Sugar beet investigations in Ohio in 1900

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Pulsed magmatic-fluid release for the formation of porphyry deposits: tracing fluid evolution in absolute time from the Tibetan Qulong Cu-Mo deposit

The magmatic-hydrothermal evolution of porphyry-style mineralization in the shallow crust that is linked to magmatic processes at depth has been extensively studied using bulk-sample isotopic analysis combined with relative timing constraints. However, a lack of evaluation of the fluid evolution process against an absolute time frame limits further understanding of the ore-forming process. Here, we quantify the fluid evolution process within an absolute time frame for the first time by integrating new in situ oxygen isotope data from the Qulong porphyry Cu-Mo deposit (Tibet) with existing fluid inclusion data and high-precision Re-Os dates of co-precipitated hydrothermal quartz and molybdenite, respectively. We demonstrate that vein quartz records primary oxygen isotopic compositions and reached oxygen isotope equilibrium with ore-forming fluids, and therefore is an archive of the isotopic composition and source of the ore-forming fluids. The δ18Oquartz and δ18Ofluid values, in absolute time, show periodic fluctuations that indicate the presence of three intermittent pulses of magmatic fluid flux, which have been balanced by meteoric water. As such, the flux of magmatic fluid during ore formation was pulsed, rather than being continuous. The overall highest δ18Ofluid in the first pulse of mineralization, with a gradual decrease to the second and third pulses, is suggestive of a progressive reduction in the magmatic component of the hydrothermal fluids and, by inference, the mineralizing potential of the hydrothermal fluids. This view is supported by a decrease in sulfide-bearing fluid inclusions and metal grade through time. Our findings favor multiple fluid-release events from a single cooling magmatic reservoir, although multiple fluid-melt recharge events remain a competitive alternative. An additional implication is that the magmatic reservoir may have a lifespan of hundreds of thousands of years, with fluid release events occurring over tens of thousands of years

Comparison of data on Mutation Frequencies of Mice Caused by Radiation - Low Dose Model -

We propose LD(Low Dose) model, the extension of LDM model which was proposed in the previous paper [Y. Manabe et al.: J. Phys. Soc. Jpn. 81 (2012) 104004] to estimate biological damage caused by irradiation. LD model takes account of all the considerable effects including cell death effect as well as proliferation, apoptosis, repair. As a typical example of estimation, we apply LD model to the experiment of mutation frequency on the responses induced by the exposure to low levels of ionizing radiation. The most famous and extensive experiments are those summarized by Russell and Kelly [Russell, W. L. & Kelly, E. M: Proc. Natl Acad. Sci. USA 79 (1982) 539-541], which are known as 'Mega-mouse project'. This provides us with important information of the frequencies of transmitted specific-locus mutations induced in mouse spermatogonia stem-cells. It is found that the numerical results of the mutation frequency of mice are in reasonable agreement with the experimental data: the LD model reproduces the total dose and dose rate dependence of data reasonably. In order to see such dose-rate dependence more explicitly, we introduce the dose-rate effectiveness factor (DREF). This represents a sort of preventable effects such as repair, apoptosis and death of broken cells, which are to be competitive with proliferation effect of broken cells induced by irradiation.Comment: subimitting to J. Phys. Soc. Jpn, 32 pages, 8 figure

Soil erosion assessment—Mind the gap

Accurate assessment of erosion rates remains an elusive problem because soil loss is strongly nonunique with respect to the main drivers. In addressing the mechanistic causes of erosion responses, we discriminate between macroscale effects of external factors—long studied and referred to as “geomorphic external variability”, and microscale effects, introduced as “geomorphic internal variability.” The latter source of erosion variations represents the knowledge gap, an overlooked but vital element of geomorphic response, significantly impacting the low predictability skill of deterministic models at field‐catchment scales. This is corroborated with experiments using a comprehensive physical model that dynamically updates the soil mass and particle composition. As complete knowledge of microscale conditions for arbitrary location and time is infeasible, we propose that new predictive frameworks of soil erosion should embed stochastic components in deterministic assessments of external and internal types of geomorphic variability.Key PointsSoil loss response to runoff is strongly controlled by “geomorphic internal variability”: microscale factors intrinsic to geomorphic systemPredictive skill of deterministic soil loss models at event scale is likely to remain poorErosion estimates must communicate uncertainty due to geomorphic external and internal types of variabilityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/136017/1/grl55374-sup-0001-Supplementary.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/136017/2/grl55374.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/136017/3/grl55374_am.pd

Objective measurement of therapeutic response in breast cancer using tumour markers.

