Food

Photos and short writings from Public Health & Nutrition undergraduate students describing the food dimension of the social determinants of health.https://thekeep.eiu.edu/pubh_belonging_exhibit/1004/thumbnail.jp

Community, Safety and Social Context

Photos and short writings from Public Health & Nutrition undergraduate students describing the community, safety and social context dimensions of the social determinants of health.https://thekeep.eiu.edu/pubh_belonging_exhibit/1002/thumbnail.jp

Belonging, post card

Marketing post card with selected images from the Belonging exhibit.https://thekeep.eiu.edu/pubh_belonging_exhibit/1006/thumbnail.jp

Introduction

Provides background to the Belonging project and an introduction to the social determinants of health.https://thekeep.eiu.edu/pubh_belonging_exhibit/1000/thumbnail.jp

Health Care

Photos and short writings from Public Health & Nutrition undergraduate students describing the Health Care and Community dimensions of the social determinants of health.https://thekeep.eiu.edu/pubh_belonging_exhibit/1005/thumbnail.jp

Using machine learning to study the kinematics of cold gas in galaxies

Next generation interferometers, such as the Square Kilometre Array, are set to obtain vast quantities of information about the kinematics of cold gas in galaxies. Given the volume of data produced by such facilities astronomers will need fast, reliable, tools to informatively filter and classify incoming data in real time. In this paper, we use machine learning techniques with a hydrodynamical simulation training set to predict the kinematic behaviour of cold gas in galaxies and test these models on both simulated and real interferometric data. Using the power of a convolutional autoencoder we embed kinematic features, unattainable by the human eye or standard tools, into a 3D space and discriminate between disturbed and regularly rotating cold gas structures. Our simple binary classifier predicts the circularity of noiseless, simulated, galaxies with a recall of 85% and performs as expected on observational CO and H i velocity maps, with a heuristic accuracy of 95%. The model output exhibits predictable behaviour when varying the level of noise added to the input data and we are able to explain the roles of all dimensions of our mapped space. Our models also allow fast predictions of input galaxies’ position angles with a 1σ uncertainty range of ±17° to ±23° (for galaxies with inclinations of 82.5° to 32.5°, respectively), which may be useful for initial parametrization in kinematic modelling samplers. Machine learning models, such as the one outlined in this paper, may be adapted for SKA science usage in the near future

Bone Mineral Density Corrected for Size in Childhood Leukaemia Survivors Treated with Haematopoietic Stem Cell Transplantation and Total Body Irradiation

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; Childhood leukaemia survivors treated with haematopoietic stem cell transplantation and total body irradiation (HSCT-TBI) have multiple risk factors for reduced bone mineral density (BMD) and growth failure; hence, BMD assessment must take body size into consideration. This study aimed to evaluate size-corrected BMD in leukaemia survivors treated with and without HSCT-TBI. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; Childhood leukaemia survivors treated with HSCT-TBI (&lt;i&gt;n&lt;/i&gt; = 35), aged 17.3 (10.5–20.9) years, were compared with those treated with chemotherapy only, (&lt;i&gt;n&lt;/i&gt; = 16) aged 18.5 (16.1–20.9) years, and population references. Outcome measures included anthropometric measurements and BMD by dual-energy X-ray absorptiometry. BMD was corrected for size as bone mineral apparent density (BMAD). Statistical analysis was performed by 1- and 2-sample &lt;i&gt;t&lt;/i&gt; tests as well as regression analysis (5% significance). &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; HSCT-TBI survivors were lighter and shorter with reduced spinal heights compared with chemotherapy-only subjects and population references. Compared with population references, HSCT-TBI survivors showed lower BMD standard deviation scores (SDS) (&lt;i&gt;p&lt;/i&gt; = 0.008), but no difference in BMAD-SDS, and chemotherapy-only survivors showed no differences in neither BMD-SDS nor BMAD-SDS. All HSCT-TBI participants with BMD-SDS &amp;#x3c;–2 had BMAD-SDS &amp;#x3e;–2. BMAD-SDS was negatively associated with age (&lt;i&gt;r&lt;/i&gt; = –0.38, &lt;i&gt;p&lt;/i&gt; = 0.029) in HSCT-TBI survivors. &lt;b&gt;&lt;i&gt;Conclusions:&lt;/i&gt;&lt;/b&gt; Size-corrected BMD are normal in HSCT-TBI survivors in young adulthood, but may reduce overtime. BMD measurements should be corrected for size in these patients to be clinically meaningful.</jats:p

The networks of care surrounding cancer palliative care patients

Objectives: This paper explicates the nature and extent of the networks of care surrounding patients with cancer palliative care needs. Method: Twenty-four patients with 15 different types/sites of cancer were recruited in one city in England, UK. During one in-depth interview patients identified who was ‘involved in their care’ and any known pathways of communication between them. One hundred of these people (35 doctors, 32 nurses, 17 professions allied to medicine, 8 family members and 8 others) were also interviewed. Maps of people/teams and the connections between them for each patient were then reconstructed using social networking software (PAJEK). Results: The 24 patients identified a total of 619 people or teams (mean 26, median 22, range 9–45 per patient) contributing to their care. Selected care network maps are displayed, illustrating the extent and nature of the care networks supporting palliative care patients. Common members of care networks for patients with palliative care needs are revealed, but their individual and unique nature is also apparent. Conclusions: The possible clinical utility and challenges of mapping care networks are discussed. Exploring the care networks surrounding individual patients can be useful for illuminating the extent and complexity of individual patient's care networks; clarifying who is involved and who they communicate with; providing opportunities to see interaction routes that may otherwise be hidden, revealing potentially missing or weak connections; and highlighting overlaps or gaps in provision