PERCEIVED AND INDUCED EMOTION RESPONSES TO POPULAR MUSIC: CATEGORICAL AND DIMENSIONAL MODELS

Music both conveys and evokes emotions, and although both phenomena are widely studied, the difference between them is often neglected. The purpose of this study is to examine the difference between perceived and induced emotion for western popular music using both categorical and dimensional models of emotion, and to examine the influence of individual listener differences on their emotion judgment. A total of 80 musical excerpts were randomly selected from an established dataset of 2,904 popular songs tagged with one of the four words happy, sad, angry, or relaxed on the last.fm web site. Participants listened to the excerpts and rated perceived and induced emotion on the categorical model and dimensional model, and the reliability of emotion tags was evaluated according to participants\u27 agreement with corresponding labels. In addition, the goldsmiths musical sophistication index (gold-msi) was used to assess participants\u27 musical expertise and engagement. As expected, regardless of the emotion model used, music evokes emotions similar to the emotional quality perceived in music. Moreover, emotion tags predict music emotion judgments. However, age, gender and three factors from gold-msi, importance, emotion, and music training were found not to predict listeners\u27 responses, nor the agreement with tags

Musical tasks targeting preserved and impaired functions in two dementias.

Studies of musical abilities in dementia have for the most part been rather general assessments of abilities, for instance, assessing retention of music learned premorbidly. Here, we studied patients with dementias with contrasting cognitive profiles to explore specific aspects of music cognition under challenge. Patients suffered from Alzheimer's disease (AD), in which a primary impairment is in forming new declarative memories, or Lewy body disease (PD/LBD), a type of parkinsonism in which executive impairments are prominent. In the AD patients, we examined musical imagery. Behavioral and neural evidence confirms involvement of perceptual networks in imagery, and these are relatively spared in early stages of the illness. Thus, we expected patients to have relatively intact imagery in a mental pitch comparison task. For the LBD patients, we tested whether executive dysfunction would extend to music. We probed inhibitory skills by asking for a speeded pitch or timbre judgment when the irrelevant dimension was held constant or also changed. Preliminary results show that AD patients score similarly to controls in the imagery tasks, but PD/LBD patients are impaired relative to controls in suppressing some irrelevant musical dimensions, particularly when the required judgment varies from trial to trial

Entangled-State Cycles of Atomic Collective-Spin States

We study quantum trajectories of collective atomic spin states of $N$ effective two-level atoms driven with laser and cavity fields. We show that interesting ``entangled-state cycles'' arise probabilistically when the (Raman) transition rates between the two atomic levels are set equal. For odd (even) $N$, there are $(N+1)/2$ ($N/2$) possible cycles. During each cycle the $N$-qubit state switches, with each cavity photon emission, between the states $(|N/2,m>\pm |N/2,-m>)/\sqrt{2}$, where $|N/2,m>$ is a Dicke state in a rotated collective basis. The quantum number $m$ ($>0$), which distinguishes the particular cycle, is determined by the photon counting record and varies randomly from one trajectory to the next. For even $N$ it is also possible, under the same conditions, to prepare probabilistically (but in steady state) the Dicke state $|N/2,0>$, i.e., an $N$-qubit state with $N/2$ excitations, which is of particular interest in the context of multipartite entanglement.Comment: 10 pages, 9 figure

Traditional Roles in a Non‐Traditional Setting: Genetic Counseling in Precision Oncology

Next generation sequencing technology is increasingly utilized in oncology with the goal of targeting therapeutics to improve response and reduce side effects. Interpretation of tumor mutations requires sequencing of paired germline DNA, raising questions about incidental germline findings. We describe our experiences as part of a research team implementing a protocol for whole genome sequencing (WGS) of tumors and paired germline DNA known as the Michigan Oncology Sequencing project (MI‐ONCOSEQ) that includes options for receiving incidental germline findings. Genetic counselors (GCs) discuss options for return of results with patients during the informed consent process and document family histories. GCs also review germline findings and actively participate in the multi‐disciplinary Precision Medicine Tumor Board (PMTB), providing clinical context for interpretation of germline results and making recommendations about disclosure of germline findings. GCs have encountered ethical and counseling challenges with participants, described here. Although GCs have not been traditionally involved in molecular testing of tumors, our experiences with MI‐ONCOSEQ demonstrate that GCs have important applicable skills to contribute to multi‐disciplinary care teams implementing precision oncology. Broader use of WGS in oncology treatment decision making and American College of Medical Genetics and Genomics (ACMG) recommendations for active interrogation of germline tissue in tumor‐normal dyads suggests that GCs will have future opportunities in this area outside of research settings.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147078/1/jgc40655.pd

Selective Constraints on Amino Acids Estimated by a Mechanistic Codon Substitution Model with Multiple Nucleotide Changes

Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices. Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins. The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences.Comment: Table 9 in this article includes corrections for errata in the Table 9 published in 10.1371/journal.pone.0017244. Supporting information is attached at the end of the article, and a computer-readable dataset of the ML estimates of selective constraints is available from 10.1371/journal.pone.001724

Current sample size conventions: Flaws, harms, and alternatives

<p>Abstract</p> <p>Background</p> <p>The belief remains widespread that medical research studies must have statistical power of at least 80% in order to be scientifically sound, and peer reviewers often question whether power is high enough.</p> <p>Discussion</p> <p>This requirement and the methods for meeting it have severe flaws. Notably, the true nature of how sample size influences a study's projected scientific or practical value precludes any meaningful blanket designation of <80% power as "inadequate". In addition, standard calculations are inherently unreliable, and focusing only on power neglects a completed study's most important results: estimates and confidence intervals. Current conventions harm the research process in many ways: promoting misinterpretation of completed studies, eroding scientific integrity, giving reviewers arbitrary power, inhibiting innovation, perverting ethical standards, wasting effort, and wasting money. Medical research would benefit from alternative approaches, including established <it>value of information </it>methods, simple choices based on cost or feasibility that have recently been justified, sensitivity analyses that examine a meaningful array of possible findings, and following previous analogous studies. To promote more rational approaches, research training should cover the issues presented here, peer reviewers should be extremely careful before raising issues of "inadequate" sample size, and reports of completed studies should not discuss power.</p> <p>Summary</p> <p>Common conventions and expectations concerning sample size are deeply flawed, cause serious harm to the research process, and should be replaced by more rational alternatives.</p

A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is capable of exploiting both the information from the observed values as well the information hidden in the missing patterns in multivariate time series (MTS) originating e.g. from EHRs. The kernel, called TCK$_{IM}$, is designed using an ensemble learning strategy in which the base models are novel mixed mode Bayesian mixture models which can effectively exploit informative missingness without having to resort to imputation methods. Moreover, the ensemble approach ensures robustness to hyperparameters and therefore TCK$_{IM}$ is particularly well suited if there is a lack of labels - a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.Comment: 2020 International Workshop on Health Intelligence, AAAI-20. arXiv admin note: text overlap with arXiv:1907.0525