Using time-use diaries to track changing behavior across successive stages of COVID-19 social restrictions

How did people change their behavior over the different phases of the UK COVID-19 restrictions, and how did these changes affect their risk of being exposed to infection? Time-use diary surveys are unique in providing a complete chronicle of daily behavior: 24-h continuous records of the populations’ activities, their social context, and their location. We present results from four such surveys, collected in real time from representative UK samples, both before and at three points over the course of the current pandemic. Comparing across the four waves, we find evidence of substantial changes in the UK population’s behavior relating to activities, locations, and social context. We assign different levels of risk to combinations of activities, locations, and copresence to compare risk-related behavior across successive “lockdowns.” We find evidence that during the second lockdown (November 2020), there was an increase in high-risk behaviors relative to the first (starting March 2020). This increase is shown to be associated with more paid work time in the workplace. At a time when capacity is still limited both in respect of immunization and track–trace technology, governments must continue to rely on changes in people’s daily behaviors to contain the spread of COVID-19 and similar viruses. Time-use diary information of this type, collected in real time across the course of the COVID-19 pandemic, can provide policy makers with information to assess and quantify changes in daily behaviors and the impact they are likely to have on overall behavioral-associated risks

Gene Ontology: Pitfalls, Biases, and Remedies.

The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices

Learning pair-wise gene functional similarity by multiplex gene expression maps

Abstract Background The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute. Results Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction. Conclusions By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions

Exact score distribution computation for ontological similarity searches

<p>Abstract</p> <p>Background</p> <p>Semantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic similarity searches can be improved by ranking results according to the probability of obtaining a given score at random rather than by the scores themselves. However, to date, there are no algorithms for computing the exact distribution of semantic similarity scores, which is necessary for computing the exact <it>P</it>-value of a given score.</p> <p>Results</p> <p>In this paper we consider the exact computation of score distributions for similarity searches in ontologies, and introduce a simple null hypothesis which can be used to compute a <it>P</it>-value for the statistical significance of similarity scores. We concentrate on measures based on Resnik's definition of ontological similarity. A new algorithm is proposed that collapses subgraphs of the ontology graph and thereby allows fast score distribution computation. The new algorithm is several orders of magnitude faster than the naive approach, as we demonstrate by computing score distributions for similarity searches in the HPO. It is shown that exact <it>P</it>-value calculation improves clinical diagnosis using the HPO compared to approaches based on sampling.</p> <p>Conclusions</p> <p>The new algorithm enables for the first time exact <it>P</it>-value calculation via exact score distribution computation for ontology similarity searches. The approach is applicable to any ontology for which the annotation-propagation rule holds and can improve any bioinformatic method that makes only use of the raw similarity scores. The algorithm was implemented in Java, supports any ontology in OBO format, and is available for non-commercial and academic usage under: <url>https://compbio.charite.de/svn/hpo/trunk/src/tools/significance/</url></p

A search for the decay modes B+/- to h+/- tau l

We present a search for the lepton flavor violating decay modes B+/- to h+/- tau l (h= K,pi; l= e,mu) using the BaBar data sample, which corresponds to 472 million BBbar pairs. The search uses events where one B meson is fully reconstructed in one of several hadronic final states. Using the momenta of the reconstructed B, h, and l candidates, we are able to fully determine the tau four-momentum. The resulting tau candidate mass is our main discriminant against combinatorial background. We see no evidence for B+/- to h+/- tau l decays and set a 90% confidence level upper limit on each branching fraction at the level of a few times 10^-5.Comment: 15 pages, 7 figures, submitted to Phys. Rev.

Evidence for an excess of B -> D(*) Tau Nu decays

Based on the full BaBar data sample, we report improved measurements of the ratios R(D(*)) = B(B -> D(*) Tau Nu)/B(B -> D(*) l Nu), where l is either e or mu. These ratios are sensitive to new physics contributions in the form of a charged Higgs boson. We measure R(D) = 0.440 +- 0.058 +- 0.042 and R(D*) = 0.332 +- 0.024 +- 0.018, which exceed the Standard Model expectations by 2.0 sigma and 2.7 sigma, respectively. Taken together, our results disagree with these expectations at the 3.4 sigma level. This excess cannot be explained by a charged Higgs boson in the type II two-Higgs-doublet model. We also report the observation of the decay B -> D Tau Nu, with a significance of 6.8 sigma.Comment: Expanded section on systematics, text corrections, improved the format of Figure 2 and included the effect of the change of the Tau polarization due to the charged Higg

Search for the decay modes D^0 → e^+e^-, D^0 → μ^+μ^-, and D^0 → e^±μ∓

We present searches for the rare decay modes D^0→e^+e^-, D^0→μ^+μ^-, and D^0→e^±μ^∓ in continuum e^+e^-→cc events recorded by the BABAR detector in a data sample that corresponds to an integrated luminosity of 468  fb^(-1). These decays are highly Glashow–Iliopoulos–Maiani suppressed but may be enhanced in several extensions of the standard model. Our observed event yields are consistent with the expected backgrounds. An excess is seen in the D^0→μ^+μ^- channel, although the observed yield is consistent with an upward background fluctuation at the 5% level. Using the Feldman–Cousins method, we set the following 90% confidence level intervals on the branching fractions: B(D^0→e^+e^-)<1.7×10^(-7), B(D^0→μ^+μ^-) within [0.6,8.1]×10^(-7), and B(D^0→e^±μ^∓)<3.3×10^(-7)

Study of the reaction e^{+}e^{-} -->J/psi\pi^{+}\pi^{-} via initial-state radiation at BaBar

We study the process $e^+e^-\to J/\psi\pi^{+}\pi^{-}$ with initial-state-radiation events produced at the PEP-II asymmetric-energy collider. The data were recorded with the BaBar detector at center-of-mass energies 10.58 and 10.54 GeV, and correspond to an integrated luminosity of 454 $\mathrm{fb^{-1}}$. We investigate the $J/\psi \pi^{+}\pi^{-}$ mass distribution in the region from 3.5 to 5.5 $\mathrm{GeV/c^{2}}$. Below 3.7 $\mathrm{GeV/c^{2}}$ the $\psi(2S)$ signal dominates, and above 4 $\mathrm{GeV/c^{2}}$ there is a significant peak due to the Y(4260). A fit to the data in the range 3.74 -- 5.50 $\mathrm{GeV/c^{2}}$ yields a mass value $4244 \pm 5$ (stat) $\pm 4$ (syst)$\mathrm{MeV/c^{2}}$ and a width value $114 ^{+16}_{-15}$ (stat)$\pm 7$(syst)$\mathrm{MeV}$ for this state. We do not confirm the report from the Belle collaboration of a broad structure at 4.01 $\mathrm{GeV/c^{2}}$. In addition, we investigate the $\pi^{+}\pi^{-}$ system which results from Y(4260) decay

Observation and study of baryonic B decays: B -> D(*) p pbar, D(*) p pbar pi, and D(*) p pbar pi pi

We present a study of ten B-meson decays to a D(*), a proton-antiproton pair, and a system of up to two pions using BaBar's data set of 455x10^6 BBbar pairs. Four of the modes (B0bar -> D0 p anti-p, B0bar -> D*0 p anti-p, B0bar -> D+ p anti-p pi-, B0bar -> D*+ p anti-p pi-) are studied with improved statistics compared to previous measurements; six of the modes (B- -> D0 p anti-p pi-, B- -> D*0 p anti-p pi-, B0bar -> D0 p anti-p pi- pi+, B0bar -> D*0 p anti-p pi- pi+, B- -> D+ p anti-p pi- pi-, B- -> D*+ p anti-p pi- pi-) are first observations. The branching fractions for 3- and 5-body decays are suppressed compared to 4-body decays. Kinematic distributions for 3-body decays show non-overlapping threshold enhancements in m(p anti-p) and m(D(*)0 p) in the Dalitz plots. For 4-body decays, m(p pi-) mass projections show a narrow peak with mass and full width of (1497.4 +- 3.0 +- 0.9) MeV/c2, and (47 +- 12 +- 4) MeV/c2, respectively, where the first (second) errors are statistical (systematic). For 5-body decays, mass projections are similar to phase space expectations. All results are preliminary.Comment: 28 pages, 90 postscript figures, submitted to LP0