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.     

Detection of prokaryotic mRNA signifies microbial viability and promotes immunity

Live vaccines have long been known to trigger far more vigorous immune responses than their killed counterparts. This has been attributed to the ability of live microorganisms to replicate and express specialized virulence factors that facilitate invasion and infection of their hosts. However, protective immunization can often be achieved with a single injection of live, but not dead, attenuated microorganisms stripped of their virulence factors. Pathogen-associated molecular patterns (PAMPs), which are detected by the immune system, are present in both live and killed vaccines, indicating that certain poorly characterized aspects of live microorganisms, not incorporated in dead vaccines, are particularly effective at inducing protective immunity. Here we show that the mammalian innate immune system can directly sense microbial viability through detection of a special class of viability-associated PAMPs (vita-PAMPs). We identify prokaryotic messenger RNA as a vita-PAMP present only in viable bacteria, the recognition of which elicits a unique innate response and a robust adaptive antibody response. Notably, the innate response evoked by viability and prokaryotic mRNA was thus far considered to be reserved for pathogenic bacteria, but we show that even non-pathogenic bacteria in sterile tissues can trigger similar responses, provided that they are alive. Thus, the immune system actively gauges the infectious risk by searching PAMPs for signatures of microbial life and thus infectivity. Detection of vita-PAMPs triggers a state of alert not warranted for dead bacteria. Vaccine formulations that incorporate vita-PAMPs could thus combine the superior protection of live vaccines with the safety of dead vaccines.     

Germline gain-of-function mutations in SOS1 cause Noonan syndrome

Noonan syndrome, the most common single-gene cause of congenital heart disease, is characterized by short stature, characteristic facies, learning problems and leukemia predisposition. Gain-of-function mutations in PTPN11, encoding the tyrosine phosphatase SHP2, cause approximately 50% of Noonan syndrome cases. SHP2 is required for RAS-ERK MAP kinase (MAPK) cascade activation, and Noonan syndrome mutants enhance ERK activation ex vivo and in mice. KRAS mutations account for <5% of cases of Noonan syndrome, but the gene(s) responsible for the remainder are unknown. We identified missense mutations in SOS1, which encodes an essential RAS guanine nucleotide-exchange factor (RAS-GEF), in approximately 20% of cases of Noonan syndrome without PTPN11 mutation. The prevalence of specific cardiac defects differs in SOS1 mutation-associated Noonan syndrome. Noonan syndrome-associated SOS1 mutations are hypermorphs encoding products that enhance RAS and ERK activation. Our results identify SOS1 mutants as a major cause of Noonan syndrome, representing the first example of activating GEF mutations associated with human disease and providing new insights into RAS-GEF regulation.     

Prion-like behaviour and tau-dependent cytotoxicity of pyroglutamylated amyloid-β

Extracellular plaques of amyloid-β and intraneuronal neurofibrillary tangles made from tau are the histopathological signatures of Alzheimer's disease. Plaques comprise amyloid-β fibrils that assemble from monomeric and oligomeric intermediates, and are prognostic indicators of Alzheimer's disease. Despite the importance of plaques to Alzheimer's disease, oligomers are considered to be the principal toxic forms of amyloid-β. Interestingly, many adverse responses to amyloid-β, such as cytotoxicity, microtubule loss, impaired memory and learning, and neuritic degeneration, are greatly amplified by tau expression. Amino-terminally truncated, pyroglutamylated (pE) forms of amyloid-β are strongly associated with Alzheimer's disease, are more toxic than amyloid-β, residues 1-42 (Aβ(1-42)) and Aβ(1-40), and have been proposed as initiators of Alzheimer's disease pathogenesis. Here we report a mechanism by which pE-Aβ may trigger Alzheimer's disease. Aβ(3(pE)-42) co-oligomerizes with excess Aβ(1-42) to form metastable low-n oligomers (LNOs) that are structurally distinct and far more cytotoxic to cultured neurons than comparable LNOs made from Aβ(1-42) alone. Tau is required for cytotoxicity, and LNOs comprising 5% Aβ(3(pE)-42) plus 95% Aβ(1-42) (5% pE-Aβ) seed new cytotoxic LNOs through multiple serial dilutions into Aβ(1-42) monomers in the absence of additional Aβ(3(pE)-42). LNOs isolated from human Alzheimer's disease brain contained Aβ(3(pE)-42), and enhanced Aβ(3(pE)-42) formation in mice triggered neuron loss and gliosis at 3 months, but not in a tau-null background. We conclude that Aβ(3(pE)-42) confers tau-dependent neuronal death and causes template-induced misfolding of Aβ(1-42) into structurally distinct LNOs that propagate by a prion-like mechanism. Our results raise the possibility that Aβ(3(pE)-42) acts similarly at a primary step in Alzheimer's disease pathogenesis.     

Macropinocytosis,mTORC1 and cellular growth control

The growth and proliferation of metazoan cells are driven by cellular nutrient status and by extracellular growth factors. Growth factor receptors on cell surfaces initiate biochemical signals that increase anabolic metabolism and macropinocytosis, an actin-dependent endocytic process in which relatively large volumes of extracellular solutes and nutrients are internalized and delivered efficiently into lysosomes. Macropinocytosis is prominent in many kinds of cancer cells, and supports the growth of cells transformed by oncogenic K-Ras. Growth factor receptor signaling and the overall metabolic status of the cell are coordinated in the cytoplasm by the mechanistic target-of-rapamycin complex-1 (mTORC1), which positively regulates protein synthesis and negatively regulates molecular salvage pathways such as autophagy. mTORC1 is activated by two distinct Ras-related small GTPases, Rag and Rheb, which associate with lysosomal membranes inside the cell. Rag recruits mTORC1 to the lysosomal surface where Rheb directly binds to and activates mTORC1. Rag is activated by both lysosomal luminal and cytosolic amino acids; Rheb activation requires phosphoinositide 3-kinase, Akt, and the tuberous sclerosis complex-1/2. Signals for activation of Rag and Rheb converge at the lysosomal membrane, and several lines of evidence support the idea that growth factor-dependent endocytosis facilitates amino acid transfer into the lysosome leading to the activation of Rag. This review summarizes evidence that growth factor-stimulated macropinocytosis is essential for amino acid-dependent activation of mTORC1, and that increased solute accumulation by macropinocytosis in transformed cells supports unchecked cell growth.     

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.