The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems

In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries at the global and national levels and show that these networks are significantly nested. This means that the less filled rows and columns of these networks' adjacency matrices tend to be subsets of the fuller rows and columns. Moreover, we show that nestedness remains relatively stable as the matrices become more filled over time and that this occurs because of a bias for industries that deviate from the networks' nestedness to disappear, and a bias for the missing industries that reduce nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. We interpret the high level of nestedness observed in these networks in the context of the neutral model of development introduced by Hidalgo and Hausmann (2009). We show that, for the observed fills, the model can reproduce the high level of nestedness observed in these networks only when we assume a high level of heterogeneity in the distribution of capabilities available in countries and required by products. In the context of the neutral model, this implies that the high level of nestedness observed in these economic networks emerges as a combination of both, the complementarity of inputs and heterogeneity in the number of capabilities available in countries and required by products. The stability of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of the study of network properties in the evolution of economic networks.Comment: 26 page

The diversity of the circumgalactic medium around z = 0 Milky Way-mass galaxies from the Auriga simulations

Galaxies are surrounded by massive gas reservoirs (i.e. the circumgalactic medium; CGM) which play a key role in their evolution. The properties of the CGM, which are dependent on a variety of internal and environmental factors, are often inferred from absorption line surveys which rely on a limited number of single lines-of-sight. In this work we present an analysis of 28 galaxy haloes selected from the Auriga project, a cosmological magnetohydrodynamical zoom-in simulation suite of isolated Milky Way-mass galaxies, to understand the impact of CGM diversity on observational studies. Although the Auriga haloes are selected to populate a narrow range in halo mass, our work demonstrates that the CGM of L galaxies is extremely diverse: column densities of commonly observed species span ∼3 − 4 dex and their covering fractions range from ∼5 to 90 per cent. Despite this diversity, we identify the following correlations: 1) the covering fractions (CF) of hydrogen and metals of the Auriga haloes positively correlate with stellar mass, 2) the CF of H I, C IV, and Si II anticorrelate with active galactic nucleus luminosity due to ionization effects, and 3) the CF of H I, C IV, and Si II positively correlate with galaxy disc fraction due to outflows populating the CGM with cool and dense gas. The Auriga sample demonstrates striking diversity within the CGM of L galaxies, which poses a challenge for observations reconstructing CGM characteristics from limited samples, and also indicates that long-term merger assembly history and recent star formation are not the dominant sculptors of the CGM

Effects of Thyroxine Exposure on Osteogenesis in Mouse Calvarial Pre-Osteoblasts

The incidence of craniosynostosis is one in every 1,800-2500 births. The gene-environment model proposes that if a genetic predisposition is coupled with environmental exposures, the effects can be multiplicative resulting in severely abnormal phenotypes. At present, very little is known about the role of gene-environment interactions in modulating craniosynostosis phenotypes, but prior evidence suggests a role for endocrine factors. Here we provide a report of the effects of thyroid hormone exposure on murine calvaria cells. Murine derived calvaria cells were exposed to critical doses of pharmaceutical thyroxine and analyzed after 3 and 7 days of treatment. Endpoint assays were designed to determine the effects of the hormone exposure on markers of osteogenesis and included, proliferation assay, quantitative ALP activity assay, targeted qPCR for mRNA expression of Runx2, Alp, Ocn, and Twist1, genechip array for 28,853 targets, and targeted osteogenic microarray with qPCR confirmations. Exposure to thyroxine stimulated the cells to express ALP in a dose dependent manner. There were no patterns of difference observed for proliferation. Targeted RNA expression data confirmed expression increases for Alp and Ocn at 7 days in culture. The genechip array suggests substantive expression differences for 46 gene targets and the targeted osteogenesis microarray indicated 23 targets with substantive differences. 11 gene targets were chosen for qPCR confirmation because of their known association with bone or craniosynostosis (Col2a1, Dmp1, Fgf1, 2, Igf1, Mmp9, Phex, Tnf, Htra1, Por, and Dcn). We confirmed substantive increases in mRNA for Phex, FGF1, 2, Tnf, Dmp1, Htra1, Por, Igf1 and Mmp9, and substantive decreases for Dcn. It appears thyroid hormone may exert its effects through increasing osteogenesis. Targets isolated suggest a possible interaction for those gene products associated with calvarial suture growth and homeostasis as well as craniosynostosis. © 2013 Cray et al

Access to housing subsidies, housing status, drug use and HIV risk among low-income U.S. urban residents

<p>Abstract</p> <p>Background</p> <p>Much research has shown an association between homelessness and unstable housing and HIV risk but most has relied on relatively narrow definitions of housing status that preclude a deeper understanding of this relationship. Fewer studies have examined access to housing subsidies and supportive housing programs among low-income populations with different personal characteristics. This paper explores personal characteristics associated with access to housing subsidies and supportive housing, the relationship between personal characteristics and housing status, and the relationship between housing status and sexual risk behaviors among low-income urban residents.</p> <p>Methods</p> <p>Surveys were conducted with 392 low-income residents from Hartford and East Harford, Connecticut through a targeted sampling plan. We measured personal characteristics (income, education, use of crack, heroin, or cocaine in the last 6 months, receipt of welfare benefits, mental illness diagnosis, arrest, criminal conviction, longest prison term served, and self-reported HIV diagnosis); access to housing subsidies or supportive housing programs; current housing status; and sexual risk behaviors. To answer the aims above, we performed univariate analyses using Chi-square or 2-sided ANOVA's. Those with significance levels above (0.10) were included in multivariate analyses. We performed 2 separate multiple regressions to determine the effects of personal characteristics on access to housing subsidies and access to supportive housing respectively. We used multinomial main effects logistic regression to determine the effects of housing status on sexual risk behavior.</p> <p>Results</p> <p>Being HIV positive or having a mental illness predicted access to housing subsidies and supportive housing, while having a criminal conviction was not related to access to either housing subsidies or supportive housing. Drug use was associated with poorer housing statuses such as living on the street or in a shelter, or temporarily doubling up with friends, acquaintances or sex partners. Living with friends, acquaintances or sex partners was associated with greater sexual risk than those living on the street or in other stable housing situations.</p> <p>Conclusions</p> <p>Results suggest that providing low-income and supportive housing may be an effective structural HIV prevention intervention, but that the availability and accessibility of these programs must be increased.</p

