abstract: Background Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. Methods We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. Results An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10 − 20 ), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P = 8×10 − 10 ). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P = 4×10 − 6 ). Conclusions Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.)

Identifying mislabeled training data

Abstract: This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30%. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.