Alcohol Use and Periodic Limb Movements of Sleep

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65771/1/j.1530-0277.1993.tb00747.x.pd

Reproducibility of histopathological findings in experimental pathology of the mouse: a sorry tail

Reproducibility of $\textit{in vivo}$ research using the mouse as a model organism depends on many factors, including experimental design, strain or stock, experimental protocols, and methods of data evaluation. Gross and histopathology are often the endpoints of such research and there is increasing concern about the accuracy and reproducibility of diagnoses in the literature. To reproduce histopathological results, the pathology protocol, including necropsy methods and slide preparation, should be followed by interpretation of the slides by a pathologist familiar with reading mouse slides and familiar with the consensus medical nomenclature used in mouse pathology. Likewise, it is important that pathologists are consulted as reviewers of manuscripts where histopathology is a key part of the investigation. The absence of pathology expertise in planning, executing and reviewing $\textit{in vivo}$ research using mice leads to questionable pathology-based findings and conclusions from studies, even in high-impact journals. We discuss the various aspects of this problem, give some examples from the literature and suggest solutions.This work was supported in part by US National Institutes of Health grants R01 AR049288, CA089713 and R21 AR063781 (to J.P.S.) and by The Warden and Fellows of Robinson College, Cambridge (to P.N.S.)

Need-driven dementia-compromised behavior: An alternative view of disruptive behavior

The disruptive behavior of persons with dementia is a problem of considerable clinical interest and growing scientific concern. This paper offers a view of these behaviors as expressions of unmet needs or goals and provides a comprehensive conceptual framework to guide further research and clinical practice. Empiricalfindings and clinical impressions related to wandering, vocalizations and aggression to support and illustrate the framework are presentedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66887/2/10.1177_153331759601100603.pd

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards

Golden single-atomic-site platinum electrocatalysts

Bimetallic nanoparticles with tailored structures constitute a desirable model system for catalysts, as crucial factors such as geometric and electronic effects can be readily controlled by tailoring the structure and alloy bonding of the catalytic site. Here we report a facile colloidal method to prepare a series of platinum–gold (PtAu) nanoparticles with tailored surface structures and particle diameters on the order of 7 nm. Samples with low Pt content, particularly Pt 4 Au 96 , exhibited unprecedented electrocatalytic activity for the oxidation of formic acid. A high forward current density of 3.77 A mg Pt −1 was observed for Pt 4 Au 96 , a value two orders of magnitude greater than those observed for core–shell structured Pt 78 Au 22 and a commercial Pt nanocatalyst. Extensive structural characterization and theoretical density functional theory simulations of the best-performing catalysts revealed densely packed single-atom Pt surface sites surrounded by Au atoms, which suggests that their superior catalytic activity and selectivity could be attributed to the unique structural and alloy-bonding properties of these single-atomic-site catalysts

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology