Statistical analysis of stratospheric temperature and ozone profile data for trends and model comparison

Work performed during the project period July 1, 1990 to June 30, 1992 on the statistical analysis of stratospheric temperature data, rawinsonde temperature data, and ozone profile data for the detection of trends is described. Our principal topics of research are trend analysis of NOAA stratospheric temperature data over the period 1978-1989; trend analysis of rawinsonde temperature data for the period 1964-1988; trend analysis of Umkehr ozone profile data for the period 1977-1991; and comparison of observed ozone and temperature trends in the lower stratosphere. Analysis of NOAA stratospheric temperature data indicates the existence of large negative trends at 0.4 mb level, with magnitudes increasing with latitudes away from the equator. Trend analysis of rawinsonde temperature data over 184 stations shows significant positive trends about 0.2 C per decade at surface to 500 mb range, decreasing to negative trends about -0.3 C at 100 to 50 mb range, and increasing slightly at 30 mb level. There is little evidence of seasonal variation in trends. Analysis of Umkehr ozone data for 12 northern hemispheric stations shows significant negative trends about -.5 percent per year in Umkehr layers 7-9 and layer 3, but somewhat less negative trends in layers 4-6. There is no pronounced seasonal variation in trends, especially in layers 4-9. A comparison was made of empirical temperature trends from rawinsonde data in the lower stratosphere with temperature changes determined from a one-dimensional radiative transfer calculation that prescribed a given ozone change over the altitude region, surface to 50 km, obtained from trend analysis of ozonsonde and Umkehr profile data. The empirical and calculated temperature trends are found in substantive agreement in profile shape and magnitude

Statistical diagnostic and correction of a chemistry-transport model for the prediction of total column ozone

International audienceIn this paper, we introduce a statistical method for examining and adjusting chemical-transport models. We illustrate the findings with total column ozone predictions, based on the University of Illinois at Urbana-Champaign 2-D (UIUC 2-D) chemical-transport model of the global atmosphere. We propose a general diagnostic procedure for the model outputs in total ozone over the latitudes ranging from 60° South to 60° North to see if the model captures some typical patterns in the data. The method proceeds in two steps to avoid possible collinearity issues. First, we regress the measurements given by a cohesive data set from the SBUV(/2) satellite system on the model outputs with an autoregressive noise component. Second, we regress the residuals of this first regression on the solar flux, the annual cycle, the Antarctic or Arctic Oscillation, and the Quasi Biennial Oscillation. If the coefficients from this second regression are statistically significant, then they mean that the model did not simulate properly the pattern associated with these factors. Systematic anomalies of the model are identified using data from 1979 to 1995, and statistically corrected afterwards. The 1996?2003 validation sample confirms that the combined approach yields better predictions than the direct UIUC 2-D outputs

Limiting distributions for explosive PAR(1) time series with strongly mixing innovation

This work deals with the limiting distribution of the least squares estimators of the coefficients a r of an explosive periodic autoregressive of order 1 (PAR(1)) time series X r = a r X r--1 +u r when the innovation {u k } is strongly mixing. More precisely {a r } is a periodic sequence of real numbers with period P \textgreater{} 0 and such that P r=1 |a r | \textgreater{} 1. The time series {u r } is periodically distributed with the same period P and satisfies the strong mixing property, so the random variables u r can be correlated

The comparison of grey-scale ultrasonic and clinical features of hepatoblastoma and hepatocellular carcinoma in children: a retrospective study for ten years

<p>Abstract</p> <p>Background</p> <p>Hepatoblastoma (HBL) and hepatocellular carcinoma (HCC) are respectively the first and the second most common pediatric malignant liver tumors. The purpose of this study was to evaluate the combined use of the ultrasound examination and the assessment of the patients' clinical features for differentiating HBL from HCC in children.</p> <p>Methods</p> <p>Thirty cases of the confirmed HBL and 12 cases of the confirmed HCC in children under the age of 15 years were enrolled into our study. They were divided into the HBL group and the HCC group according to the histological types of the tumors. The ultrasonic features and the clinical manifestations of the two groups were retrospectively analyzed, with an emphasis on the following parameters: onset age, gender (male/female) ratio, positive epatitis-B-surface-antigen (HBV), alpha-fetoprotein increase, and echo features including septa, calcification and liquefaction within the tumors.</p> <p>Results</p> <p>Compared with the children with HCC, the children with HBL had a significantly younger onset age (8.2 years vs. 3.9 years, P < 0.001) and a significantly smaller frequency of positive HBV (66.7% vs. 13.3%, P < 0.001). The septa and liquefaction were more frequently found in HBL than in HCC (25/30, 83.3% vs. 2/12, 16.7%, P < 0.001; 17/30, 56.7% vs. 3/12, 25%, P = 0.02). When a combination of the liquefaction, septa, negative HBV and onset age smaller than 5 years was used in the evaluation, the sensitivity was raised to 90%, the accuracy was raised to 88%, and the negative predictive value was raised to 73%.</p> <p>Conclusion</p> <p>Ultrasonic features combined with clinical manifestations are valuable for differentiating HBL from HCC in children.</p

