Genome-wide Screening Identifies SFMBT1 as an Oncogenic Driver in Cancer with VHL Loss

von Hippel-Lindau (VHL) is a critical tumor suppressor in clear cell renal cell carcinomas (ccRCCs). It is important to identify additional therapeutic targets in ccRCC downstream of VHL loss besides hypoxia-inducible factor 2α (HIF2α). By performing a genome-wide screen, we identified Scm-like with four malignant brain tumor domains 1 (SFMBT1) as a candidate pVHL target. SFMBT1 was considered to be a transcriptional repressor but its role in cancer remains unclear. ccRCC patients with VHL loss-of-function mutations displayed elevated SFMBT1 protein levels. SFMBT1 hydroxylation on Proline residue 651 by EglN1 mediated its ubiquitination and degradation governed by pVHL. Depletion of SFMBT1 abolished ccRCC cell proliferation in vitro and inhibited orthotopic tumor growth in vivo. Integrated analyses of ChIP-seq, RNA-seq, and patient prognosis identified sphingosine kinase 1 (SPHK1) as a key SFMBT1 target gene contributing to its oncogenic phenotype. Therefore, the pVHL-SFMBT1-SPHK1 axis serves as a potential therapeutic avenue for ccRCC. © 2020 Elsevier Inc.The pVHL-SFMBT1-SPHK1 axis serves as a potential therapeutic avenue for ccRCC

Inclusive eta' Production from the Upsilon(1S)

Using the CLEO II detector at CESR, we measure the eta' - gluon - gluon form-factor in Y(1S) decays. This form-factor especially at large eta' energies may provide an explanation of the large rate for B -> Xs eta'. Our data do not support a large anomalous coupling at higher q^2 and thus the large eta' rate remains a mystery, possibly requiring a non-Standard Model explanation.Comment: 14 pages postscript, also available through http://w4.lns.cornell.edu/public/CLNS, submitted to PR

FOXP3 Is an X-Linked Breast Cancer Suppressor Gene and an Important Repressor of the HER-2/ErbB2 Oncogene

The X-linked Foxp3 is a member of the forkhead/winged helix transcription factor family. Germ-line mutations cause lethal autoimmune diseases in males. Serendipitously, we observed that Foxp3sf/+ heterozygous mice developed cancer at a high rate. The majority of the cancers were mammary carcinomas in which the wild-type Foxp3 allele was inactivated and ErbB2 was over-expressed. Foxp3 bound and repressed the ErbB2 promoter. Deletion, functionally significant somatic mutations and down-regulation of the FOXP3 gene were commonly found in human breast cancer samples and correlated significantly with HER-2 over-expression, regardless of the status of HER-2 amplification. In toto, the data demonstrate that FOXP3 is an X-linked breast cancer suppressor gene and an important regulator of the HER-2/ErbB2 oncogene

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records

Purpose: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients� electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction. Method: We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction. Results: For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort. Conclusion: The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model. © 202

Galaxy Clusters Associated with Short GRBs. II. Predictions for the Rate of Short GRBs in Field and Cluster Early-Type Galaxies

We determine the relative rates of short GRBs in cluster and field early-type galaxies as a function of the age probability distribution of their progenitors, P(\tau) \propto \tau^n. This analysis takes advantage of the difference in the growth of stellar mass in clusters and in the field, which arises from the combined effects of the galaxy stellar mass function, the early-type fraction, and the dependence of star formation history on mass and environment. This approach complements the use of the early- to late-type host galaxy ratio, with the added benefit that the star formation histories of early-type galaxies are simpler than those of late-type galaxies, and any systematic differences between progenitors in early- and late-type galaxies are removed. We find that the ratio varies from R(cluster)/R(field) ~ 0.5 for n = -2 to ~ 3 for n = 2. Current observations indicate a ratio of about 2, corresponding to n ~ 0 - 1. This is similar to the value inferred from the ratio of short GRBs in early- and late-type hosts, but it differs from the value of n ~ -1 for NS binaries in the Milky Way. We stress that this general approach can be easily modified with improved knowledge of the effects of environment and mass on the build-up of stellar mass, as well as the effect of globular clusters on the short GRB rate. It can also be used to assess the age distribution of Type Ia supernova progenitors.Comment: ApJ accepted versio

Periodicities in the Daily Proton Fluxes from 2011 to 2019 Measured by the Alpha Magnetic Spectrometer on the International Space Station from 1 to 100 GV

We present the precision measurement of the daily proton fluxes in cosmic rays from May 20, 2011 to October 29, 2019 (a total of 2824 days or 114 Bartels rotations) in the rigidity interval from 1 to 100 GV based on 5.5×109 protons collected with the Alpha Magnetic Spectrometer aboard the International Space Station. The proton fluxes exhibit variations on multiple timescales. From 2014 to 2018, we observed recurrent flux variations with a period of 27 days. Shorter periods of 9 days and 13.5 days are observed in 2016. The strength of all three periodicities changes with time and rigidity. The rigidity dependence of the 27-day periodicity is different from the rigidity dependences of 9-day and 13.5-day periods. Unexpectedly, the strength of 9-day and 13.5-day periodicities increases with increasing rigidities up to ∼10 GV and ∼20 GV, respectively. Then the strength of the periodicities decreases with increasing rigidity up to 100 GV.</p

Precision Measurement of the Proton Flux in Primary Cosmic Rays from Rigidity 1 GV to 1.8 TV with the Alpha Magnetic Spectrometer on the International Space Station

A precise measurement of the proton flux in primary cosmic rays with rigidity (momentum/charge) from 1 GV to 1.8 TV is presented based on 300 million events. Knowledge of the rigidity dependence of the proton flux is important in understanding the origin, acceleration, and propagation of cosmic rays. We present the detailed variation with rigidity of the flux spectral index for the first time. The spectral index progressively hardens at high rigidities.</p