Improved gnss-r altimetry methods: Theory and experimental demonstration using airborne dual frequency data from the microwave interferometric reflectometer (mir)

Altimetric performance of Global Navigation Satellite System - Reflectometry (GNSS-R) instruments depends on receiver’s bandwidth and signal-to-noise ratio (SNR). The altimetric delay is usually computed from the time difference between the peak of the direct signal waveform and the maximum of the derivative of the reflected signal waveform. Dual-frequency data gathered by the airborne Microwave Interferometric Reflectometer (MIR) in the Bass Strait, between Australia and Tasmania, suggest that this approach is only valid for flat surfaces and large bandwidth receivers. This work analyses different methods to compute the altimetric observables using GNSS-R. A proposed novel methodThis work was funded by the Spanish Ministry of Science, Innovation and Universities, “Sensing with Pioneering Opportunistic Techniques”, grant RTI2018-099008-B-C21/AEI/10.13039/ 501100011033, and the grant for recruitment of early-stage research staff FI-DGR 2015 of the AGAUR— Generalitat de Catalunya (FEDER), Spain, and the grant for recruitment of early-stage research staff FI 2018 of the AGAUR—Generalitat de Catalunya (FEDER), Spain, and Unidad de Excelencia María de Maeztu MDM-2016-060Postprint (published version

Nonsteroidal Anti-inflammatory Drugs and Endometrial Carcinoma Mortality and Recurrence

Background: Recent data suggest that the use of nonsteroidal anti-inflammatory drugs (NSAIDs) may be associated with reductions in endometrial cancer risk, yet very few have examined whether their use is related to prognosis among endometrial cancer patients

