170 research outputs found
Role of surface roughness in hard x-ray emission from femtosecond laser produced copper plasmas
The hard x-ray emission in the energy range of 30-300 keV from copper plasmas
produced by 100 fs, 806 nm laser pulses at intensities in the range of
10 W cm is investigated. We demonstrate that surface
roughness of the targets overrides the role of polarization state in the
coupling of light to the plasma. We further show that surface roughness has a
significant role in enhancing the x-ray emission in the above mentioned energy
range.Comment: 5 pages, 4 figures, to appear in Phys. Rev.
An investigation of the clinical impact and therapeutic relevance of a DNA damage immune response (DDIR) signature in patients with advanced gastroesophageal adenocarcinoma
BackgroundAn improved understanding of which gastroesophageal adenocarcinoma (GOA) patients respond to both chemotherapy and immune checkpoint inhibitors (ICI) is needed. We investigated the predictive role and underlying biology of a 44-gene DNA damage immune response (DDIR) signature in patients with advanced GOA.Materials and methodsTranscriptional profiling was carried out on pretreatment tissue from 252 GOA patients treated with platinum-based chemotherapy (three dose levels) within the randomized phase III GO2 trial. Cross-validation was carried out in two independent GOA cohorts with transcriptional profiling, immune cell immunohistochemistry and epidermal growth factor receptor (EGFR) fluorescent in situ hybridization (FISH) (n = 430).ResultsIn the GO2 trial, DDIR-positive tumours had a greater radiological response (51.7% versus 28.5%, P = 0.022) and improved overall survival in a dose-dependent manner (P = 0.028). DDIR positivity was associated with a pretreatment inflamed tumour microenvironment (TME) and increased expression of biomarkers associated with ICI response such as CD274 (programmed death-ligand 1, PD-L1) and a microsatellite instability RNA signature. Consensus pathway analysis identified EGFR as a potential key determinant of the DDIR signature. EGFR amplification was associated with DDIR negativity and an immune cold TME.ConclusionsOur results indicate the importance of the GOA TME in chemotherapy response, its relationship to DNA damage repair and EGFR as a targetable driver of an immune cold TME. Chemotherapy-sensitive inflamed GOAs could benefit from ICI delivered in combination with standard chemotherapy. Combining EGFR inhibitors and ICIs warrants further investigation in patients with EGFR-amplified tumours
Molecular determinants of the interaction between HSV-1 glycoprotein D and heparan sulfate
Literature has well-established the importance of 3-O-sulfation of neuronal cell surface glycan heparan sulfate (HS) to its interaction with herpes simplex virus type 1 glycoprotein D (gD). Previous investigations of gD to its viral receptors HVEM and nectin-1 also highlighted the conformational dynamics of gD’s N- and C-termini, necessary for viral membrane fusion. However, little is known on the structural interactions of gD with HS. Here, we present our findings on this interface from both the glycan and the protein perspective. We used C-terminal and N-terminal gD variants to probe the role of their respective regions in gD/HS binding. The N-terminal truncation mutants (with Δ1-22) demonstrate equivalent or stronger binding to heparin than their intact glycoproteins, indicating that the first 22 amino acids are disposable for heparin binding. Characterization of the conformational differences between C-terminal truncated mutants by sedimentation velocity analytical ultracentrifugation distinguished between the “open” and “closed” conformations of the glycoprotein D, highlighting the region’s modulation of receptor binding. From the glycan perspective, we investigated gD interacting with heparin, heparan sulfate, and other de-sulfated and chemically defined oligosaccharides using surface plasmon resonance and glycan microarray. The results show a strong preference of gD for 6-O-sulfate, with 2-O-sulfation becoming more important in the presence of 6-O-S. Additionally, 3-O-sulfation shifted the chain length preference of gD from longer chain to mid-chain length, reaffirming the sulfation site’s importance to the gD/HS interface. Our results shed new light on the molecular details of one of seven known protein-glycan interactions with 3-O-sulfated heparan sulfate
Model confidence sets and forecast combination: an application to age-specific mortality
Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model.
Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts.
Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+.
Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification
An investigation of the clinical impact and therapeutic relevance of a DNA damage immune response (DDIR) signature in patients with advanced gastroesophageal adenocarcinoma
Background: An improved understanding of which gastroesophageal adenocarcinoma (GOA) patients respond to both chemotherapy and immune checkpoint inhibitors (ICI) is needed. We investigated the predictive role and underlying biology of a 44-gene DNA damage immune response (DDIR) signature in patients with advanced GOA.Materials and methods: Transcriptional profiling was carried out on pretreatment tissue from 252 GOA patients treated with platinum-based chemotherapy (three dose levels) within the randomized phase III GO2 trial. Cross-validation was carried out in two independent GOA cohorts with transcriptional profiling, immune cell immunohistochemistry and epidermal growth factor receptor (EGFR) fluorescent in situ hybridization (FISH) (n = 430).Results: In the GO2 trial, DDIR-positive tumours had a greater radiological response (51.7% versus 28.5%, P = 0.022) and improved overall survival in a dose-dependent manner (P = 0.028). DDIR positivity was associated with a pretreatment inflamed tumour microenvironment (TME) and increased expression of biomarkers associated with ICI response such as CD274 (programmed death-ligand 1, PD-L1) and a microsatellite instability RNA signature. Consensus pathway analysis identified EGFR as a potential key determinant of the DDIR signature. EGFR amplification was associated with DDIR negativity and an immune cold TME.Conclusions: Our results indicate the importance of the GOA TME in chemotherapy response, its relationship to DNA damage repair and EGFR as a targetable driver of an immune cold TME. Chemotherapy-sensitive inflamed GOAs could benefit from ICI delivered in combination with standard chemotherapy. Combining EGFR inhibitors and ICIs warrants further investigation in patients with EGFR-amplified tumours
p53 convergently activates Dux/DUX4 in embryonic stem cells and in facioscapulohumeral muscular dystrophy cell models
p53 activates Dux in mouse embryos and embryonic stem cells, as well as DUX4 in human facioscapulohumeral muscular dystrophy cell models.In mammalian embryos, proper zygotic genome activation (ZGA) underlies totipotent development. Double homeobox (DUX)-family factors participate in ZGA, and mouse Dux is required for forming cultured two-cell (2C)-like cells. Remarkably, in mouse embryonic stem cells, Dux is activated by the tumor suppressor p53, and Dux expression promotes differentiation into expanded-fate cell types. Long-read sequencing and assembly of the mouse Dux locus reveals its complex chromatin regulation including putative positive and negative feedback loops. We show that the p53-DUX/DUX4 regulatory axis is conserved in humans. Furthermore, we demonstrate that cells derived from patients with facioscapulohumeral muscular dystrophy (FSHD) activate human DUX4 during p53 signaling via a p53-binding site in a primate-specific subtelomeric long terminal repeat (LTR)10C element. In summary, our work shows that p53 activation convergently evolved to couple p53 to Dux/DUX4 activation in embryonic stem cells, embryos and cells from patients with FSHD, potentially uniting the developmental and disease regulation of DUX-family factors and identifying evidence-based therapeutic opportunities for FSHD.Molecular Technology and Informatics for Personalised Medicine and HealthFunctional Genomics of Muscle, Nerve and Brain Disorder
Mortality forecasting in Colombia from abridged life tables by sex
[EN] BACKGROUND:
An adequate forecasting model of mortality that allows an analysis of different population changes is a topic of interest for countries in demographic transition. Phenomena such as the reduction of mortality, ageing, and the increase in life expectancy are extremely useful in the planning of public policies that seek to promote the economic and social development of countries. To our knowledge, this paper is one of the first to evaluate the performance of mortality forecasting models applied to abridged life tables.
OBJECTIVE:
Select a mortality model that best describes and forecasts the characteristics of mortality in Colombia when only abridged life tables are available.
DATA AND METHOD:
We used Colombian abridged life tables for the period 1973-2005 with data from the Latin American Human Mortality Database. Different mortality models to deal with modeling and forecasting probability of death are presented in this study. For the comparison of mortality models, two criteria were analyzed: graphical residuals analysis and the hold-out method to evaluate the predictive performance of the models, applying different goodness of fit measures.
RESULTS:
Only three models did not have convergence problems: Lee-Carter (LC), Lee-Carter with two terms (LC2), and Age-Period-Cohort (APC) models. All models fit better for women, the improvement of LC2 on LC is mostly for central ages for men, and the APC model's fit is worse than the other two. The analysis of the standardized deviance residuals allows us to deduce that the models that reasonably fit the Colombian mortality data are LC and LC2. The major residuals correspond to children's ages and later ages for both sexes.
CONCLUSION:
The LC and LC2 models present better goodness of fit, identifying the principal characteristics of mortality for Colombia.Mortality forecasting from abridged life tables by sex has clear added value for studying differences between developing countries and convergence/divergence of demographic changes.Support for the research presented in this paper was provided by a grant from the Ministerio de Economía y Competitividad of Spain, project no. MTM2013-45381-P.Diaz-Rojo, G.; Debón Aucejo, AM.; Giner-Bosch, V. (2018). Mortality forecasting in Colombia from abridged life tables by sex. Genus. Journal of Population Sciences (Online). 74(15):1-23. https://doi.org/10.1186/s41118-018-0038-6S1237415Aburto, J.M., & García-Guerrero, V.M. (2015). El modelo aditivo doble multiplicativo. Una aplicacion a la mortalidad mexicaná. Papeles de Población, 21(84), 9–44.Acosta, K., & Romero, J. (2014). Cambios recientes en las principales causas de mortalidad en Colombia. Technical report: Banco de la Banco de la República- Serie Documentos de Trabajo Sobre Economía Regional.Aguilar, E. (2013). Estimación y proyección de la mortalidad para Costa Rica con la aplicación del método Lee-Carter con dos variantes. Población y, Salud en Mesoamérica, 11(1), 3–24.Andreozzi, L (2012). Estimación y pronósticos de la mortalidad de Argentina utilizando el modelo de Lee-Carter. Revista de la Sociedad Argentina de Estadística, 10(1), 21–43.Andreozzi, L, & Blaconá, MT (2011). Estimación y pronóstico de las tasas de mortalidad y la esperanza de vida en la República Argentina. In Proceedings of Anales de las Decimosextas Jornadas Investigaciones en la Facultad de Ciencias Económicas y Estadística. Universidad Nacional de Rosario, Argentina.Andres, V, Millossovich, P, Vladimir, K (2018). StMoMo: Stochastic Mortality Modeling in R. Journal of Statistical Software, 84(3), 1–38.Belliard, M., & Williams, I. (2013). Proyección estocástica de la mortalidad. Una aplicación de Lee-Carter en la Argentina. Revista Latinoamericana de Población, 7(13), 129–148.Bertranou, E. (2008). Tendencias demográficas y protección social en América Latina y el Caribe: CEPAL. http://repositorio.cepal.org/handle/11362/7224 . Accessed 8 Aug 2017.Blaconá, M.T, & Andreozzi, L. (2014). Análisis de la mortalidad por edad y sexo mediante modelos para datos funcionales. Estadística, 66(186–187), 65–89.Blum, A., Kalai, A., Langford, J. (1999). Beating the hold-out: bounds for k-fold and progressive cross-validation. In Proceedings of the twelfth annual Conference on Computational Learning Theory. ACM, (pp. 203–208).Booth, H., Maindonald, J., Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325–336.Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science, 3(1–2), 3–43.Butt, Z., Haberman, S., Shang, H.L. (2014). ilc: Lee-Carter mortality models using iterative fitting algorithms. http://cran.r-project.org/package=ilc .Cairns, A.J., Blake, D., Dowd, K. (2006). A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73(4), 687–718.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Khalaf-Allah, M. (2011). Mortality density forecasts: an analysis of six stochastic mortality models. Insurance: Mathematics and Economics, 48(3), 355–367.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A., Balevich, I. (2009). A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal, 13(1), 1–35.Canudas-Romo, V. (2008). The modal age at death and the shifting mortality hypothesis. Demographic Research, 19(30), 1179–1204.Carfora, M.F., Cutillo, L., Orlando, A. (2017). A quantitative comparison of stochastic mortality models on Italian population data. Computational Statistics & Data Analysis, 112, 198–214.Currie, I.D., Durban, M., Eilers, P.H.C. (2006). Generalized linear array models with applications to multidimensional smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(2), 259–280.Debón, A., Martínez-Ruiz, F., Montes, F. (2012). Temporal evolution of mortality indicators: application to Spanish data. North American Actuarial Journal, 16(3), 364–377.Debón, A., Montes, F., Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operation Research, 189(3), 624–637.Debón, A., Martínez-Ruiz, F., Montes, F. (2010). A geostatistical approach for dynamic life tables: the effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327–336.Díaz, G., & Debón, A. (2016). Tendencias y comportamiento de la mortalidad en Colombia entre 1973 y 2005. Estadística Española, 58(191), 277–300.García-Guerrero, V.M., & Mellado, M.O. (2012). Proyección estocástica de la mortalidad mexicana por medio del método de Lee-Carter. Estudios Demográficos y Urbanos, 27(2), 409–448.Garfield, R., & Llanten, C. (2004). The public health context of violence in Colombia. Revista Panamericana de Salud Pública, 16(4), 266–271.Haberman, S. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35–55.Holford, T.R. (2006). Approaches to fitting age-period-cohort models with unequal intervals. Statistics in Medicine, 25(6), 977–993.Hunt, A., & Villegas, A.M. (2015). Robustness and convergence in the Lee-Carter model with cohort effects. Insurance: Mathematics and Economics, 64, 186–202.Hyndman, R.J. (2016). Forecast: forecasting functions for time series and linear models. R package version 7.3. https://CRAN.R-project.org/package=forecast .Hyndman, R.J, Booth, H., Tickle, L., Maindonald, J. (2014). Demography: forecasting mortality, fertility, migration and population data. R package version 1.18. https://CRAN.R-project.org/package=demography .Hyndman, R.J., & Ullah, M.S (2007). Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51(10), 4942–4956.Kennes, T. (2017). The convergence and robustness of cohort extensions of mortality models. MaRBLe, 1, 36–53.Lee, R., & Carter, L. (1992). Modelling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–671.Lee, R., & Rofman, R. (1994). Modelación y proyección de la mortalidad en Chile. Notas de Poblacion, 6(59), 183–213.Lee, W. (1997). Characterizing exposure-disease association in human populations using the Lorenz curve and Gini index. Statistics in Medicine, 16(7), 729–739.Levitt, S., & Rubio, M. (2000). Understanding crime in Colombia and what can be done about it. Technical Report 20, FEDESARROLLO.Llorca, J., Prieto, M.D., Alvarez, C.F., Delgado-Rodriguez, M. (1998). Age differential mortality in Spain, 1900–1991. Journal of Epidemiology & Community Health, 52, 259–261.Llorca, J, Prieto, M.D, Delgado-Rodriguez, M (2000). Medición de las desigualdades en la edad de muerte: cálculo del índice de Gini a partir de las tablas de mortalidad. Revista Española de Salud Pública, 74(1), 5–12.Lora, E. (2008). Técnicas de medición económica. Metodología y aplicaciones en Colombia, Ed. Siglo Veintiuno XXI y Fedesarrollo, fourth edition. Bogotá D.C.: Alfaomega Colombiana S.A.Ochoa, C.A. (2015). El modelo Lee-Carter para estimar y pronosticar mortalidad: una aplicación para Colombia. Master’s thesis: Universidad Nacional de Colombia-Sede Medellín.O’hare, C., & Li, Y. (2017). Modelling mortality: are we heading in the right direction?Applied Economics, 49(2), 170–187.Ornelas, A. (2015). La mortalidad y la longevidad en la cuantificación del riesgo actuarial para la población de México. PhD thesis: Universitat de Barcelona.Remund, A., Camarda, C.G., Riffe, T., et al. (2017). A cause-of-death decomposition of the young adult mortality hump. Technical report. Rostock, Germany: Max Planck Institute for Demographic Research.Renshaw, A.E., & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255–272.Renshaw, A.E., & Haberman, S. (2006). A cohort-based extensionto the Lee-Carter model for mortality reduction factors. Insurance: Mathematic and Economics, 38(3), 556–570.Reyes, A.R. (2010). Una aproximación al costo fiscal en pensiones como consecuencia del envejecimiento de la población en Colombia y el efecto de la sobre-mortalidad masculina. Master’s thesis: Universidad Nacional de Colombia.Richards, S. (2008). Detecting year-of-birth mortality patterns with limited data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(1), 279–298.Rodríguez, J. (2007). Desigualdades socioeconómicas entre departamentos y su asociación con indicadores de mortalidad en Colombia en 2000. Rev Panam Salud Publica, 21(2/3), 111–124.Shkolnikov, V.M., Andreev, E.M., Begun, A. (2003). Gini coefficient as a life table function: computation from discrete data, decomposition of differences and empirical examples. Demographic Research, 8, 305–358.Singh, A., Shukla, A., Ram, F., Kumar, K. (2017). Trends in inequality in length of life in India: a decomposition analysis by age and causes of death. Genus, 73(1), 5.Tabeau, E. (2001). A review of demographic forecasting models for mortality. In: Tabeau, E., van den Berg Jeths A., Heathcote C. (Eds.) In Forecasting Mortality in Developed Countries. European Studies of Population, vol 9. Springer, Dordrecht, (pp. 1–32).Turner, H., & Firth, D. (2015). gnm: Generalized nonlinear models in R. R package version 1.0-8. https://CRAN.R-project.org/package=gnm .Urdinola, B.P., & Queiroz, B.L. (2017). Latin American Human Mortality Database. http://www.lamortalidad.org . Accessed 21 Nov 2015
Collisional and Radiative Processes in Optically Thin Plasmas
Most of our knowledge of the physical processes in distant plasmas is obtained
through measurement of the radiation they produce. Here we provide an overview of the
main collisional and radiative processes and examples of diagnostics relevant to the microphysical
processes in the plasma. Many analyses assume a time-steady plasma with ion
populations in equilibrium with the local temperature and Maxwellian distributions of particle
velocities, but these assumptions are easily violated in many cases. We consider these
departures from equilibrium and possible diagnostics in detail
The genetic architecture of the human cerebral cortex
INTRODUCTION
The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure.
RATIONALE
To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations.
RESULTS
We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness).
Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness.
To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity.
We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism.
CONCLUSION
This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function
- …