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First recognized as regulators of development in worms and fruitflies, microRNAs are emerging as pivotal modulators of mammalian cardiovascular development and disease. Individual microRNAs modulate the expression of collections of messenger RNA targets that often have related functions, thereby governing complex biological processes. The wideranging functions of microRNAs in the cardiovascular system have provided new perspectives on disease mechanisms and have revealed intriguing therapeutic targets, as well as diagnostics, for a variety of cardiovascular disorders.

Currently, the ability to use remotely sensed soil moisture to investigate linkages between the water and energy cycles and for use in data assimilation studies is limited to passive microwave data whose temporal revisit time is 2–3 days or active microwave products with a much longer (>10 days) revisit time. This paper describes a dataset that provides soil moisture retrievals, which are gridded to 36 km, for the upper 5 cm of the soil surface at sparsely sampled 6-hour intervals for +/− 38 degrees latitude for 2017–present. Retrievals are derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation, which uses GNSS-Reflectometry to obtain L-band reflectivity observations over the Earth’s surface. The product was developed by calibrating CYGNSS reflectivity observations to soil moisture retrievals from NASA’s Soil Moisture Active Passive (SMAP) mission. Retrievals were validated against observations from 171 in-situ soil moisture probes, with a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3 cm−3 (standard deviation = 0.026 cm3 cm−3) and median correlation coefficient of 0.4 (standard deviation = 0.27). For the same stations, the median ubRMSE between SMAP and in-situ observations was 0.045 cm3 cm−3 (standard deviation = 0.025 cm3 cm−3) and median correlation coefficient was 0.69 (standard deviation = 0.27). The UCAR/CU Soil Moisture Product is thus complementary to SMAP, albeit with a larger random noise component, providing soil moisture retrievals for applications that require faster revisit times than passive microwave remote sensing currently provides.

The 2017 Atlantic Hurricane Season was one of the most active and destructive on record, leading to significant flooding in many parts of the United States and the Caribbean. During flooding events such as these, there is an urgent need to quickly map in detail which areas have been severely affected, yet current satellite missions are not capable of sampling the global land surface at high enough spatio-temporal scales for flooding applications. Here, we demonstrate a novel approach to high-resolution flood mapping by repurposing data from the new NASA mission, CYGNSS. The CYGNSS multi-satellite constellation was designed for frequent temporal sampling of the ocean surface in the tropics. We demonstrate that CYGNSS data provide clear signals of surface saturation and inundation extent over land at higher spatio-temporal resolution than radiometers like SMAP. Using a simple thresholding technique, we are able to estimate that approximately 32,580 km 2 of land area in Texas flooded during Hurricane Harvey, and approximately 7210 km 2 of land area flooded in Cuba during Hurricane Irma, or about 7% of Cuba’s total area.

Abiraterone has been approved as second-line chemotherapy in patients with metastatic castration-resistant prostate cancer. This study shows significant improvement in progression-free survival with abiraterone as first-line chemotherapy in these patients. Metastatic castration-resistant prostate cancer, defined by tumor growth despite a testosterone level of less than 50 ng per deciliter (1.7 nmol per liter), causes approximately 258,400 deaths annually worldwide. 1 , 2 Death of patients with this condition, which typically occurs within 24 to 48 months after the onset of castration resistance, is commonly preceded by a sequence of landmark events associated with deterioration of overall health and worsening symptoms (Figure S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org). 3 – 7 Among the treatment options for patients with metastatic castration-resistant prostate cancer who have not undergone chemotherapy . . .

Snow is a critical component of global climate and provides water resources to over 1 billion people worldwide. Yet current measurement methods and modeling techniques lack the ability to fully capture snow characteristics such as snow water equivalent (SWE) and density across variable landscapes. In recent years, physics‐informed machine learning (ML) methods have demonstrated promise for combining data‐driven learning and physical information. However, this capability has not been widely explored within snow hydrology. Here, we develop a “hybrid” model that applies ML informed by outputs from a physical model and assess whether it provides more accurate estimations of SWE and snow density. We trained and evaluated models at 49 SNOw TELemetry locations spanning a range of snow climates in the western US using 9 years of daily data. The research addressed two questions. In the first, the performance of the hybrid model was compared against a plain neural network (long short‐term memory, Long‐Short Term Memory), a high‐quality physical model, and a statistical snow density model. The second question focused on how regionally trained hybrid models compared to a westwide model as well as their transferability between multiple snow regions. The results showed that combining physical information and ML reduced SWE Root Mean Square Error by 35% compared to a physical model and 51% compared to a neural network. Additionally, regional training only provided minimal benefits compared with a westwide model. These findings indicate that a hybrid approach can yield more accurate snowpack characterization than either physical snow models or ML alone. Plain Language Summary Seasonal mountain snow provides important water resources for communities throughout the western United States and other global regions. Measuring this snow is difficult due to the large area and variable terrain found in most mountain ranges. This study uses a specialized computer model to produce better estimates of important snowpack information such as snow water equivalent (SWE) and snow density. The computer model combines a neural network (a type of model meant to mimic the human brain) with snowpack estimates from a mathematical model based on physics. When used together, this study found that this “hybrid” approach of the computer and mathematical models was better than other traditional ways of estimating SWE and snow density. Key Points A hybrid approach combining machine learning and physical model output was developed and trained over a wide range of snow conditions The hybrid model estimated snow density and snow water equivalent with improved accuracy relative to data‐driven and physical models Local training of the hybrid model did not improve snow estimates over a model trained at all study sites across the western United States

