Decadal re-evaluation of contaminant exposure and productivity of ospreys (Pandion haliaetus) nesting in Chesapeake Bay Regions of Concern

The last large-scale ecotoxicological study of ospreys (Pandion haliaetus) in Chesapeake Bay was conducted in 2000-2001 and focused on U.S. EPA-designated Regions of Concern (ROCs; Baltimore Harbor/Patapsco, Anacostia/middle Potomac, and Elizabeth Rivers). In 2011-2012, ROCs were re-evaluated to determine spatial and temporal trends in productivity and contaminants. Concentrations of p,p\u27-DDE were low in eggs and below the threshold associated with eggshell thinning. Eggs from the Anacostia/middle Potomac Rivers had lower total PCB concentrations in 2011 than in 2000; however, concentrations remained unchanged in Baltimore Harbor. Polybrominated diphenyl ether flame retardants declined by 40%, and five alternative brominated flame retardants were detected at low levels. Osprey productivity was adequate to sustain local populations, and there was no relation between productivity and halogenated contaminants. Our findings document continued recovery of the osprey population, declining levels of many persistent halogenated compounds, and modest evidence of genetic damage in nestlings from industrialized regions

Assessing the Potential of Classical Q-learning in General Game Playing

After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game playing. However, deep learning is resource-intensive and the theory is not yet well developed. For small games, simple classical table-based Q-learning might still be the algorithm of choice. General Game Playing (GGP) provides a good testbed for reinforcement learning to research AGI. Q-learning is one of the canonical reinforcement learning methods, and has been used by (Banerjee $\&$ Stone, IJCAI 2007) in GGP. In this paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to allow comparison to Banerjee et al.. We find that Q-learning converges to a high win rate in GGP. For the $\epsilon$-greedy strategy, we propose a first enhancement, the dynamic $\epsilon$ algorithm. In addition, inspired by (Gelly $\&$ Silver, ICML 2007) we combine online search (Monte Carlo Search) to enhance offline learning, and propose QM-learning for GGP. Both enhancements improve the performance of classical Q-learning. In this work, GGP allows us to show, if augmented by appropriate enhancements, that classical table-based Q-learning can perform well in small games.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0594

Issues in modern bone histomorphometry

This review reports on proceedings of a bone histomorphometry session conducted at the Fortieth International IBMS Sun Valley Skeletal Tissue Biology Workshop held on August 1, 2010. The session was prompted by recent technical problems encountered in conducting histomorphometry on bone biopsies from humans and animals treated with anti-remodeling agents such as bisphosphonates and RANKL antibodies. These agents reduce remodeling substantially, and thus cause problems in calculating bone remodeling dynamics using in vivo fluorochrome labeling. The tissue specimens often contain few or no fluorochrome labels, and thus create statistical and other problems in analyzing variables such as mineral apposition rates, mineralizing surface and bone formation rates. The conference attendees discussed these problems and their resolutions, and the proceedings reported here summarize their discussions and recommendations

Interaction of Stress, Lead Burden, and Age on Cognition in Older Men: The VA Normative Aging Study

BACKGROUND. Low-level exposure to lead and to chronic stress may independently influence cognition. However, the modifying potential of psychosocial stress on the neurotoxicity of lead and their combined relationship to aging-associated decline have not been fully examined. OBJECTIVES. We examined the cross-sectional interaction between stress and lead exposure on Mini-Mental State Examination (MMSE) scores among 811 participants in the Normative Aging Study, a cohort of older U.S. men. METHODS. We used two self-reported measures of stress appraisal-a self-report of stress related to their most severe problem and the Perceived Stress Scale (PSS). Indices of lead exposure were blood lead and bone (tibia and patella) lead. RESULTS. Participants with higher self-reported stress had lower MMSE scores, which were adjusted for age, education, computer experience, English as a first language, smoking, and alcohol intake. In multivariable-adjusted tests for interaction, those with higher PSS scores had a 0.57-point lower (95% confidence interval, -0.90 to 0.24) MMSE score for a 2-fold increase in blood lead than did those with lower PSS scores. In addition, the combination of high PSS scores and high blood lead categories on one or both was associated with a 0.05-0.08 reduction on the MMSE for each year of age compared with those with low PSS score and blood lead level (p < 0.05). CONCLUSIONS. Psychological stress had an independent inverse association with cognition and also modified the relationship between lead exposure and cognitive performance among older men. Furthermore, high stress and lead together modified the association between age and cognition.National Institutes of Health (R01ES07821, R01HL080674, R01HL080674-02S1, R01ES013744, ES05257-06A1, P20MD000501, P42ES05947, ES03918-02); National Center for Research Resources General Clinical Research Center (M01RR02635); Leaves of Grass Foundation; United States Department of Veterans Affair

