Review of How To Pray: Reflections and Essays

Review of: C. S. Lewis, How To Pray: Reflections and Essays (New York: HarperOne, 2018). 157 pages. $22.99. ISBN 9780062847133

Trance: From Africa to Pentecostalism

The author, a student at Concordia Seminary, St. Louis, studied indigenous religious movements in Asia and Africa during 1969-70 as a John Courtney Murray Fellow of Yale University. His book about indigenous churches in Ghana, Eden Revival is in the process of publication. Observations of indigenous Afro-American churches in the Caribbean during 1971 were made possible by a partial grant from the World Mission Institute of Concordia Seminary

Review of Deeper Magic: The Theology Behind the Writings of C. S. Lewis

A review of Donald T. Williams, Deeper Magic: The Theology Behind the Writings of C. S. Lewis (Baltimore, Maryland: Square Halo Books, 2016). 287 pages. $16.99. ISBN 9781941106051

Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture

Core Count vs Cache Size for Manycore Architectures in the Cloud

The number of cores which fit on a single chip is growing at an exponential rate while off-chip main memory bandwidth is growing at a linear rate at best. This core count to off-chip bandwidth disparity causes per-core memory bandwidth to decrease as process technology advances. Continuing per-core off-chip bandwidth reduction will cause multicore and manycore chip architects to rethink the optimal grain size of a core and the on-chip cache configuration in order to save main memory bandwidth. This work introduces an analytic model to study the tradeoffs of utilizing increased chip area for larger caches versus more cores. We focus this study on constructing manycore architectures well suited for the emerging application space of cloud computing where many independent applications are consolidated onto a single chip. This cloud computing application mix favors small, power-efficient cores. The model is exhaustively evaluated across a large range of cache and core-count configurations utilizing SPEC Int 2000 miss rates and CACTI timing and area models to determine the optimal cache configurations and the number of cores across four process nodes. The model maximizes aggregate computational throughput and is applied to SRAM and logic process DRAM caches. As an example, our study demonstrates that the optimal manycore configuration in the 32nm node for a 200 mm^2 die uses on the order of 158 cores, with each core containing a 64KB L1I cache, a 16KB L1D cache, and a 1MB L2 embedded-DRAM cache. This study finds that the optimal cache size will continue to grow as process technology advances, but the tradeoff between more cores and larger caches is a complex tradeoff in the face of limited off-chip bandwidth and the non-linearities of cache miss rates and memory controller queuing delay

Understanding the unexplained : the magnitude and correlates of individual differences in residual variance

Behavioral and physiological ecologists have long been interested in explaining the causes and consequences of trait variation, with a focus on individual differences in mean values. However, the majority of phenotypic variation typically occurs within individuals, rather than among individuals (as indicated by average repeatability being less than 0.5). Recent studies have further shown that individuals can also differ in the magnitude of variation that is unexplained by individual variation or environmental factors (i.e., residual variation). The significance of residual variation, or why individuals differ, is largely unexplained, but is important from evolutionary, methodological, and statistical perspectives. Here, we broadly reviewed literature on individual variation in behavior and physiology, and located 39 datasets with sufficient repeated measures to evaluate individual differences in residual variance. We then analyzed these datasets using methods that permit direct comparisons of parameters across studies. This revealed substantial and widespread individual differences in residual variance. The magnitude of individual variation appeared larger in behavioral traits than in physiological traits, and heterogeneity was greater in more controlled situations. We discuss potential ecological and evolutionary implications of individual differences in residual variance and suggest productive future research directions

Understanding and predicting antidepressant response : using animal models to move toward precision psychiatry

There are two important gaps of knowledge in depression treatment, namely the lack of biomarkers predicting response to antidepressants and the limited knowledge of the molecular mechanisms underlying clinical improvement. However, individually tailored treatment strategies and individualized prescription are greatly needed given the huge socio-economic burden of depression, the latency until clinical improvement can be observed and the response variability to a particular compound. Still, individual patient-level antidepressant treatment outcomes are highly unpredictable. In contrast to other therapeutic areas and despite tremendous efforts during the past years, the genomics era so far has failed to provide biological or genetic predictors of clinical utility for routine use in depression treatment. Specifically, we suggest to 1) shift the focus from the group patterns to individual outcomes, 2) use dimensional classifications such as Research Domain Criteria, 3) envision better planning and improved connections between pre-clinical and clinical studies within translational research units. In contrast to studies in patients, animal models enable both searches for peripheral biosignatures predicting treatment response and in depth analyses of the neurobiological pathways shaping individual antidepressant response in the brain. While there is a considerable number of animal models available aiming at mimicking disease-like conditions such as those seen in depressive disorder, only a limited number of preclinical or truly translational investigations is dedicated to the issue of heterogeneity seen in response to antidepressant treatment. In this mini-review, we provide an overview on the current state of knowledge and propose a framework for successful translational studies into antidepressant treatment response