Unifying Community Detection Across Scales from Genomes to Landscapes

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity

A conceptual cellular interaction model of left ventricular remodelling post-MI: dynamic network with exit-entry competition strategy

Abstract Background Progressive remodelling of the left ventricle (LV) following myocardial infarction (MI) is an outcome of spatial-temporal cellular interactions among different cell types that leads to heart failure for a significant number of patients. Cellular populations demonstrate temporal profiles of flux post-MI. However, little is known about the relationship between cell populations and the interaction strength among cells post-MI. The objective of this study was to establish a conceptual cellular interaction model based on a recently established graph network to describe the interaction between two types of cells. Results We performed stability analysis to investigate the effects of the interaction strengths, the initial status, and the number of links between cells on the cellular population in the dynamic network. Our analysis generated a set of conditions on interaction strength, structure of the network, and initial status of the network to predict the evolutionary profiles of the network. Computer simulations of our conceptual model verified our analysis. Conclusions Our study introduces a dynamic network to model cellular interactions between two different cell types which can be used to model the cellular population changes post-MI. The results on stability analysis can be used as a tool to predict the responses of particular cell populations

Revival of the magnetar PSR J1622-4950: observations with MeerKAT, Parkes, XMM-Newton, Swift, Chandra, and NuSTAR

New radio (MeerKAT and Parkes) and X-ray (XMM-Newton, Swift, Chandra, and NuSTAR) observations of PSR J1622-4950 indicate that the magnetar, in a quiescent state since at least early 2015, reactivated between 2017 March 19 and April 5. The radio flux density, while variable, is approximately 100x larger than during its dormant state. The X-ray flux one month after reactivation was at least 800x larger than during quiescence, and has been decaying exponentially on a 111+/-19 day timescale. This high-flux state, together with a radio-derived rotational ephemeris, enabled for the first time the detection of X-ray pulsations for this magnetar. At 5%, the 0.3-6 keV pulsed fraction is comparable to the smallest observed for magnetars. The overall pulsar geometry inferred from polarized radio emission appears to be broadly consistent with that determined 6-8 years earlier. However, rotating vector model fits suggest that we are now seeing radio emission from a different location in the magnetosphere than previously. This indicates a novel way in which radio emission from magnetars can differ from that of ordinary pulsars. The torque on the neutron star is varying rapidly and unsteadily, as is common for magnetars following outburst, having changed by a factor of 7 within six months of reactivation.Comment: Published in ApJ (2018 April 5); 13 pages, 4 figure

Editors’ Introduction: An Overview of the Educational Administration and Leadership Curriculum: Traditions of Islamic Educational Administration and Leadership in Higher Education

This chapter provides an overview of several topics relevant to constructing an approach to teaching educational administration and leadership in Muslim countries. First, it places the topic in the context of the changing nature and critiques of the field that argue for a greater internationalisation to both resist some of the negative aspects of globalisation and to represent countries’ traditions in the professional curriculum. Then, it identifies literature that presents the underlying principles and values of Islamic education that guide curriculum and pedagogy and shape its administration and leadership including the Qur’an and Sunnah and the classical educational literature which focuses on aims, values and goals of education as well as character development upon which a ‘good’ society is built. This is followed by a section on the Islamic administration and leadership traditions that are relevant to education, including the values of educational organisations and how they should be administered, identifying literature on the distinctive Islamic traditions of leadership and administrator education and training as it applies to education from the establishment of Islam and early classical scholars and senior administrators in the medieval period who laid a strong foundation for a highly sophisticated preparation and practice of administration in philosophical writings and the Mirrors of Princes writings, and subsequent authors who have built upon it up to the contemporary period. The final section provides an overview of the chapters in this collection

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The risk of developing major depression among individuals with subthreshold depression: a systematic review and meta-analysis of longitudinal cohort studies.

Studies have consistently shown that subthreshold depression is associated with an increased risk of developing major depression. However, no study has yet calculated a pooled estimate that quantifies the magnitude of this risk across multiple studies. We conducted a systematic review to identify longitudinal cohort studies containing data on the association between subthreshold depression and future major depression. A baseline meta-analysis was conducted using the inverse variance heterogeneity method to calculate the incidence rate ratio (IRR) of major depression among people with subthreshold depression relative to non-depressed controls. Subgroup analyses were conducted to investigate whether IRR estimates differed between studies categorised by age group or sample type. Sensitivity analyses were also conducted to test the robustness of baseline results to several sources of study heterogeneity, such as the case definition for subthreshold depression. Data from 16 studies (n = 67 318) revealed that people with subthreshold depression had an increased risk of developing major depression (IRR = 1.95, 95% confidence interval 1.28-2.97). Subgroup analyses estimated similar IRRs for different age groups (youth, adults and the elderly) and sample types (community-based and primary care). Sensitivity analyses demonstrated that baseline results were robust to different sources of study heterogeneity. The results of this study support the scaling up of effective indicated prevention interventions for people with subthreshold depression, regardless of age group or setting

Unifying community detection across scales from genomes to landscapes

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity