Behaviour of Cold-Formed Steel Semi Rigid Connections

Ductility and inelastic performance are important considerations in aseismic design of buildings. The dissipation of energy due to inelastic deformation is predominantly required in the connections like beam column joints. It is necessary to design these joints as semi rigid for its economic and structural benefits. Semi-rigid connections have highly nonlinear behaviour that makes the analysis and design of frames difficult and complicated. Steel structures are highly regarded for their seismic performance. It is required to understand and study the inelastic behavior of steel connections which would help in an economical and simpler design. This paper involves the modeling of deformational behaviour of a cold formed steel connection in a finite element software simulating the real time behavior. The ultimate moment and rotation is studied for different semi rigid connections after validation of the model

Environmental toxins and breast cancer on Long Island. I. Polycyclic aromatic hydrocarbon DNA adducts

Polycyclic aromatic hydrocarbons (PAH) are potent mammary carcinogens in rodents, but their effect on breast cancer development in women is not clear. To examine whether currently measurable PAH damage to DNA increases breast cancer risk, a population-based case-control study was undertaken on Long Island, NY. Cases were women newly diagnosed with in situ and invasive breast cancer; controls were randomly selected women frequency matched to the age distribution of cases. Blood samples were donated by 1102 (73.0%) and 1141 (73.3%) of case and control respondents, respectively. Samples from 576 cases and 427 controls were assayed for PAH-DNA adducts using an ELISA. The geometric mean (and geometric SD) of the log-transformed levels of PAH-DNA adducts on a natural scale was slightly, but nonsignificantly, higher among cases [7.36 (7.29)] than among controls [6.21 (4.17); p = 0.51]. The age-adjusted odds ratio (OR) for breast cancer in relation to the highest quintile of adduct levels compared with the lowest was 1.51 [95% confidence interval (CI), 1.04 –2.20], with little or no evidence of substantial confounding (corresponding multivariate-adjusted OR, 1.49; 95% CI, 1.00 –2.21). There was no consistent elevation in risk with increasing adduct levels, nor was there a consistent association between adduct levels and two of the main sources of PAH, active or passive cigarette smoking or consumption of grilled and smoked foods. These data indicate that PAH-DNA adduct formation may influence breast cancer development, although the association does not appear to be dose dependent and may have a threshold effect

The Long Island Breast Cancer Study Project: description of a multi-institutional collaboration to identify environmental risk factors for breast cancer

The Long Island Breast Cancer Study Project is a federally mandated, population-based case-control study to determine whether breast cancer risk among women in the counties of Nassau and Suffolk, NY, is associated with selected environmental exposures, assessed by blood samples, self-reports, and environmental home samples. This report describes the collaborative project’s background, rationale, methods, participation rates, and distributions of known risk factors for breast cancer by case-control status, by blood donation, and by availability of environmental home samples. Interview response rates among eligible cases and controls were 82.1% (n=1,508) and 62.8% (n=1,556), respectively. Among case and control respondents who completed the interviewer-administered questionnaire, 98.2 and 97.6% self-completed the food frequency questionnaire; 73.0 and 73.3% donated a blood sample; and 93.0 and 83.3% donated a urine sample. Among a random sample of case and control respondents who are long-term residents, samples of dust (83.6 and 83.0%); soil (93.5 and 89.7%); and water (94.3 and 93.9%) were collected. Established risk factors for breast cancer that were found to increase risk among Long Island women include lower parity, late age at first birth, little or no breast feeding, and family history of breast cancer. Factors that were found to be associated with a decreased likelihood that a respondent would donate blood include increasing age and past smoking; factors associated with an increased probability include white or other race, alcohol use, ever breastfed, ever use of hormone replacement therapy, ever use of oral contraceptives, and ever had a mammogram. Long-term residents (defined as 15+ years in the interview home) with environmental home samples did not differ from other long-term residents, although there were a number of differences in risk factor distributions between long-term residents and other participants, as anticipated

Environmental toxins and breast cancer on Long Island. II. Organochlorine compound levels in blood