Abiraterone acetate plus prednisone significantly improved radiographic progression-free survival compared with placebo plus prednisone in men with chemotherapy-naive castration-resistant prostate cancer at the interim analyses of the COU-AA-302 trial. Here, we present the prespecified final analysis of the trial, assessing the effect of abiraterone acetate plus prednisone on overall survival, time to opiate use, and use of other subsequent therapies. In this placebo-controlled, double-blind, randomised phase 3 study, 1088 asymptomatic or mildly symptomatic patients with chemotherapy-naive prostate cancer stratified by Eastern Cooperative Oncology performance status (0 vs 1) were randomly assigned with a permuted block allocation scheme via a web response system in a 1:1 ratio to receive either abiraterone acetate (1000 mg once daily) plus prednisone (5 mg twice daily; abiraterone acetate group) or placebo plus prednisone (placebo group). Coprimary endpoints were radiographic progression-free survival and overall survival analysed in the intention-to-treat population. The study is registered with ClinicalTrials.gov, number NCT00887198. At a median follow-up of 49·2 months (IQR 47·0–51·8), 741 (96%) of the prespecified 773 death events for the final analysis had been observed: 354 (65%) of 546 patients in the abiraterone acetate group and 387 (71%) of 542 in the placebo group. 238 (44%) patients initially receiving prednisone alone subsequently received abiraterone acetate plus prednisone as crossover per protocol (93 patients) or as subsequent therapy (145 patients). Overall, 365 (67%) patients in the abiraterone acetate group and 435 (80%) in the placebo group received subsequent treatment with one or more approved agents. Median overall survival was significantly longer in the abiraterone acetate group than in the placebo group (34·7 months [95% CI 32·7–36·8] vs 30·3 months [28·7–33·3]; hazard ratio 0·81 [95% CI 0·70–0·93]; p=0·0033). The most common grade 3–4 adverse events of special interest were cardiac disorders (41 [8%] of 542 patients in the abiraterone acetate group vs 20 [4%] of 540 patients in the placebo group), increased alanine aminotransferase (32 [6%] vs four [<1%]), and hypertension (25 [5%] vs 17 [3%]). In this randomised phase 3 trial with a median follow-up of more than 4 years, treatment with abiraterone acetate prolonged overall survival compared with prednisone alone by a margin that was both clinically and statistically significant. These results further support the favourable safety profile of abiraterone acetate in patients with chemotherapy-naive metastatic castration-resistant prostate cancer. Janssen Research & Development.

> Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring 1 . However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete 2 – 12 . Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery. Deep whole-genome sequencing of serial blood samples and matched metastatic tissue reveals that circulating tumour DNA profiling enables detailed study of treatment-driven subclone dynamics, epigenomics and genome-wide somatic evolution in metastatic human cancers.

Soil moisture is an essential climate variable influencing land–atmosphere interactions, an essential hydrologic variable impacting rainfall–runoff processes, an essential ecological variable regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large‐scale soil moisture monitoring has advanced in recent years, creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication; and, for some applications, the science required to utilize large‐scale soil moisture data is poorly developed. In this review, we describe the state of the art in large‐scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of (i) emerging in situ and proximal sensing techniques, (ii) dedicated soil moisture remote sensing missions, (iii) soil moisture monitoring networks, and (iv) applications of large‐scale soil moisture measurements. Significant near‐term progress seems possible in the use of large‐scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to spatial variability and model structures remain. Little progress has been made in the use of large‐scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large‐scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting.

Among men with nonmetastatic prostate cancer who had a rapid PSA doubling time and were receiving androgen-deprivation therapy, the median metastasis-free survival was 40.5 months with apalutamide versus 16.2 months with placebo. Rash occurred in 24% of men receiving apalutamide.

microRNAs (miRNAs) play key roles in modulating a variety of cellular processes through repression of mRNA targets. In a screen for miRNAs regulated by myocardin-related transcription factor-A (MRTF-A), a coactivator of serum response factor (SRF), we discovered a muscle-enriched miRNA, miR-486, controlled by an alternative promoter within intron 40 of the Ankyrin-1 gene. Transcription of miR-486 is directly controlled by SRF and MRTF-A, as well as by MyoD. Among the most strongly predicted targets of miR-486 are phosphatase and tensin homolog (PTEN) and Foxo1a, which negatively affect phosphoinositide-3-kinase (PI3K)/Akt signaling. Accordingly, PTEN and Foxo1a protein levels are reduced by miR-486 overexpression, which, in turn, enhances PI3K/Akt signaling. Similarly, we show that MRTF-A promotes PI3K/Akt signaling by up-regulating miR-486 expression. Conversely, inhibition of miR-486 expression enhances the expression of PTEN and Foxo1a and dampens signaling through the PI3K/Akt-signaling pathway. Our findings implicate miR-486 as a downstream mediator of the actions of SRF/MRTF-A and MyoD in muscle cells and as a potential modulator of PI3K/Akt signaling.