I am Cuba-Soy Cuba: Commemorating 100 years of Russian Revolution

I am Cuba Soy Cuba is a 1964 Soviet Cuban film directed by Mikhail Kalatozov Hidden away in the Soviet archives for three decades, I Am Cuba is a wild celebration of Communist kitsch, mixing Slavic solemnity with Latin sensuality — a whirling, feverish dance through both the sensuous decadence of pre-revolutionary Havana and the grinding poverty and oppression of the Cuban people. In four stories of the revolution, the camera takes the viewer on a rapturous roller-coaster ride of bathing beauties, landless peasants, fascist police, and student revolutionaries.https://digitalcommons.fiu.edu/cri_events/1370/thumbnail.jp

Parental education and inequalitties in child mortality: a global systematic review and meta-analysis

The educational attainment of parents, particularly mothers, has been associated with lower levels of child mortality, yet there is no consensus on the magnitude of this relationship globally. We aimed to estimate the total reductions in under-5 mortality that are associated with increased maternal and paternal education, during distinct age intervals. This study is a comprehensive global systematic review and meta-analysis of all existing studies of the effects of parental education on neonatal, infant, and under-5 child mortality, combined with primary analyses of Demographic and Health Survey (DHS) data

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Tate Form and Weak Coupling Limits in F-theory

We consider the weak coupling limit of F-theory in the presence of non-Abelian gauge groups implemented using the traditional ansatz coming from Tate's algorithm. We classify the types of singularities that could appear in the weak coupling limit and explain their resolution. In particular, the weak coupling limit of SU(n) gauge groups leads to an orientifold theory which suffers from conifold singulaties that do not admit a crepant resolution compatible with the orientifold involution. We present a simple resolution to this problem by introducing a new weak coupling regime that admits singularities compatible with both a crepant resolution and an orientifold symmetry. We also comment on possible applications of the new limit to model building. We finally discuss other unexpected phenomena as for example the existence of several non-equivalent directions to flow from strong to weak coupling leading to different gauge groups.Comment: 34 page

U(n) Spectral Covers from Decomposition

We construct decomposed spectral covers for bundles on elliptically fibered Calabi-Yau threefolds whose structure groups are S(U(1) x U(4)), S(U(2) x U(3)) and S(U(1) x U(1) x U(3)) in heterotic string compactifications. The decomposition requires not only the tuning of the SU(5) spectral covers but also the tuning of the complex structure moduli of the Calabi-Yau threefolds. This configuration is translated to geometric data on F-theory side. We find that the monodromy locus for two-cycles in K3 fibered Calabi-Yau fourfolds in a stable degeneration limit is globally factorized with squared factors under the decomposition conditions. This signals that the monodromy group is reduced and there is a U(1) symmetry in a low energy effective field theory. To support that, we explicitly check the reduction of a monodromy group in an appreciable region of the moduli space for an $E_6$ gauge theory with (1+2) decomposition. This may provide a systematic way for constructing F-theory models with U(1) symmetries.Comment: 41 pages, 14 figures; v2: minor improvements and a reference adde

Six-dimensional (1,0) effective action of F-theory via M-theory on Calabi-Yau threefolds

The six-dimensional effective action of F-theory compactified on a singular elliptically fibred Calabi-Yau threefold is determined by using an M-theory lift. The low-energy data are derived by comparing a circle reduction of a general six-dimensional (1,0) gauged supergravity theory with the effective action of M-theory on the resolved Calabi-Yau threefold. The derivation includes six-dimensional tensor multiplets for which the (anti-) self-duality constraints are imposed on the level of the five-dimensional action. The vector sector of the reduced theory is encoded by a non-standard potential due to the Green-Schwarz term in six dimensions. This Green-Schwarz term also contains higher curvature couplings which are considered to establish the full map between anomaly coefficients and geometry. F-/M-theory duality is exploited by moving to the five-dimensional Coulomb branch after circle reduction and integrating out massive vector multiplets and matter hypermultiplets. The associated fermions then generate additional Chern-Simons couplings at one-loop. Further couplings involving the graviphoton are induced by quantum corrections due to excited Kaluza-Klein modes. On the M-theory side integrating out massive fields corresponds to resolving the singularities of the Calabi-Yau threefold, and yields intriguing relations between six-dimensional anomalies and classical topology.Comment: 55 pages, v2: typos corrected, discussion of loop corrections improve