In 65 patients with systemic breast cancer, a biochemical response index using three tumour markers in combination, carcinoembryonic antigen (CEA), carbohydrate antigen 15-3 (CA 15-3) and erythrocyte sedimentation rate (ESR), allowed objective biochemical assessment of response to endocrine therapy. Changes in these three markers at 2, 4 and 6 months showed a highly significant correlation with UICC assessed response at 6 months. At 4 months, changes in these three markers resulted in a selectivity of 93%, with a sensitivity of 92% and a specificity of 82%. Survival of groups of patients assessed biochemically or by UICC criteria for non-progression or progression showed no significant difference. The advantage of the biochemical assessment are that it is objective and reproducible. The assessment gives similar information to the UICC assessment but can be carried out earlier. Changes in the three markers appears to reflect the dynamics of change in tumour mass in response to systemic therapy in contrast to the UICC criteria which reflect structural change

Unsupervised machine learning of integrated health and social care data from the Macmillan Improving the Cancer Journey service in Glasgow

Background: Improving the Cancer Journey (ICJ) was launched in 2014 by Glasgow City Council and Macmillan Cancer Support. As part of routine service, data is collected on ICJ users including demographic and health information, results from holistic needs assessments and quality of life scores as measured by EQ-5D health status. There is also data on the number and type of referrals made and feedback from users on the overall service. By applying artificial intelligence and interactive visualization technologies to this data, we seek to improve service provision and optimize resource allocation.Method: An unsupervised machine-learning algorithm was deployed to cluster the data. The classical k-means algorithm was extended with the k-modes technique for categorical data, and the gap heuristic automatically identified the number of clusters. The resulting clusters are used to summarize complex data sets and produce three-dimensional visualizations of the data landscape. Furthermore, the traits of new ICJ clients are predicted by approximately matching their details to the nearest existing cluster center.Results: Cross-validation showed the model’s effectiveness over a wide range of traits. For example, the model can predict marital status, employment status and housing type with an accuracy between 2.4 to 4.8 times greater than random selection. One of the most interesting preliminary findings is that area deprivation (measured through Scottish Index of Multiple Deprivation-SIMD) is a better predictor of an ICJ client’s needs than primary diagnosis (cancer type).Conclusion: A key strength of this system is its ability to rapidly ingest new data on its own and derive new predictions from those data. This means the model can guide service provision by forecasting demand based on actual or hypothesized data. The aim is to provide intelligent person-centered recommendations. The machine-learning model described here is part of a prototype software tool currently under development for use by the cancer support community.Disclosure: Funded by Macmillan Cancer Support</p

“I didn’t have any option”: Experiences of people receiving in-centre haemodialysis during the COVID-19 pandemic

People receiving in-centre haemodialysis (ICHD) during the COVID-19 pandemic had to adjust to more challenging treatment conditions. To explore people’s experiences of adjustment to ICHD during the pandemic. Thematic analysis of in-depth, semi-structured interviews with 14 adult UK ICHD patients. Findings: Four themes were identified: ‘perceptions of the threat’, ‘impacts on treatment’, ‘impaired communication’ and ‘coping and positive adjustment’. These described participants’ experiences of vulnerability to COVID-19; the ways the pandemic affected dialysis and clinical care; the impact that measures to reduce viral transmission had on communication and interaction within dialysis units; and ways that participants coped and made positive adjustments to the adversities imposed by the pandemic. The findings give insights into adjustment during extreme adversity. They also help to identify ways that support for ICHD patients could be improved as pandemic conditions recede, and ways that dialysis units could prepare for future outbreaks of infectious illness