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Background: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors

Deficiency and Also Transgenic Overexpression of Timp-3 Both Lead to Compromised Bone Mass and Architecture In Vivo

Tissue inhibitor of metalloproteinases-3 (TIMP-3) regulates extracellular matrix via its inhibition of matrix metalloproteinases and membrane-bound sheddases. Timp-3 is expressed at multiple sites of extensive tissue remodelling. This extends to bone where its role, however, remains largely unresolved. In this study, we have used Micro-CT to assess bone mass and architecture, histological and histochemical evaluation to characterise the skeletal phenotype of Timp-3 KO mice and have complemented this by also examining similar indices in mice harbouring a Timp-3 transgene driven via a Col-2a-driven promoter to specifically target overexpression to chondrocytes. Our data show that Timp-3 deficiency compromises tibial bone mass and structure in both cortical and trabecular compartments, with corresponding increases in osteoclasts. Transgenic overexpression also generates defects in tibial structure predominantly in the cortical bone along the entire shaft without significant increases in osteoclasts. These alterations in cortical mass significantly compromise predicted tibial load-bearing resistance to torsion in both genotypes. Neither Timp-3 KO nor transgenic mouse growth plates are significantly affected. The impact of Timp-3 deficiency and of transgenic overexpression extends to produce modification in craniofacial bones of both endochondral and intramembranous origins. These data indicate that the levels of Timp-3 are crucial in the attainment of functionally-appropriate bone mass and architecture and that this arises from chondrogenic and osteogenic lineages

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behaviour of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48 machines test system and a realistic power system of China

Correlations and forecast of death tolls in the Syrian conflict

The Syrian armed conflict has been ongoing since 2011 and has already caused thousands of deaths. The analysis of death tolls helps to understand the dynamics of the conflict and to better allocate resources and aid to the affected areas. In this article, we use information on the daily number of deaths to study temporal and spatial correlations in the data, and exploit this information to forecast events of deaths. We found that the number of violent deaths per day in Syria varies more widely than that in England in which non-violent deaths dominate. We have identified strong positive auto-correlations in Syrian cities and non-trivial cross-correlations across some of them. The results indicate synchronization in the number of deaths at different times and locations, suggesting respectively that local attacks are followed by more attacks at subsequent days and that coordinated attacks may also take place across different locations. Thus the analysis of high temporal resolution data across multiple cities makes it possible to infer attack strategies, warn potential occurrence of future events, and hopefully avoid further deaths

The degradation of p53 and its major E3 ligase Mdm2 is differentially dependent on the proteasomal ubiquitin receptor S5a.

p53 and its major E3 ligase Mdm2 are both ubiquitinated and targeted to the proteasome for degradation. Despite the importance of this in regulating the p53 pathway, little is known about the mechanisms of proteasomal recognition of ubiquitinated p53 and Mdm2. In this study, we show that knockdown of the proteasomal ubiquitin receptor S5a/PSMD4/Rpn10 inhibits p53 protein degradation and results in the accumulation of ubiquitinated p53. Overexpression of a dominant-negative deletion of S5a lacking its ubiquitin-interacting motifs (UIM)s, but which can be incorporated into the proteasome, also causes the stabilization of p53. Furthermore, small-interferring RNA (siRNA) rescue experiments confirm that the UIMs of S5a are required for the maintenance of low p53 levels. These observations indicate that S5a participates in the recognition of ubiquitinated p53 by the proteasome. In contrast, targeting S5a has no effect on the rate of degradation of Mdm2, indicating that proteasomal recognition of Mdm2 can be mediated by an S5a-independent pathway. S5a knockdown results in an increase in the transcriptional activity of p53. The selective stabilization of p53 and not Mdm2 provides a mechanism for p53 activation. Depletion of S5a causes a p53-dependent decrease in cell proliferation, demonstrating that p53 can have a dominant role in the response to targeting S5a. This study provides evidence for alternative pathways of proteasomal recognition of p53 and Mdm2. Differences in recognition by the proteasome could provide a means to modulate the relative stability of p53 and Mdm2 in response to cellular signals. In addition, they could be exploited for p53-activating therapies. This work shows that the degradation of proteins by the proteasome can be selectively dependent on S5a in human cells, and that this selectivity can extend to an E3 ubiquitin ligase and its substrate

Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997–2003

BACKGROUND: The objective of this work was to develop a model to predict malaria incidence in an area of unstable transmission by studying the association between environmental variables and disease dynamics. METHODS: The study was carried out in Karuzi, a province in the Burundi highlands, using time series of monthly notifications of malaria cases from local health facilities, data from rain and temperature records, and the normalized difference vegetation index (NDVI). Using autoregressive integrated moving average (ARIMA) methodology, a model showing the relation between monthly notifications of malaria cases and the environmental variables was developed. RESULTS: The best forecasting model (R2adj = 82%, p < 0.0001 and 93% forecasting accuracy in the range +/- 4 cases per 100 inhabitants) included the NDVI, mean maximum temperature, rainfall and number of malaria cases in the preceding month. CONCLUSION: This model is a simple and useful tool for producing reasonably reliable forecasts of the malaria incidence rate in the study area