Whether environmental contaminants increase breast cancer risk among women on Long Island, NY, is unknown. The study objective is to determine whether breast cancer risk is increased in relation to organochlorines, compounds with known estrogenic characteristics that were extensively used on Long Island and other areas of the United States. Recent reports do not support a strong association, although there are concerns with high risks observed in subgroups of women. Blood samples from 646 case and 429 control women from a population-based case-control study conducted on Long Island were analyzed. No substantial elevation in breast cancer risk was observed in relation to the highest quintile of lipid-adjusted serum levels of p,p'-bis(4-chlorophenyl)-1,1-dichloroethene (DDE) [odds ratio (OR), 1.20 versus lowest quintile; 95% confidence interval (CI), 0.76 –1.90], chlordane (OR, 0.98; 95% CI, 0.62–1.55), dieldrin (OR, 1.37; 95% CI, 0.69 –2.72), the sum of the four most frequently occurring PCB congeners (nos. 118, 153, 138, and 180; OR, 0.83; 95% CI, 0.54 –1.29), and other PCB congener groupings. No dose-response relations were apparent. Nor was risk increased in relation to organochlorines among women who had not breastfed or were overweight, postmenopausal, or long-term residents of Long Island; or with whether the case was diagnosed with invasive rather than in situ disease, or with a hormone receptor-positive tumor. These findings, based on the largest number of samples analyzed to date among primarily white women, do not support the hypothesis that organochlorines increase breast cancer risk among Long Island women

AusTraits: a curated plant trait database for the Australian flora

INTRODUCTION AusTraits is a transformative database, containing measurements on the traits of Australia’s plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 250 distinct sources, describing > 400 plant traits and > 26,000 taxa. To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project’s GitHub repository, http://traitecoevo.github.io/austraits.build/. Further information on the project is available in the associated publication and at the project website austraits.org. Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 CONTRIBUTORS The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University. We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment, Land, Water and Planning, Victoria. AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their “Transformative data collections” (https://doi.org/10.47486/TD044) and “Data Partnerships” (https://doi.org/10.47486/DP720) programs; fellowship grants from Australian Research Council to Falster (FT160100113), Gallagher (DE170100208) and Wright (FT100100910), a grant from Macquarie University to Gallagher. The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS). ACCESSING AND USE OF DATA The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community. A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors: Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 In addition, we encourage users you to cite the original data sources, wherever possible. Note that under the license data may be redistributed, provided the attribution is maintained. The downloads below provide the data in two formats: austraits-3.0.2.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python. austraits-3.0.2.rds: data as compressed R object. Suitable for users of R (see below). Both objects contain all the data and relevant meta-data. AUSTRAITS R PACKAGE For R users, access and manipulation of data is assisted with the austraits R package. The package can both download data and provides examples and functions for running queries. STRUCTURE OF AUSTRAITS The compiled AusTraits database has the following main components: austraits ├── traits ├── sites ├── contexts ├── methods ├── excluded_data ├── taxanomic_updates ├── taxa ├── definitions ├── contributors ├── sources └── build_info These elements include all the data and contextual information submitted with each contributed datasets. A schema and definitions for the database are given in the file/component definitions, available within the download. The file dictionary.html provides the same information in textual format. Full details on each of these components and columns are contained within the definition. Similar information is available at http://traitecoevo.github.io/austraits.build/articles/Trait_definitions.html and http://traitecoevo.github.io/austraits.build/articles/austraits_database_structure.html. CONTRIBUTING We envision AusTraits as an on-going collaborative community resource that: Increases our collective understanding the Australian flora; and Facilitates accumulation and sharing of trait data; Builds a sense of community among contributors and users; and Aspires to fully transparent and reproducible research of the highest standard. As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at traitecoevo/austraits.build. Here are some of the ways you can contribute: Reporting Errors: If you notice a possible error in AusTraits, please post an issue on GitHub. Refining documentation: We welcome additions and edits that make using the existing data or adding new data easier for the community. Contributing new data: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at http://traitecoevo.github.io/austraits.build/articles/contributing_data.html

AusTraits, a curated plant trait database for the Australian flora

International audienceWe introduce the austraits database-a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual-and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge