A mid to late Holocene cryptotephra framework from eastern North America

Holocene cryptotephras of Alaskan and Pacific Northwestern origin have recently been detected ca. 7000 km away on the east coast of North America. This study extends the emerging North American tephrochronological framework by geochemically characterising seventeen cryptotephra layers from four newly explored peatlands. All detected tephras were deposited during the late Holocene, with no horizons present in the peat between ca. 3000–5000 years ago. The prevalence of the Alaskan White River Ash eastern lobe (AD 847 ± 1) is confirmed across the eastern seaboard from Newfoundland to Maine and a regional depositional pattern from Mount St Helens Set W (AD 1479–1482) is presented. The first occurrences of four additional cryptotephras in eastern North America are described, three of which may originate from source regions in Mexico, Kamchatka (Russia) and Hokkaido (Japan). The possibility of such tephras reaching eastern North America presents the opportunity to link palaeo-archives from the tropics and eastern Asia with those from the western Atlantic seaboard, aiding inter-regional comparisons of proxy-climatic records.

Traumatic brain injury and co-occurring problems in prison populations: a systematic review.

Background: A growing body of epidemiological research suggests high rates of traumatic brain injury (TBI) in prisoners. The aim of this review is to systematically explore the literature surrounding the rates of TBI and their co-occurrences in a prison population.
Methods: Six electronic databases were systematically searched for articles published between 1980 and 2014. Studies were screened for inclusion based on predetermined criteria by two researchers who independently performed data extraction. Study quality was appraised based on a modified quality assessment tool.
Results: Twenty six studies were included in this review. Quality assessment ranged from 20% (poor) to 80% (good) with an overall average of 60%. Twenty four papers included TBI prevalence rates, which ranged from 5.69%-88%. Seventeen studies explored co-occurring factors including rates of aggression (n=7), substance abuse (n=9), anxiety and depression (n=5), neurocognitive deficits (n=4), and psychiatric conditions (n=3).
Conclusions: The high degree of variation in TBI rates may be attributed to the inconsistent way in which TBI was measured with only seven studies using valid and reliable screening tools. Additionally, gaps in the literature surrounding personality outcomes in prisoners with TBI, female prisoners with TBI, and qualitative outcomes were found.

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.

Search filters: mapping the literature using a novel form of content analysis.

Background: Search filters are combinations of words and phrases designed to retrieve an optimal set of records on a particular topic (subject filters) or study design (methodological filters). Information specialists are increasingly turning to reusable filters to focus their searches. However, the extent of the academic literature on search filters is unknown. We provide a broad overview to the academic literature on search filters.
Objectives: To map the academic literature on search filters from 2004 to 2015 using a novel form of content analysis.
Methods: We conducted a comprehensive search for literature between 2004 and 2015 across eight databases using a subjectively derived search strategy. We identified key words from titles, grouped them into categories, and examined their frequency and co-occurrences.
Results: The majority of records were housed in Embase (n = 178) and MEDLINE (n = 154). Over the last decade, both databases appeared to exhibit a bimodal distribution with the number of publications on search filters rising until 2006, before dipping in 2007, and steadily increasing until 2012. Few articles appeared in social science databases over the same time frame (e.g. Social Services Abstracts, n = 3).
Unsurprisingly, the term ‘search’ appeared in most titles, and quite often, was used as a noun adjunct for the word 'filter' and ‘strategy’. Across the papers, the purpose of searches as a means of 'identifying' information and gathering ‘evidence’ from 'databases' emerged quite strongly. Other terms relating to the methodological assessment of search filters, such as precision and validation, also appeared albeit less frequently.
Conclusions: Our findings show surprising commonality across the papers with regard to the literature on search filters. Much of the literature seems to be focused on developing search filters to identify and retrieve information, as opposed to testing or validating such filters. Furthermore, the literature is mostly housed in health-related databases, namely MEDLINE, CINAHL, and Embase, implying that it is medically driven. Relatively few papers focus on the use of search filters in the social sciences.