Oxidative stress and inflammation induced by environmental and psychological stressors: a biomarker perspective

Significance. The environment can elicit biological responses such as oxidative stress (OS) and inflammation as consequence of chemical, physical or psychological changes. As population studies are essential for establishing these environment-organism interactions, biomarkers of oxidative stress or inflammation are critical in formulating mechanistic hypotheses. Recent advances. By using examples of stress induced by various mechanisms, we focus on the biomarkers that have been used to assess oxidative stress and inflammation in these conditions. We discuss the difference between biomarkers that are the result of a chemical reaction (such as lipid peroxides or oxidized proteins that are a result of the reaction of molecules with reactive oxygen species, ROS) and those that represent the biological response to stress, such as the transcription factor NRF2 or inflammation and inflammatory cytokines. Critical issues. The high-throughput and holistic approaches to biomarker discovery used extensively in large-scale molecular epidemiological exposome are also discussed in the context of human exposure to environmental stressors. Future directions. We propose to consider the role of biomarkers as signs and distinguish between signs that are just indicators of biological processes and proxies that one can interact with and modify the disease process

Iodine status in western Kenya: a community-based cross-sectional survey of urinary and drinking water iodine concentrations

Spot urinary iodine concentrations (UIC) are presented for 248 individuals from western Kenya with paired drinking water collected between 2016 and 2018. The median UIC was 271 µg L−1, ranging from 9 to 3146 µg L−1, unadjusted for hydration status/dilution. From these data, 12% were potentially iodine deficient ( 300 µg L−1). The application of hydration status/urinary dilution correction methods was evaluated for UICs, using creatinine, osmolality and specific gravity. The use of specific gravity correction for spot urine samples to account for hydration status/urinary dilution presents a practical approach for studies with limited budgets, rather than relying on unadjusted UICs, 24 h sampling, use of significantly large sample size in a cross-sectional study and other reported measures to smooth out the urinary dilution effect. Urinary corrections did influence boundary assessment for deficiency–sufficiency–excess for this group of participants, ranging from 31 to 44% having excess iodine intake, albeit for a study of this size. However, comparison of the correction methods did highlight that 22% of the variation in UICs was due to urinary dilution, highlighting the need for such correction, although creatinine performed poorly, yet specific gravity as a low-cost method was comparable to osmolality corrections as the often stated ‘gold standard’ metric for urinary concentration. Paired drinking water samples contained a median iodine concentration of 3.2 µg L−1 (0.2–304.1 µg L−1). A weak correlation was observed between UIC and water-I concentrations (R = 0.11)

HMM based scenario generation for an investment optimisation problem

This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2012 Springer-Verlag.The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented.This study was funded by NET ACE at OptiRisk Systems

Patient-doctor continuity and diagnosis of cancer:electronic medical records study in general practice

BACKGROUND: Continuity of care may affect the diagnostic process in cancer but there is little research. AIM: To estimate associations between patient–doctor continuity and time to diagnosis and referral of three common cancers. DESIGN AND SETTING: Retrospective cohort study in general practices in England. METHOD: This study used data from the General Practice Research Database for patients aged ≥40 years with a diagnosis of breast, colorectal, or lung cancer. Relevant cancer symptoms or signs were identified up to 12 months before diagnosis. Patient–doctor continuity (fraction-of-care index adjusted for number of consultations) was calculated up to 24 months before diagnosis. Time ratios (TRs) were estimated using accelerated failure time regression models. RESULTS: Patient–doctor continuity in the 24 months before diagnosis was associated with a slightly later diagnosis of colorectal (time ratio [TR] 1.01, 95% confidence interval [CI] =1.01 to 1.02) but not breast (TR = 1.00, 0.99 to 1.01) or lung cancer (TR = 1.00, 0.99 to 1.00). Secondary analyses suggested that for colorectal and lung cancer, continuity of doctor before the index consultation was associated with a later diagnosis but continuity after the index consultation was associated with an earlier diagnosis, with no such effects for breast cancer. For all three cancers, most of the delay to diagnosis occurred after referral. CONCLUSION: Any effect for patient–doctor continuity appears to be small. Future studies should compare investigations, referrals, and diagnoses in patients with and without cancer who present with possible cancer symptoms or signs; and focus on ‘difficult to diagnose’ types of cancer

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping is a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms such as random forest (RF) models have been developed for such tasks as they are capable of modelling non-linear relationships using a range of datasets and determining the importance of predictor variables, offering multiple benefits to traditional techniques such as kriging. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with auxiliary environmental covariates. The model was applied to predict the total concentration (mg kg-1 ) of 56 elements, soil pH and organic matter content, as well as to assess prediction uncertainty using 466 soil samples in western Kenya (Watts et al 2021). The results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work due to their contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH was assessed as an important parameter to define soil chemical reactions. Algorithm performance was evaluated to determine the importance of each predictor variable and the model’s response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating the out-of-bag predicted values. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of auxiliary values did improve model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agricommunity utilise the geochemical maps presented here, and the webtool, for assessing the relationship between environmental geochemistry and endemic diseases and preventable micronutrient deficiency

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping techniques represent a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms using random forest (RF) models have been developed for classification, pattern recognition and regression tasks, they are capable of modelling non-linear relationships using a range of datasets, identifying hierarchical relationships, and determining the importance of predictor variables. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with ancillary environmental covariates. The model was applied to predict the total concentration (mg kg−1) and assess the prediction uncertainty of 56 elements, soil pH and organic matter content using 466 soil samples in western Kenya; the results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work. These elements were selected due to contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH is an important parameter controlling soil chemical reactions. Algorithm performance was evaluated determining the relative importance of each predictor variable and the model's response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating out-of-bag predicted values. The models R2 values range from 0.31 to 0.64 whilst CCC values range from 0.51 to 0.77. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of ancillary environmental data improved model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agri-community utilise the geochemical maps presented here for assessing the relationship between environmental geochemistry, endemic diseases and preventable micronutrient deficiency

Financial considerations in the conduct of multi-centre randomised controlled trials: evidence from a qualitative study.

National Coordinating Centre for Research Methodology; Medical Research Council, UK Department of Health; Chief Scientist OfficeNot peer reviewedPublisher PD

The Baryon Oscillation Spectroscopic Survey of SDSS-III

The Baryon Oscillation Spectroscopic Survey (BOSS) is designed to measure the scale of baryon acoustic oscillations (BAO) in the clustering of matter over a larger volume than the combined efforts of all previous spectroscopic surveys of large scale structure. BOSS uses 1.5 million luminous galaxies as faint as i=19.9 over 10,000 square degrees to measure BAO to redshifts z<0.7. Observations of neutral hydrogen in the Lyman alpha forest in more than 150,000 quasar spectra (g<22) will constrain BAO over the redshift range 2.15<z<3.5. Early results from BOSS include the first detection of the large-scale three-dimensional clustering of the Lyman alpha forest and a strong detection from the Data Release 9 data set of the BAO in the clustering of massive galaxies at an effective redshift z = 0.57. We project that BOSS will yield measurements of the angular diameter distance D_A to an accuracy of 1.0% at redshifts z=0.3 and z=0.57 and measurements of H(z) to 1.8% and 1.7% at the same redshifts. Forecasts for Lyman alpha forest constraints predict a measurement of an overall dilation factor that scales the highly degenerate D_A(z) and H^{-1}(z) parameters to an accuracy of 1.9% at z~2.5 when the survey is complete. Here, we provide an overview of the selection of spectroscopic targets, planning of observations, and analysis of data and data quality of BOSS.Comment: 49 pages, 16 figures, accepted by A

Construction of reference chromosome-scale pseudomolecules for potato: integrating the potato genome with genetic and physical maps

