How does socio-economic and demographic dissimilarity determine physical and virtual segregation?

It is established that socio-economic and demographic dissimilarities between populations are determinants of spatial segregation. However, the understanding of how such dissimilarities translate into actual segregation is limited. We propose a novel network-analysis approach to comprehensively study the determinants of communicative and mobility-related spatial segregation, using geo-tagged Twitter data. We constructed weighted spatial networks representing tie strength between geographical areas, then modeled tie formation as a function of socio-economic and demographic dissimilarity between areas. Physical and virtual tie formation were affected by income, age, and race differences, although these effects were smaller by an order of magnitude than the geographical distance effect. Tie formation was more frequent when destination area had higher median income and lower median age. We hypothesize that physical tie formation is more costly than a virtual one resulting in stronger segregation in the physical world. Economic and cultural motives may result in stronger segregation of relatively rich and young populations from their surroundings. Our methodology can help identify types of states that lead to spatial segregation and thus guide planning decisions for reducing its adverse effects

DSCOVR-EPIC MAIAC AOD - A Proxy for Understanding Aerosol Diurnal Patterns from Space

The Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. The Earth Polychromatic Imaging Camera (EPIC) onboard DSCOVR views the entire sunlit Earth from sunrise to sunset, every 1-2 hours, at scattering angles between 168.5 and 175.5 with 10 narrowband filters in the range of 317-779 nm. NASA Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm, originally developed for MODIS, has been applied to EPIC data with an Aerosol Optical Depth (AOD) product at 440nm with a 10km spatial resolution. This high temporal resolution product is a unique dataset for investigating diurnal patterns in aerosols from space. Our work analyzed the capability of the satellite-borne data to capture the aerosol diurnal variation by associating it with AERONET AOD at 440nm data over the contiguous US. We validated the DSCOVR MAIAC AOD data over 100 AERONET stations during 2015-2018, and examined the contribution of the surface reflectance and relevant acquisition angles, derived by the MAIAC algorithm, to the predicted error. We used over 180,000 hourly DSCOVR-EPIC MAIAC AOD observations with collocated with AERONET AOD observations averaged over +-30 minutes from the satellite overpass time. The AERONET and DSCOVR AOD temporal patterns show that the diurnal variation is different across US AERONET sites, with higher diurnal variation in the DSCOVR dataset in general

High levels of protein carboxyl methyltransferase in well-differentiated human endometrial carcinoma

AbstractThe levels of protein carboxyl methyltransferase, an enzyme that methylates free carboxyl groups of proteins, were determined in normal human proliferative and secretory endometrium and in poorly and well-differentiated endometrial carcinoma. Protein carboxyl methyltransferase activity was 67% higher in the well-differentiated carcinoma than in the normal tissues, while similar enzyme levels were observed in the two normal tissues and in the poorly differentiated carcinoma. The results suggest that in the well-differentiated tumor there is excessive protein methylation and offer a possible biochemical probe for distinguishing between the two types of tumor

Farnesyl Thiosalicylic Acid Chemosensitizes Human Melanoma In Vivo

Malignant melanoma is well known for its poor response to a variety of chemotherapeutic agents. Testing of numerous treatment strategies has identified dacarbazine as the most active single drug; however, its response rates in various clinical settings are quite limited. Defective apoptosis in combination with oncogenic proteins (such as activated Ras) in cell proliferation pathways plays a key part in both the development and disease progression of human melanoma. Farnesyl thiosalicylic acid, a novel Ras inhibitor, dislodges Ras proteins from the cell membrane, leading to inhibition of cell transformation and tumor growth. In this study we evaluated the effect of farnesyl thiosalicylic acid treatment on established human melanoma xenografts grown in mice with severe combined immunodeficiency as well as the chemosensitizing effect of farnesyl thiosalicylic acid in combination with dacarbazine. Daily administration of 10, 20, or 40 mg per kg of farnesyl thiosalicylic acid resulted in a concentration-dependent reduction in tumor growth, with growth inhibition reaching a mean value of 45±7%, at the highest concentration. The combination of farnesyl thiosalicylic acid (10 mg per kg per day) and dacarbazine (80 mg per kg per day) resulted in a significant reduction of 56%±9%, in mean tumor growth. Analysis of toxicologic parameters (mouse weight, blood cell counts, and blood chemistry) showed an acceptable and similar toxicity profile for both the single-agent farnesyl thiosalicylic acid treatment and the combination of farnesyl thiosalicylic acid plus dacarbazine treatment. Given the observed preclinical treatment responses and the low toxicity, our results support the notion that farnesyl thiosalicylic acid in combination with dacarbazine may qualify as a rational treatment approach for human melanoma

Eff ect of increased concentrations of atmospheric carbon dioxide on the global threat of zinc defi ciency: a modelling study

Background Increasing concentrations of atmospheric carbon dioxide (CO2) lower the content of zinc and other nutrients in important food crops. Zinc defi ciency is currently responsible for large burdens of disease globally, and the populations who are at highest risk of zinc defi ciency also receive most of their dietary zinc from crops. By modelling dietary intake of bioavailable zinc for the populations of 188 countries under both an ambient CO2 and elevated CO2 scenario, we sought to estimate the eff ect of anthropogenic CO2 emissions on the global risk of zinc defi ciency. Methods We estimated per capita per day bioavailable intake of zinc for the populations of 188 countries at ambient CO2 concentrations (375–384 ppm) using food balance sheet data for 2003–07 from the Food and Agriculture Organization. We then used previously published data from free air CO2 enrichment and open-top chamber experiments to model zinc intake at elevated CO2 concentrations (550 ppm, which is the concentration expected by 2050). Estimates developed by the International Zinc Nutrition Consultative Group were used for country-specifi c theoretical mean daily per-capita physiological requirements for zinc. Finally, we used these data on zinc bioavailability and population-weighted estimated average zinc requirements to estimate the risk of inadequate zinc intake among the populations of the diff erent nations under the two scenarios (ambient and elevated CO2). The diff erence between the population at risk at elevated and ambient CO2 concentrations (ie, population at new risk of zinc defi ciency) was our measure of impact. Findings The total number of people estimated to be placed at new risk of zinc defi ciency by 2050 was 138 million (95% CI 120–156). The people likely to be most aff ected live in Africa and South Asia, with nearly 48 million (32–63) residing in India alone. Global maps of increased risk show signifi cant heterogeneity. Interpretation Our results indicate that one heretofore unquantifi ed human health eff ect associated with anthropogenic CO2 emissions will be a signifi cant increase in the human population at risk of zinc defi ciency. Our country-specifi c fi ndings can be used to help guide interventions aimed at reducing this vulnerability

Activated K-Ras and H-Ras display different interactions with saturable nonraft sites at the surface of live cells

Ras–membrane interactions play important roles in signaling and oncogenesis. H-Ras and K-Ras have nonidentical membrane anchoring moieties that can direct them to different membrane compartments. Ras–lipid raft interactions were reported, but recent studies suggest that activated K-Ras and H-Ras are not raft resident. However, specific interactions of activated Ras proteins with nonraft sites, which may underlie functional differences and phenotypic variation between different Ras isoforms, are unexplored. Here we used lateral mobility studies by FRAP to investigate the membrane interactions of green fluorescent protein–tagged H- and K-Ras in live cells. All Ras isoforms displayed stable membrane association, moving by lateral diffusion and not by exchange with a cytoplasmic pool. The lateral diffusion rates of constitutively active K- and H-Ras increased with their expression levels in a saturable manner, suggesting dynamic association with saturable sites or domains. These sites are distinct from lipid rafts, as the activated Ras mutants are not raft resident. Moreover, they appear to be different for H- and K-Ras. However, wild-type H-Ras, the only isoform preferentially localized in rafts, displayed cholesterol-sensitive interactions with rafts that were independent of its expression level. Our findings provide a mechanism for selective signaling by different Ras isoforms

Exposure to sub-chronic and long-term particulate air pollution and heart rate variability in an elderly cohort: the Normative Aging Study

Background: Short-term particulate air pollution exposure is associated with reduced heart rate variability (HRV), a risk factor for cardiovascular morbidity and mortality, in many studies. Associations with sub-chronic or long-term exposures, however, have been sparsely investigated. We evaluated the effect of fine particulate matter (PM2.5) and black carbon (BC) exposures on HRV in an elderly cohort: the Normative Aging Study. Methods: We measured power in high frequency (HF) and low frequency (LF), standard deviation of normal-to-normal intervals (SDNN), and the LF:HF ratio among participants from the Greater Boston area. Residential BC exposures for 540 men (1161 study visits, 2000–2011) were estimated using a spatio-temporal land use regression model, and residential PM2.5 exposures for 475 men (992 visits, 2003–2011) were modeled using a hybrid satellite based and land-use model. We evaluated associations between moving averages of sub-chronic (3–84 day) and long-term (1 year) pollutant exposure estimates and HRV parameters using linear mixed models. Results: One-standard deviation increases in sub-chronic, but not long-term, BC were associated with reduced HF, LF, and SDNN and an increased LF:HF ratio (e.g., 28 day BC: −2.3 % HF [95 % CI:−4.6, −0.02]). Sub-chronic and long-term PM2.5 showed evidence of relations to an increased LF and LF:HF ratio (e.g., 1 year PM: 21.0 % LF:HF [8.6, 34.8]), but not to HF or SDNN, though the effect estimates were very imprecise and mostly spanned the null. Conclusions: We observed some evidence of a relation between longer-term BC and PM2.5 exposures and changes in HRV in an elderly cohort. While previous studies focused on short-term air pollution exposures, our results suggest that longer-term exposures may influence cardiac autonomic function. Electronic supplementary material The online version of this article (doi:10.1186/s12940-015-0074-z) contains supplementary material, which is available to authorized users

a review of airq models and their applications for forecasting the air pollution health outcomes

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects

Assessing PM(sub 2.5) Exposures with High Spatiotemporal Resolution Across the Continental United States

A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index and meteoroidal fields, are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed good model performance with total R2 of 0.84 on the left out monitors. Regional R2 could be even higher for the Eastern and Central United States. Model performance was still good at low PM2.5 concentrations. Then, we used the trained neural network to make daily prediction of PM2.5 at 1 km 1 km grid cells. This model allows epidemiologists to access PM2.5 exposure in both the short term and the long term