Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

Increased migration of olfactory ensheathing cells secreting the Nogo receptor ectodomain over inhibitory substrates and lesioned spinal cord

Olfactory ensheathing cell (OEC) transplantation emerged some years ago as a promising therapeutic strategy to repair injured spinal cord. However, inhibitory molecules are present for long periods of time in lesioned spinal cord, inhibiting both OEC migration and axonal regrowth. Two families of these molecules, chondroitin sulphate proteoglycans (CSPG) and myelin-derived inhibitors (MAIs), are able to trigger inhibitory responses in lesioned axons. Mounting evidence suggests that OEC migration is inhibited by myelin. Here we demonstrate that OEC migration is largely inhibited by CSPGs and that inhibition can be overcome by the bacterial enzyme Chondroitinase ABC. In parallel, we have generated a stable OEC cell line overexpressing the Nogo receptor (NgR) ectodomain to reduce MAI-associated inhibition in vitro and in vivo. Results indicate that engineered cells migrate longer distances than unmodified OECs over myelin or oligodendrocyte-myelin glycoprotein (OMgp)-coated substrates. In addition, they also show improved migration in lesioned spinal cord. Our results provide new insights toward the improvement of the mechanisms of action and optimization of OEC-based cell therapy for spinal cord lesion.     

Gastrointestinal helminths of nine species of Cnemaspis (Squamata: Gekkonidae) from Peninsular Malaysia,one species from Cambodia and Thailand and two species from Vietnam

A total of 12 species of Cnemaspis (N = 104) from Southeast Asia were examined for gastrointestinal helminths. Samples consisted of nine species (n = 86) from Peninsular Malaysia: Cnemaspis affinis (n = 4); Cnemaspis baueri (n = 17); Cnemaspis biocellata (n = 12); Cnemaspis grismeri (n = 8); Cnemaspis kumpoli (n = 11); Cnemaspis limi (n = 9): Cnemaspis monachorum (n = 7); Cnemaspis pemanggilensis (n = 10); Cnemaspis peninsularis (n = 8); one species (n = 5) from Cambodia and Thailand, Cnemaspis chanthaburiensis (n = 5); and two species (n = 13) from Vietnam: Cnemaspis nuicamensis (n = 6) and Cnemaspis tucdupensis (n = 7). The aggregate helminth community consisted of one species of Cestoda, Cylindrotaenia malayi and nine species of Nematoda: Bakeria schadi, Meteterakis singaporensis, Parapharyngodon maplestoni, Maxvachonia sp., Physalopteroides sp., Physalopteridae gen. sp., Riticulariidae gen. sp., Seuratoidea gen. sp., Ascaridoidea gen. sp. Meteterakis singaporensis had the largest number of individuals (457) and greatest prevalence (24%). Twenty-eight new host records are reported.     

Muhaka icipe,an enigmatic new genus and species of Kleidotomini (Hymenoptera: Figitidae: Eucoilinae) from an East African coastal forest

A remarkable new eucoiline genus and species, Muhaka icipe, is described herein. The genus is clearly a Kleidotomini, but is distinguished from other genera in the tribe by a unique head and scutellar morphology. The genus belongs to the ‘wedge-head’-syndrome group of species that, to date, is unique to Afrotropical eucoilines. The new genus and species is reminiscent of Stentorceps Quinlan and Nanocthulhu Buffington, but is readily distinguished from these genera. Muhaka was collected from a threatened kaya (sacred forest) of coastal Kenya. The biological importance of this and other kaya forests, as well as their protection, is discussed.

http://www.zoobank.org/urn:lsid:zoobank.org:pub:6918ED2C-69A4-48FC-A1E4-2B5DFF58E876     

页岩结构完整性评价的热-流体-变形耦合理论与方法

该文应用连续介质力学理论建立了与蒸汽注入过程相关的热-流-变形耦合问题的基本数学模型;应用全隐式顺序Galerkin有限元数值解方案,对注蒸汽井的热-流体-变形耦合过程进行了数值模拟。结果表明,由于低渗透页岩地层流体高温膨胀导致的超高孔隙压力,有可能产生拉张应力,严重时会导致地层破裂。     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Prion protein cleavage fragments regulate adult neural stem cell quiescence through redox modulation of mitochondrial fission and SOD2 expression

Neurogenesis continues in the post-developmental brain throughout life. The ability to stimulate the production of new neurones requires both quiescent and actively proliferating pools of neural stem cells (NSCs). Actively proliferating NSCs ensure that neurogenic demand can be met, whilst the quiescent pool makes certain NSC reserves do not become depleted. The processes preserving the NSC quiescent pool are only just beginning to be defined. Herein, we identify a switch between NSC proliferation and quiescence through changing intracellular redox signalling. We show that N-terminal post-translational cleavage products of the prion protein (PrP) induce a quiescent state, halting NSC cellular growth, migration, and neurite outgrowth. Quiescence is initiated by the PrP cleavage products through reducing intracellular levels of reactive oxygen species. First, inhibition of redox signalling results in increased mitochondrial fission, which rapidly signals quiescence. Thereafter, quiescence is maintained through downstream increases in the expression and activity of superoxide dismutase-2 that reduces mitochondrial superoxide. We further observe that PrP is predominantly cleaved in quiescent NSCs indicating a homeostatic role for this cascade. Our findings provide new insight into the regulation of NSC quiescence, which potentially could influence brain health throughout adult life.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.