The genome of potato, a major global food crop, was recently sequenced. The work presented here details the integration of the potato reference genome (DM) with a new STS marker based linkage map and other physical and genetic maps of potato and the closely related species tomato. Primary anchoring of the DM genome assembly was accomplished using a diploid segregating population, which was genotyped with several types of molecular genetic markers to construct a new ~936 cM linkage map comprising 2,469 marker loci. In silico anchoring approaches employed genetic and physical maps from the diploid potato genotype RH and tomato. This combined approach has allowed 951 superscaffolds to be ordered into pseudomolecules corresponding to the 12 potato chromosomes. These pseudomolecules represent 674 Mb (~93%) of the 723 Mb genome assembly and 37,482 (~96%) of the 39,031 predicted genes. The superscaffold order and orientation within the pseudomolecules is closely collinear with independently constructed high density linkage maps. Comparisons between marker distribution and physical location reveal regions of greater and lesser recombination, as well as regions exhibiting significant segregation distortion. The work presented here has led to a greatly improved ordering of the potato reference genome superscaffolds into chromosomal 'pseudomolecules'.Fil: Carboni, Martín Federico. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires. Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: D'ambrosio, Juan Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional San Cristobal de Huamanga. Laboratorio de Genética y Biotecnología Vegetal; PerúFil: Sharma, Sanjeev Kumar. The James Hutton Institute; Reino UnidoFil: Bolser, Daniel. University of Dundee; Reino UnidoFil: de Boer, Jan. Wageningen University & Researc; Países BajosFil: Sønderkær, Mads . Aalborg University; DinamarcaFil: Amoros, Walter. International Potato Center; PerúFil: de la Cruz, Germán. Universidad Nacional San Cristobal de Huamanga; PerúFil: Di Genova, Alex. Universidad de Chile; ChileFil: Douches, David S.. Michigan State University; Estados UnidosFil: Eguiluz, Maria. Universidad Peruana Cayetano Heredia; PerúFil: Guo, Xiao. Shandong Academy of Agricultural Sciences; ChinaFil: Guzman, Frank. Universidad Peruana Cayetano Heredia; PerúFil: Hackett, Christine A.. Biomathematics and Statistics Scotland; Reino UnidoFil: Hamilton, John P.. Crops Environment and Land Use Programme; IrlandaFil: Li, Guangcun. Shandong Academy of Agricultural Sciences; ChinaFil: Li, Ying. The New Zealand Institute for Plant & Food Research; Nueva ZelandaFil: Lozano, Roberto. Universidad Peruana Cayetano Heredia; PerúFil: Maass, Alejandro. Universidad de Chile; ChileFil: Marshall, David. The James Hutton Institute; Reino UnidoFil: Martinez, Diana. Universidad Peruana Cayetano Heredia; PerúFil: McLean, Karen. The James Hutton Institute; Reino UnidoFil: Mejía, Nilo. Instituto de Investigaciones Agropecuarias. Centro Regional de Investigación La Platina; ChileFil: Milne, Linda. The James Hutton Institute; Reino UnidoFil: Munive, Susan. International Potato Center; PerúFil: Nagy, Istvan. Crops Environment and Land Use Programme; IrlandaFil: Ponce, Olga. Universidad Peruana Cayetano Heredia; PerúFil: Ramirez, Manuel. Universidad Peruana Cayetano Heredia; PerúFil: Simon, Reinhard. International Potato Center; PerúFil: Thomson, Susan J.. Chinese Academy of Agricultural Sciences; Chin

Quality of Nonmetastatic Colorectal Cancer Care in the Department of Veterans Affairs

The Veterans Affairs (VA) healthcare system treats approximately 3% of patients with cancer in the United States each year. We measured the quality of nonmetastatic colorectal cancer (CRC) care in VA as indicated by concordance with National Comprehensive Cancer Network practice guidelines (six indicators) and timeliness of care (three indicators)