On the nature and impact of self-similarity in real-time systems

In real-time systems with highly variable task execution times simplistic task models are insufficient to accurately model and to analyze the system. Variability can be tackled using distributions rather than a single value, but the proper charac- terization depends on the degree of variability. Self-similarity is one of the deep- est kinds of variability. It characterizes the fact that a workload is not only highly variable, but it is also bursty on many time-scales. This paper identifies in which situations this source of indeterminism can appear in a real-time system: the com- bination of variability in task inter-arrival times and execution times. Although self- similarity is not a claim for all systems with variable execution times, it is not unusual in some applications with real-time requirements, like video processing, networking and gaming. The paper shows how to properly model and to analyze self-similar task sets and how improper modeling can mask deadline misses. The paper derives an analyti- cal expression for the dependence of the deadline miss ratio on the degree of self- similarity and proofs its negative impact on real-time systems performance through system¿s modeling and simulation. This study about the nature and impact of self- similarity on soft real-time systems can help to reduce its effects, to choose the proper scheduling policies, and to avoid its causes at system design time.This work was developed under a grant from the European Union (FRESCOR-FP6/2005/IST/5-03402).Enrique Hernández-Orallo; Vila Carbó, JA. (2012). On the nature and impact of self-similarity in real-time systems. Real-Time Systems. 48(3):294-319. doi:10.1007/s11241-012-9146-0S294319483Abdelzaher TF, Sharma V, Lu C (2004) A utilization bound for aperiodic tasks and priority driven scheduling. IEEE Trans Comput 53(3):334–350Abeni L, Buttazzo G (1999) QoS guarantee using probabilistic deadlines. In: Proc of the Euromicro confererence on real-time systemsAbeni L, Buttazzo G (2004) Resource reservation in dynamic real-time systems. Real-Time Syst 37(2):123–167Anantharam V (1999) Scheduling strategies and long-range dependence. Queueing Syst 33(1–3):73–89Beran J (1994) Statistics for long-memory processes. Chapman and Hall, LondonBeran J, Sherman R, Taqqu M, Willinger W (1995) Long-range dependence in variable-bit-rate video traffic. IEEE Trans Commun 43(2):1566–1579Boxma O, Zwart B (2007) Tails in scheduling. SIGMETRICS Perform Eval Rev 34(4):13–20Brichet F, Roberts J, Simonian A, Veitch D (1996) Heavy traffic analysis of a storage model with long range dependent on/off sources. Queueing Syst 23(1):197–215Crovella M, Bestavros A (1997) Self-similarity in world wide web traffic: evidence and possible causes. IEEE/ACM Trans Netw 5(6):835–846Dìaz J, Garcìa D, Kim K, Lee C, Bello LL, López J, Min LS, Mirabella O (2002) Stochastic analysis of periodic real-time systems. In: Proc of the 23rd IEEE real-time systems symposium, pp 289–300Erramilli A, Narayan O, Willinger W (1996) Experimental queueing analysis with long-range dependent packet traffic. IEEE/ACM Trans Netw 4(2):209–223Erramilli A, Roughan M, Veitch D, Willinger W (2002) Self-similar traffic and network dynamics. Proc IEEE 90(5):800–819Gardner M (1999) Probabilistic analysis and scheduling of critical soft real-time systems. Phd thesis, University of Illinois, Urbana-ChampaignGarrett MW, Willinger W (1994) Analysis, modeling and generation of self-similar vbr video traffic. In: ACM SIGCOMMHarchol-Balter M (2002) Task assignment with unknown duration. J ACM 49(2):260–288Harchol-Balter M (2007) Foreword: Special issue on new perspective in scheduling. SIGMETRICS Perform Eval Rev 34(4):2–3Harchol-Balter M, Downey AB (1997) Exploiting process lifetime distributions for dynamic load balancing. ACM Trans Comput Syst 15(3):253–285Hernandez-Orallo E, Vila-Carbo J (2007) Network performance analysis based on histogram workload models. In: Proceedings of the 15th international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS), pp 331–336Hernandez-Orallo E, Vila-Carbo J (2010) Analysis of self-similar workload on real-time systems. In: IEEE real-time and embedded technology and applications symposium (RTAS). IEEE Computer Society, Washington, pp 343–352Hernández-Orallo E, Vila-Carbó J (2010) Network queue and loss analysis using histogram-based traffic models. Comput Commun 33(2):190–201Hughes CJ, Kaul P, Adve SV, Jain R, Park C, Srinivasan J (2001) Variability in the execution of multimedia applications and implications for architecture. SIGARCH Comput Archit News 29(2):254–265Leland W, Ott TJ (1986) Load-balancing heuristics and process behavior. SIGMETRICS Perform Eval Rev 14(1):54–69Leland WE, Taqqu MS, Willinger W, Wilson DV (1994) On the self-similar nature of ethernet traffic (extended version). IEEE/ACM Trans Netw 2(1):1–15Liu CL, Layland JW (1973) Scheduling algorithms for multiprogramming in a hard-real-time environment. J ACM 20(1):46–61Mandelbrot B (1965) Self-similar error clusters in communication systems and the concept of conditional stationarity. IEEE Trans Commun 13(1):71–90Mandelbrot BB (1969) Long run linearity, locally Gaussian processes, h-spectra and infinite variances. Int Econ Rev 10:82–113Norros I (1994) A storage model with self-similar input. Queueing Syst 16(3):387–396Norros I (2000) Queueing behavior under fractional Brownian traffic. In: Park K, Willinger W (eds) Self-similar network traffic and performance evaluation. Willey, New York, Chap 4Park K, Willinger W (2000) Self-similar network traffic: An overview. In: Park K, Willinger W (eds) Self-similar network traffic and performance evaluation. Willey, New York, Chap 1Paxson V, Floyd S (1995) Wide area traffic: the failure of Poisson modeling. IEEE/ACM Trans Netw 3(3):226–244Rolls DA, Michailidis G, Hernández-Campos F (2005) Queueing analysis of network traffic: methodology and visualization tools. Comput Netw 48(3):447–473Rose O (1995) Statistical properties of mpeg video traffic and their impact on traffic modeling in atm systems. In: Conference on local computer networksRoy N, Hamm N, Madhukar M, Schmidt DC, Dowdy L (2009) The impact of variability on soft real-time system scheduling. In: RTCSA ’09: Proceedings of the 2009 15th IEEE international conference on embedded and real-time computing systems and applications. IEEE Computer Society, Washington, pp 527–532Sha L, Abdelzaher T, Årzén KE, Cervin A, Baker T, Burns A, Buttazzo G, Caccamo M, Lehoczky J, Mok AK (2004) Real time scheduling theory: A historical perspective. Real-Time Syst 28(2):101–155Taqqu MS, Willinger W, Sherman R (1997) Proof of a fundamental result in self-similar traffic modeling. SIGCOMM Comput Commun Rev 27(2):5–23Tia T, Deng Z, Shankar M, Storch M, Sun J, Wu L, Liu J (1995) Probabilistic performance guarantee for real-time tasks with varying computation times. In: Proc of the real-time technology and applications symposium, pp 164–173Vila-Carbó J, Hernández-Orallo E (2008) An analysis method for variable execution time tasks based on histograms. Real-Time Syst 38(1):1–37Willinger W, Taqqu M, Erramilli A (1996) A bibliographical guide to self-similar traffic and performance modeling for modern high-speed networks. In: Stochastic networks: Theory and applications, pp 339–366Willinger W, Taqqu MS, Sherman R, Wilson DV (1997) Self-similarity through high-variability: statistical analysis of ethernet lan traffic at the source level. IEEE/ACM Trans Netw 5(1):71–8

Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers

Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2 x 10(53)). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2 x 10(-20)). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management

BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

Background: The K3326X variant in BRCA2 (BRCA2*c.9976A>T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations

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

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

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

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