Wireless technologies for the construction sector—Requirements, energy and cost efficiencies

The construction sector has been rather reluctant with respect to the implementation of ITC innovations that other industries have adopted for years. One of the reasons could be the lack of services by the proposed innovations especially the RFID solutions. This technology is well‐researched within the building sector and is therefore used to analyse requirements for alternative technologies. The motivationof the current work is to find upcoming technologies that bring improvements into the sector, for example improved life cycle costs and energy efficiencies, increasing quality, construction and operation efficiency and reducing faults and losses.The paper also lays out requirements expected by the sector. It will be shown that the wireless sensor network technology is a strong competitor that may meet the requirements. By analysing the application of such technologies throughout the building lifecycle, the utilization can be manifold, hereby minimising overall economic costs and maximising the added values for all involved stakeholders.Based on the expectations of the sector, the experiences with the introduction of the RFID technology and by estimating the applicability of the extra services that follow the wireless sensor network, the paper will line up the requirements that the new technology has to meet to be introduced successfully

Prospective analysis of circulating metabolites and breast cancer in EPIC

Abstract Background Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk. Methods A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression. Results Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70–0.90), asparagine (OR = 0.83 (0.74–0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76–0.90)), aa C36:3 (OR = 0.84 (0.77–0.93)), ae C34:2 (OR = 0.85 (0.78–0.94)), ae C36:2 (OR = 0.85 (0.78–0.88)), and ae C38:2 (OR = 0.84 (0.76–0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11–1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06–1.24)) and PC ae C36:3 (OR = 0.88 (0.82–0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity. Conclusions These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies

Additional file 1: of Prospective analysis of circulating metabolites and breast cancer in EPIC

Supplementary tables describing the completeness of the metabolites measures (Table S1.) and metabolites concentrations by case-control status (Table S2..); Supplementary figures showing age-adjusted correlations between metabolites in control participants (Figure S1.) and adjusted P values for associations between metabolites and different breast cancer subtypes (Figure S2.). Abbreviations: BMI: Body Mass Index; EPIC: European Prospective Investigation into Cancer and nutrition; ER: estrogen receptor; FDR: False Discovery Rate; HER2: Human epidermal growth factor receptor 2; IARC: International Agency for Research on Cancer; MHT: menopause hormone therapy; MS: Mass spectrometry; NMR: nuclear magnetic resonance; OR: odds ratio; PC: phosphatidylcholine; PR: progesterone receptor; SD: standard deviation; WC: waist circumference. (PDF 1041 kb

Prospective analysis of circulating metabolites and breast cancer in EPIC

Abstract Background Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk. Methods A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression. Results Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70–0.90), asparagine (OR = 0.83 (0.74–0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76–0.90)), aa C36:3 (OR = 0.84 (0.77–0.93)), ae C34:2 (OR = 0.85 (0.78–0.94)), ae C36:2 (OR = 0.85 (0.78–0.88)), and ae C38:2 (OR = 0.84 (0.76–0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11–1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06–1.24)) and PC ae C36:3 (OR = 0.88 (0.82–0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity. Conclusions These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies

EVS Trend File 1981-2017 – Sensitive Dataset

The European Values Study is a large-scale, cross-national and longitudinal survey research program on how Europeans think about family, work, religion, politics, and society. Repeated every nine years in an increasing number of countries, the survey provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens all over Europe. The EVS Trend File 1981-2017 is constructed from the five EVS waves and covers almost 40 years. In altogether 160 surveys, more than 224.000 respondents from 48 countries/regions were interviewed. It is based on the updated data of the EVS Longitudinal Data File 1981-2008 (v.3.1.0) and the current EVS 2017 Integrated Dataset (v.5.0.0). For the EVS Trend File, a Restricted-Use File (ZA7504) is available in addition to the (factually anonymised) Scientific-Use File (ZA7503). The EVS Trend File – Sensitive Dataset (ZA7504) is provided as an add-on file. In addition to a small set of admin and protocol variables needed to merge with the SUF data, the Sensitive Dataset contains the following variables that could not be included in the scientific-use file due to their sensitive nature: W005_3 Job profession/industry (3-digit ISCO88) - spouse/partner EVS 2008W005_3_01 Job profession/industry (3-digit ISCO08) - spouse/partner EVS 2017W005_4 Job profession/industry (4-digit ISCO88) - spouse/partner EVS 2008X035_3 Job profession/industry (3-digit ISCO88) – respondent EVS 1999, EVS 2008 X035_3_01 Job profession/industry (3-digit ISCO08) - respondent EVS 2017X035_4 Job profession/industry (4-digit ISCO88) – respondent EVS 1999, EVS 2008 x048c_n3 Region where the interview was conducted (NUTS-3): NUTS version 2006 EVS 2008X048J_N3 Region where the interview was conducted (NUTS-3): NUTS version 2016 EVS 2017X049 Size of town (8 categories) EVS 2008, EVS 2017 Detailed information on the anonymization process in the EVS Trend File is provided in the EVS Trend File Variable Report