Nitrate and Vitamin A Interrelationship in Sheep

Several researchers have demonstrated that dietary nitrate reduced liver stores of vitamin A in ruminants. However, it has not been shown whether the effect is due to a more rapid depletion of existing vitamin A stores or to a lowered depatic deposition of the vitamin. Two experiments were conducted to determine the effect of nitrate on plasma vitamin A, hepatic vitamin A and performance of lambs during a growing period (experiment 1) and vitamin A depletion and repletion periods (experiment 2)

Urea-Nitrate Interrelations in Sheep Under Feedlot Conditions

Three experiments were conducted to determine if measurable urea-nitrate interrelationships exist in sheep under feedlot conditions, and the extent of nitrogen utilization from urea and nitrate by sheep fed rations containing suboptimum quantities of protein. Dietary conditions imposed provided for the simultaneous adaptation of lambs to urea and sodium nitrate (experiment 1); an exposure to sodium nitrate without prior adaptation after lambs has been brought to a full feed on a ration containing urea (experiment 2); and the use of soybean meal, urea and sodium nitrate independently as protein supplements to rations providing suboptimum levels of crude protein (experiment 3)

Development of an occupational airborne chemical exposure matrix

Background Population-based studies of the occupational contribution to chronic obstructive pulmonary disease generally rely on self-reported exposures to vapours, gases, dusts and fumes (VGDF), which are susceptible to misclassification. Aims To develop an airborne chemical job exposure matrix (ACE JEM) for use with the UK Standard Occupational Classification (SOC 2000) system. Methods We developed the ACE JEM in stages: (i) agreement of definitions, (ii) a binary assignation of exposed/not exposed to VGDF, fibres or mists (VGDFFiM), for each of the individual 353 SOC codes and (iii) assignation of levels of exposure (L; low, medium and high) and (iv) the proportion of workers (P) likely to be exposed in each code. We then expanded the estimated exposures to include biological dusts, mineral dusts, metals, diesel fumes and asthmagens. \ud Results We assigned 186 (53%) of all SOC codes as exposed to at least one category of VGDFFiM, with 23% assigned as having medium or high exposure. We assigned over 68% of all codes as not being exposed to fibres, gases or mists. The most common exposure was to dusts (22% of codes with >50% exposed); 12% of codes were assigned exposure to fibres. We assigned higher percentages of the codes as exposed to diesel fumes (14%) compared with metals (8%). Conclusions We developed an expert-derived JEM, using a strict set of a priori defined rules. The ACE JEM could also be applied to studies to assess risks of diseases where the main route of occupational exposure is via inhalation

Urea and Nitrate Interrelationships in Sheep Under Feedlot Conditions

Several sources of nonorotein nitrogen, including urea and nitrate, are known to be utilized by rumen microbes for protein synthesis after conversion to a common intermediate, ammonia. This has led to the speculation that the presence of urea may result in a decrease in the utilization of nitrate or its reduction products, thereby increasing the apparent toxicity of nitrate. The experiments reported herein were ccnducted to determine if measurable urea-nitrate interrelationahios exist in sheep under feedlot conditions, and the extent of nitrogen utilization from urea and nitrate by sheep fed rations containing otherwise suboptimum quantities of crude protein

Atrazine and cancer incidence among pesticide applicators in the agricultural health study (1994-2007).

Atrazine is a triazine herbicide used widely in the United States. Although it is an animal carcinogen, the mechanism in rodents does not appear to operate in humans. Few epidemiologic studies have provided evidence for an association

A Bioengineered Implant for a Predetermined Bone Cellular Response to Loading Forces. A Literature Review and Case Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142048/1/jper1276.pd

<i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data

Clustering of cancer among families of cases with Hodgkin Lymphoma (HL), Multiple Myeloma (MM), Non-Hodgkin's Lymphoma (NHL), Soft Tissue Sarcoma (STS) and control subjects

<p>Abstract</p> <p>Background</p> <p>A positive family history of chronic diseases including cancer can be used as an index of genetic and shared environmental influences. The tumours studied have several putative risk factors in common including occupational exposure to certain pesticides and a positive family history of cancer.</p> <p>Methods</p> <p>We conducted population-based studies of Hodgkin lymphoma (HL), Multiple Myeloma (MM), non-Hodgkin's Lymphoma (NHL), and Soft Tissue Sarcoma (STS) among male incident case and control subjects in six Canadian provinces. The postal questionnaire was used to collect personal demographic data, a medical history, a lifetime occupational history, smoking pattern, and the information on family history of cancer. The family history of cancer was restricted to first degree relatives and included relationship to the index subjects and the types of tumours diagnosed among relatives. The information was collected on 1528 cases (HL (n = 316), MM (n = 342), NHL (n = 513), STS (n = 357)) and 1506 age ± 2 years and province of residence matched control subjects. Conditional logistic regression analyses adjusted for the matching variables were conducted.</p> <p>Results</p> <p>We found that most families were cancer free, and a minority included two or more affected relatives. HL [(OR_adj(95% CI) <b>1.79 (1.33, 2.42)]</b>, MM <b>(1.38(1.07, 1.78))</b>, NHL <b>(1.43 (1.15, 1.77)</b>), and STS cases <b>(1.30(1.00, 1.68)) </b>had higher incidence of cancer if any first degree relative was affected with cancer compared to control families. Constructing mutually exclusive categories combining "family history of cancer" (yes, no) and "pesticide exposure ≥10 hours per year" (yes, no) indicated that a positive family history was important for HL <b>(2.25(1.61, 3.15))</b>, and for the combination of the two exposures increased risk for MM <b>(1.69(1.14,2.51))</b>. Also, a positive family history of cancer both with <b>(1.72 (1.21, 2.45)) </b>and without pesticide exposure <b>(1.43(1.12, 1.83)) </b>increased risk of NHL.</p> <p>Conclusion</p> <p>HL, MM, NHL, and STS cases had higher incidence of cancer if any first degree relative affected with cancer compared to control families. A positive family history of cancer and/or shared environmental exposure to agricultural chemicals play an important role in the development of cancer.</p