MScanner: a classifier for retrieving Medline citations

BACKGROUND: Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. RESULTS: MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning results ranked by decreasing probability of relevance. For maximum speed it uses the Medical Subject Headings (MeSH) and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline. The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline. We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic. In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92. CONCLUSION: MScanner is an effective non-domain-specific classifier that operates on the entire Medline database, and is suited to retrieving topics for which many features may indicate relevance. Its web interface simplifies the task of classifying Medline citations, compared to building a pre-filter and classifier specific to the topic. The data sets and open source code used to obtain the results in this paper are available on-line and as supplementary material, and the web interface may be accessed at http://mscanner.stanford.edu

Comparing Tree‐Ring and Permanent Plot Estimates of Aboveground Net Primary Production in Three Eastern U.S. Forests

Forests account for a large portion of sequestered carbon, much of which is stored as wood in trees. The rate of carbon accumulation in aboveground plant material, or aboveground net primary productivity (aNPP), quantifies annual to decadal variations in forest carbon sequestration. Permanent plots are often used to estimate aNPP but are usually not annually resolved and take many years to develop a long data set. Tree rings are a unique and infrequently used source for measuring aNPP, and benefit from fine spatial (individual trees) and temporal (annual) resolution. Because of this precision, tree rings are complementary to permanent plots and the suite of tools used to study forest productivity. Here we evaluate whether annual estimates of aNPP developed from tree rings approximate estimates derived from colocated permanent plots. We studied a lowland evergreen (Howland, Maine), mixed deciduous (Harvard Forest, Massachusetts), and mixed mesophytic (Fernow, West Virginia) forest in the eastern United States. Permanent plots at the sites cover an area of 2–3 ha, and we use these areas as benchmarks indicative of the forest stand. We simulate random draws of permanent plot subsets to describe the distribution of aNPP estimates given a sampling area size equivalent to the tree-ring plots. Though mean tree-ring aNPP underestimates permanent plot aNPP slightly at Howland and Fernow and overestimates at Harvard Forest when compared with the entire permanent plot, it is within the 95% confidence interval of the random draws of equal-sized sampling area at all sites. To investigate whether tree-ring aNPP can be upscaled to the stand, we conducted a second random draw of permanent plot subsets simulating a twofold increase in sampling area. aNPP estimates from this distribution were not significantly different from results of the initial sampling area, though variance decreased as sampling area approaches stand area. Despite several concerns to consider when using tree rings to reconstruct aNPP (e.g., upscaling, allometric, and sampling uncertainties), the benefits are apparent, and we call for the continued application of tree rings in carbon cycle studies across a broader range of species diversity, productivity, and disturbance histories to fully develop this potential

Comparing Tree-Ring And Permanent Plot Estimates Of Aboveground Net Primary Production In Three Eastern U.S. Forests

Forests account for a large portion of sequestered carbon, much of which is stored as wood in trees. The rate of carbon accumulation in aboveground plant material, or aboveground net primary productivity (aNPP), quantifies annual to decadal variations in forest carbon sequestration. Permanent plots are often used to estimate aNPP but are usually not annually resolved and take many years to develop a long data set. Tree rings are a unique and infrequently used source for measuring aNPP, and benefit from fine spatial (individual trees) and temporal (annual) resolution. Because of this precision, tree rings are complementary to permanent plots and the suite of tools used to study forest productivity. Here we evaluate whether annual estimates of aNPP developed from tree rings approximate estimates derived from colocated permanent plots. We studied a lowland evergreen (Howland, Maine), mixed deciduous (Harvard Forest, Massachusetts), and mixed mesophytic (Fernow, West Virginia) forest in the eastern United States. Permanent plots at the sites cover an area of 2-3 ha, and we use these areas as benchmarks indicative of the forest stand. We simulate random draws of permanent plot subsets to describe the distribution of aNPP estimates given a sampling area size equivalent to the tree-ring plots. Though mean tree-ring aNPP underestimates permanent plot aNPP slightly at Howland and Fernow and overestimates at Harvard Forest when compared with the entire permanent plot, it is within the 95% confidence interval of the random draws of equal-sized sampling area at all sites. To investigate whether tree-ring aNPP can be upscaled to the stand, we conducted a second random draw of permanent plot subsets simulating a twofold increase in sampling area. aNPP estimates from this distribution were not significantly different from results of the initial sampling area, though variance decreased as sampling area approaches stand area. Despite several concerns to consider when using tree rings to reconstruct aNPP (e.g., upscaling, allometric, and sampling uncertainties), the benefits are apparent, and we call for the continued application of tree rings in carbon cycle studies across a broader range of species diversity, productivity, and disturbance histories to fully develop this potential

"Otherness", otherism, discrimination, and health inequalities: entrenched challenges for modern psychiatric disciplines

Identity is a complex concept that can be informed by various factors, involving biological, psychological, experiential, and social influences. Specifically, one’s social identity refers to the ways in which individuals can adopt attributes from established collective categories, like cultural identities, ethnic identities, gender identities, and class identities, amongst others. Social identity can encompass unique and diverse interactions at an individual level, known as micro-identities, that may be selectively expressed, hidden, or downplayed, contingent on distinct sociocultural settings. However, the formation of social identity is recurrently defined in opposition to perceptions of the Other, which can entail adverse paradigms of marginalisation, stigma, and discrimination. Although this theory of Otherness has been developed across different fields, particularly sociology, it may be important in psychiatric contexts as it can engender inherent risk factors and mental health inequalities. Consequently, this paper seeks to bring attention towards these issues, exploring the construction of Otherness and its detrimental outcomes for psychiatry, such as systemic discrimination and disparities in therapeutic support, alongside recommended initiatives to mitigate against the effects of Otherness. This may require multifactorial approaches that include cultural competency training, interventions informed by micro-identities and intersectionality, patient advocacy, and structural changes to mental health policy

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Huntzinger, D. N., Schaefer, K., Schwalm, C., Fisher, J. B., Hayes, D., Stofferahn, E., Carey, J., Michalak, A. M., Wei, Y., Jain, A. K., Kolus, H., Mao, J., Poulter, B., Shi, X., Tang, J., & Tian, H. Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems. Environmental Research Letters, 15(2), (2020): 025005, doi:10.1088/1748-9326/ab6784.Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.This work was supported by NASA'S Arctic Boreal Vulnerability Experiment (ABoVE; https://above.nasa.gov); NNN13D504T. Funding for the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP; https://nacp.ornl.gov/MsTMIP.shtml) activity was provided through NASA ROSES Grant #NNX10AG01A. Data management support for preparing, documenting, and distributing model driver and output data was performed by the Modeling and Synthesis Thematic Data Center at Oak Ridge National Laboratory (MAST-DC; https://nacp.ornl.gov), with funding through NASA ROSES Grant #NNH10AN681. Finalized MsTMIP data products are archived at the ORNL DAAC (https://daac.ornl.gov). We also acknowledge the modeling groups that provided results to MsTMIP. The synthesis of site-level soil respiration, temperature, and moisture data reported in Carey et al 2016a, 2016b) was funded by the US Geological Survey (USGS) John Wesley Powell Center for Analysis and Synthesis Award G13AC00193. Additional support for that work was also provided by the USGS Land Carbon Program. JBF carried out the research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged

Neurodegenerative Disease and the NLRP3 Inflammasome.

The prevalence of neurodegenerative disease has increased significantly in recent years, and with a rapidly aging global population, this trend is expected to continue. These diseases are characterised by a progressive neuronal loss in the brain or peripheral nervous system, and generally involve protein aggregation, as well as metabolic abnormalities and immune dysregulation. Although the vast majority of neurodegeneration is idiopathic, there are many known genetic and environmental triggers. In the past decade, research exploring low-grade systemic inflammation and its impact on the development and progression of neurodegenerative disease has increased. A particular research focus has been whether systemic inflammation arises only as a secondary effect of disease or is also a cause of pathology. The inflammasomes, and more specifically the NLRP3 inflammasome, a crucial component of the innate immune system, is usually activated in response to infection or tissue damage. Dysregulation of the NLRP3 inflammasome has been implicated in the progression of several neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and prion diseases. This review aims to summarise current literature on the role of the NLRP3 inflammasome in the pathogenesis of neurodegenerative diseases, and recent work investigating NLRP3 inflammasome inhibition as a potential future therapy

Managing Big Sagebrush in a Changing Climate

This publication identifies areas where big sagebrush populations are most and least vulnerable to climate change and demonstrates where continued investment in sagebrush conservation and restoration could have the most impact