Silent progression in disease activity-free relapsing multiple sclerosis.

ObjectiveRates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow-up. Notably, "no evidence of disease activity" at 2 years did not predict long-term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long-term disability accumulation.MethodsDisability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0-5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA-DRB1*15:01 as covariates.ResultsRelapses were associated with a transient increase in disability over 1-year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p < 0.05).InterpretationLong-term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing-remitting MS. Ann Neurol 2019;85:653-666

Cross-Disorder Genomewide Analysis of Schizophrenia, Bipolar Disorder, and Depression

Family and twin studies indicate substantial overlap of genetic influences on psychotic and mood disorders. Linkage and candidate gene studies have also suggested overlap across schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD). The objective of this study was to apply genomewide association study (GWAS) analysis to address the specificity of genetic effects on these disorders

Adult Romantic Attachment, Negative Emotionality, and Depressive Symptoms in Middle Aged Men: A Multivariate Genetic Analysis

Adult romantic attachment styles reflect ways of relating in close relationships and are associated with depression and negative emotionality. We estimated the extent to which dimensions of romantic attachment and negative emotionality share genetic or environmental risk factors in 1,237 middle-aged men in the Vietnam Era Twin Study of Aging (VETSA). A common genetic factor largely explained the covariance between attachment-related anxiety, attachment-related avoidance, depressive symptoms, and two measures of negative emotionality: Stress-Reaction (anxiety), and Alienation. Multivariate results supported genetic and environmental differences in attachment. Attachment-related anxiety and attachment-related avoidance were each influenced by additional genetic factors not shared with other measures; the genetic correlation between the attachment measure-specific genetic factors was 0.41, indicating some, but not complete overlap of genetic factors. Genetically informative longitudinal studies on attachment relationship dimensions can help to illuminate the role of relationship-based risk factors in healthy aging

Variation in diabetes care by age: opportunities for customization of care

BACKGROUND: The quality of diabetes care provided to older adults has usually been judged to be poor, but few data provide direct comparison to other age groups. In this study, we hypothesized that adults age 65 and over receive lower quality diabetes care than adults age 45–64 years old. METHODS: We conducted a cohort study of members of a health plan cared for by multiple medical groups in Minnesota. Study subjects were a random sample of 1109 adults age 45 and over with an established diagnosis of diabetes using a diabetes identification method with estimated sensitivity 0.91 and positive predictive value 0.94. Survey data (response rate 86.2%) and administrative databases were used to assess diabetes severity, glycemic control, quality of life, microvascular and macrovascular risks and complications, preventive care, utilization, and perceptions of diabetes. RESULTS: Compared to those aged 45–64 years (N = 627), those 65 and older (N = 482) had better glycemic control, better health-related behaviors, and perceived less adverse impacts of diabetes on their quality of life despite longer duration of diabetes and a prevalence of cardiovascular disease twice that of younger patients. Older patients did not ascribe heart disease to their diabetes. Younger adults often had explanatory models of diabetes that interfere with effective and aggressive care, and accessed care less frequently. Overall, only 37% of patients were simultaneously up-to-date on eye exams, foot exams, and glycated hemoglobin (A1c) tests within one year. CONCLUSION: These data demonstrate the need for further improvement in diabetes care for all patients, and suggest that customisation of care based on age and explanatory models of diabetes may be an improvement strategy that merits further evaluation

Whole genome comparison of a large collection of mycobacteriophages reveals a continuum of phage genetic diversity

The bacteriophage population is large, dynamic, ancient, and genetically diverse. Limited genomic information shows that phage genomes are mosaic, and the genetic architecture of phage populations remains ill-defined. To understand the population structure of phages infecting a single host strain, we isolated, sequenced, and compared 627 phages of Mycobacterium smegmatis. Their genetic diversity is considerable, and there are 28 distinct genomic types (clusters) with related nucleotide sequences. However, amino acid sequence comparisons show pervasive genomic mosaicism, and quantification of inter-cluster and intra-cluster relatedness reveals a continuum of genetic diversity, albeit with uneven representation of different phages. Furthermore, rarefaction analysis shows that the mycobacteriophage population is not closed, and there is a constant influx of genes from other sources. Phage isolation and analysis was performed by a large consortium of academic institutions, illustrating the substantial benefits of a disseminated, structured program involving large numbers of freshman undergraduates in scientific discovery

Will Democracy Endure Private School Choice? The Effect of the Milwaukee Parental Choice Program on Adult Voting Behavior

We employ probit regression analysis to compare the adult voting activity of students who participated in the Milwaukee Parental Choice Program (MPCP) to their matched public school counterparts. We use a sophisticated matching algorithm to create a traditional public school student comparison group using data from the state-mandated evaluation of the MPCP. By the time the students are 19-26 years old, we do not find evidence that private school voucher students are more or less likely to vote in 2012 or 2016 than students educated in public schools. These results are robust to all models and are consistent for all subgroups

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License