Aryl hydrocarbon receptor is required for optimal B-cell proliferation

The aryl hydrocarbon receptor (AhR), a transcription factor known for mediating xenobiotic toxicity, is expressed in B cells, which are known targets for environmental pollutants. However, it is unclear what the physiological functions of AhR in B cells are. We show here that expression of Ahr in B cells is up‐regulated upon B‐cell receptor (BCR) engagement and IL‐4 treatment. Addition of a natural ligand of AhR, FICZ, induces AhR translocation to the nucleus and transcription of the AhR target gene Cyp1a1, showing that the AhR pathway is functional in B cells. AhR‐deficient (Ahr (−/−)) B cells proliferate less than AhR‐sufficient (Ahr (+/+)) cells following in vitro BCR stimulation and in vivo adoptive transfer models confirmed that Ahr (−/−) B cells are outcompeted by Ahr (+/+) cells. Transcriptome comparison of AhR‐deficient and AhR‐sufficient B cells identified cyclin O (Ccno), a direct target of AhR, as a top candidate affected by AhR deficiency

The JSpecView Project: an Open Source Java viewer and converter for JCAMP-DX, and XML spectral data files

The JSpecView Open Source project began with the intention of providing both a teaching and research tool for the display of JCAMP-DX spectra. The development of the Java source code commenced under license in 2001 and was released as Open Source in March 2006. The scope was then broadened to take advantage of the XML initiative in Chemistry and routines to read and write AnIML and CMLspect documents were added

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/

The Integrated Behavioural Model for Water, Sanitation, and Hygiene: a systematic review of behavioural models and a framework for designing and evaluating behaviour change interventions in infrastructure-restricted settings.

BACKGROUND: Promotion and provision of low-cost technologies that enable improved water, sanitation, and hygiene (WASH) practices are seen as viable solutions for reducing high rates of morbidity and mortality due to enteric illnesses in low-income countries. A number of theoretical models, explanatory frameworks, and decision-making models have emerged which attempt to guide behaviour change interventions related to WASH. The design and evaluation of such interventions would benefit from a synthesis of this body of theory informing WASH behaviour change and maintenance. METHODS: We completed a systematic review of existing models and frameworks through a search of related articles available in PubMed and in the grey literature. Information on the organization of behavioural determinants was extracted from the references that fulfilled the selection criteria and synthesized. Results from this synthesis were combined with other relevant literature, and from feedback through concurrent formative and pilot research conducted in the context of two cluster-randomized trials on the efficacy of WASH behaviour change interventions to inform the development of a framework to guide the development and evaluation of WASH interventions: the Integrated Behavioural Model for Water, Sanitation, and Hygiene (IBM-WASH). RESULTS: We identified 15 WASH-specific theoretical models, behaviour change frameworks, or programmatic models, of which 9 addressed our review questions. Existing models under-represented the potential role of technology in influencing behavioural outcomes, focused on individual-level behavioural determinants, and had largely ignored the role of the physical and natural environment. IBM-WASH attempts to correct this by acknowledging three dimensions (Contextual Factors, Psychosocial Factors, and Technology Factors) that operate on five-levels (structural, community, household, individual, and habitual). CONCLUSIONS: A number of WASH-specific models and frameworks exist, yet with some limitations. The IBM-WASH model aims to provide both a conceptual and practical tool for improving our understanding and evaluation of the multi-level multi-dimensional factors that influence water, sanitation, and hygiene practices in infrastructure-constrained settings. We outline future applications of our proposed model as well as future research priorities needed to advance our understanding of the sustained adoption of water, sanitation, and hygiene technologies and practices

The Canadian Joint Replacement Registry—what have we learned?

The Canadian Joint Replacement Registry (CJRR) was launched in 2000 through the collaborative efforts of the Canadian Orthopedic Association and the Canadian Institutes for Health Information. Participation is voluntary, and data collected by participating surgeons in the operating room is linked to hospital stay information from administrative databases to compile yearly reports. In the fiscal year 2006–2007, there were 62,196 hospitalizations for hip and knee replacements in Canada, excluding Quebec. This represents a 10-year increase of 101% and a 1-year increase of 6%. Compared to men, Canadian women have higher age-adjusted rates per 105 for both TKA (148 vs. 110) and THA (86 vs. 76). There also exist substantial inter-provincial variations in both age-adjusted rates of arthroplasty and implant utilization that cannot be explained entirely on the basis of differing patient demographics. The reasons for these variations are unclear, but probably represent such factors as differences in provincial health expenditure, efforts to reduce waiting lists, and surgeon preference. The main challenge currently facing the CJRR is to increase procedure capture to > 90%. This is being pursued through a combination of efforts including simplification of the consent process, streamlining of the data collection form, and the production of customized reports with information that has direct clinical relevance for surgeons and administrators. As the CJRR continues to mature, we are optimistic that it will provide clinically important information on the wide range of factors that affect arthroplasty outcome