Effects of Price Volatility and Surging South American Soybean Production on Short-run Soybean Basis Dynamics

This study investigates the effects of South American production (SAP) and futures volatility on the soybean price dynamics in terms of their effects on the basis. The results of the econometric model showed that both South American production and futures volatility of the nearby contract have negative effects on the basis though in the forecast model, lagged values of these two factors failed to predict basis change in the future. If information about the change of the expected SAP or futures volatility is available, then the model can predict the changes in basis. This information would be helpful for hedgers to decide the time to lift their hedge.Crop Production/Industries, Marketing,

Impacts of government risk management policies on hedging in futures and options:LPM2 hedge model vs. EU hedge model

The main objective of this study is to compare the impacts of government payments and crop insurance policies on the use of futures and options measured from a downside risk hedge model with the impacts analyzed by the expected utility (EU) hedge model. Understanding the effects of government-provided risk management tools on the private market risk management tools, such as futures and options, provides value to both crop farmers and policy makers. Comparison of the impacts from the two hedge models shows that crop farmer will hedge less in futures under the LPM2 model than under the EU hedge model. This finding indicates that model misspecification is another reason for the phenomenon that farmers actually hedge less in futures than predicted by the EU model. From the perspective of exploring new research techniques, this study applied two relatively new simulation concepts, copula simulation and conditional kernel density approach, to make the simulation assumptions less restrictive and more consistent with observations. The copula simulation applied in this study allows yield and price to have more flexible joint distribution functions than multivariate normal; the conditional kernel density approach used in farm yield simulation enables the variance of farm yield varies with county yield rather than being constant.Down-side Risk, LPM2 Hedge Model, Government Payments, Crop Insurance Policies, Copula Simulation, Conditional Kernel Density, Agricultural Finance,

Hedging Downside Risk to Farm Income with Futures and Options: Effects of Government Payment Programs and Federal Crop Insurance Plans

The high proportion of government payments in total crop farm income and the purchase of subsidized crop insurance have changed the income distribution of U.S. crop farmers. As a result, the risk management behaviors of U.S. crop farmers are affected by these programs in terms of the use of private market risk management tools, such as futures and options. The objective of this research is to investigate the effects of the government payments and federal crop insurance policies on the usage of futures and options by crop farmers from a downside risk management perspective. Results in this study suggest that both yield insurance and revenue insurance creates more hedging demands for futures. But revenue insurance decreases the buying of put options at the same time. Loan deficiency government payments substitutes largely for the hedging role of put options while Counter Cyclical payments substitutes futures hedge. This research contributes the literature by proposing to use a downside risk hedge model, the second-order lower partial moment (LPM2) hedge model, to investigate the interaction of government and private risk management tools used by crop farmers. This study also initiatively applies the conditional kernel density method and the copula approach in the data simulation process. The conditional kernel density method generates county yield and farm yield with the same conditional pattern as revealed in the historical yields. The copula simulation allows the crop yield and prices have more flexible joint distributions other than bivariate normal.Agricultural and Food Policy, Agricultural Finance,

Submucosal Gland Myoepithelial Cells Are Reserve Stem Cells That Can Regenerate Mouse Tracheal Epithelium

The mouse trachea is thought to contain two distinct stem cell compartments that contribute to airway repair-basal cells in the surface airway epithelium (SAE) and an unknown submucosal gland (SMG) cell type. Whether a lineage relationship exists between these two stem cell compartments remains unclear. Using lineage tracing of glandular myoepithelial cells (MECs), we demonstrate that MECs can give rise to seven cell types of the SAE and SMGs following severe airway injury. MECs progressively adopted a basal cell phenotype on the SAE and established lasting progenitors capable of further regeneration following reinjury. MECs activate Wnt-regulated transcription factors (Lef-1/TCF7) following injury and Lef-1 induction in cultured MECs promoted transition to a basal cell phenotype. Surprisingly, dose-dependent MEC conditional activation of Lef-1 in vivo promoted self-limited airway regeneration in the absence of injury. Thus, modulating the Lef-1 transcriptional program in MEC-derived progenitors may have regenerative medicine applications for lung diseases

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

The All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X) Mission Concept

The All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X) is designed to identify and characterize gamma rays from extreme explosions and accelerators. The main science themes include: supermassive black holes and their connections to neutrinos and cosmic rays; binary neutron star mergers and the relativistic jets they produce; cosmic ray particle acceleration sources including Galactic supernovae; and continuous monitoring of other astrophysical events and sources over the full sky in this important energy range. AMEGO-X will probe the medium energy gamma-ray band using a single instrument with sensitivity up to an order of magnitude greater than previous telescopes in the energy range 100 keV to 1 GeV that can be only realized in space. During its three-year baseline mission, AMEGO-X will observe nearly the entire sky every two orbits, building up a sensitive all-sky map of gamma-ray sources and emission. AMEGO-X was submitted in the recent 2021 NASA MIDEX Announcement of Opportunity.Comment: 23 pages, 16 figures, Published Journal of Astronomical Telescopes, Instruments, and System

All-sky Medium Energy Gamma-ray Observatory: Exploring the Extreme Multimessenger Universe

The All-sky Medium Energy Gamma-ray Observatory (AMEGO) is a probe class mission concept that will provide essential contributions to multimessenger astrophysics in the late 2020s and beyond. AMEGO combines high sensitivity in the 200 keV to 10 GeV energy range with a wide field of view, good spectral resolution, and polarization sensitivity. Therefore, AMEGO is key in the study of multimessenger astrophysical objects that have unique signatures in the gamma-ray regime, such as neutron star mergers, supernovae, and flaring active galactic nuclei. The order-of-magnitude improvement compared to previous MeV missions also enables discoveries of a wide range of phenomena whose energy output peaks in the relatively unexplored medium-energy gamma-ray band