The mutational constraint spectrum quantified from variation in 141,456 humans

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes(1). Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.Peer reviewe

Trends in the Vertical Distribution of Ozone: A Comparison of Two Analyses of Ozonesonde Data

We present the results of two independent analyses of ozonesonde measurements of the vertical profile of ozone. For most of the ozonesonde stations we use data that were recently reprocessed and reevaluated to improve their quality and internal consistency. The two analyses give similar results for trends in ozone. We attribute differences in results primarily to differences in data selection criteria and in utilization of data correction factors, rather than in statistical trend models. We find significant decreases in stratospheric ozone at all stations in middle and high latitudes of the northern hemisphere from 1970 to 1996, with the largest decreases located between 12 and 21 km, and trends of -3 to -10 %/decade near 17 km. The decreases are largest at the Canadian and the most northerly Japanese station, and are smallest at the European stations, and at Wallops Island, U.S.A. The mean mid-latitude trend is largest, -7 %/decade, from 12 to 17.5 km for 1970-96. For 1980-96, the decrease is more negative by 1-2 %/decade, with a maximum trend of -9 %/decade in the lowermost stratosphere. The trends vary seasonally from about 12 to 17.5 km, with largest ozone decreases in winter and spring. Trends in tropospheric ozone are highly variable and depend on region. There are decreases or zero trends at the Canadian stations for 1970-96, and decreases of -2 to -8 %/decade for the mid-troposphere for 1980-96; the three European stations show increases for 1970-96, but trends are close to zero for two stations for 1980-96 and positive for one; there are increases in ozone for the three Japanese stations for 1970-96, but trends are either positive or zero for 1980-96; the U.S. stations show zero or slightly negative trends in tropospheric ozone after 1980. It is not possible to define reliably a mean tropospheric ozone trend for northern mid-latitudes, given the small number of stations and the large variability in trends. The integrated column trends derived from the sonde data are consistent with trends derived from both surface based and satellite measurements of the ozone column

Development of an in-vitro model system to investigate the mechanism of muscle protein catabolism induced by proteolysis-inducing factor

The mechanism of muscle protein catabolism induced by proteolysis-inducing factor, produced by cachexia-inducing murine and human tumours has been studied in vitro using C2C12 myoblasts and myotubes. In both myoblasts and myotubes protein degradation was enhanced by proteolysis-inducing factor after 24 h incubation. In myoblasts this followed a bell-shaped dose-response curve with maximal effects at a proteolysis-inducing factor concentration between 2 and 4 nM, while in myotubes increased protein degradation was seen at all concentrations of proteolysis-inducing factor up to 10 nM, again with a maximum of 4 nM proteolysis-inducing factor. Protein degradation induced by proteolysis-inducing factor was completely attenuated in the presence of cycloheximide (1 μM), suggesting a requirement for new protein synthesis. In both myoblasts and myotubes protein degradation was accompanied by an increased expression of the α-type subunits of the 20S proteasome as well as functional activity of the proteasome, as determined by the ‘chymotrypsin-like’ enzyme activity. There was also an increased expression of the 19S regulatory complex as well as the ubiquitin-conjugating enzyme (E214k), and in myotubes a decrease in myosin expression was seen with increasing concentrations of proteolysis-inducing factor. These results show that proteolysis-inducing factor co-ordinately upregulates both ubiquitin conjugation and proteasome activity in both myoblasts and myotubes and may play an important role in the muscle wasting seen in cancer cachexia

Predicting Worst-Case Execution Time Trends in Long-Lived Real-Time Systems

In some long-lived real-time systems, it is not uncommon to see that the execution times of some tasks may exhibit trends. For hard and firm real-time systems, it is important to ensure these trends will not jeopardize the system. In this paper, we first introduce the notion of dynamic worst-case execution time (dWCET), which forms a new perspective that could help a system to predict potential timing failures and optimize resource allocations. We then have a comprehensive review of trend prediction methods. In the evaluation, we make a comparative study of dWCET trend prediction. Four prediction methods, combined with three data selection processes, are applied in an evaluation framework. The result shows the importance of applying data preprocessing and suggests that non-parametric estimators perform better than parametric methods

An Intermittent Live Cell Imaging Screen for siRNA Enhancers and Suppressors of a Kinesin-5 Inhibitor

Kinesin-5 (also known as Eg5, KSP and Kif11) is required for assembly of a bipolar mitotic spindle. Small molecule inhibitors of Kinesin-5, developed as potential anti-cancer drugs, arrest cell in mitosis and promote apoptosis of cancer cells. We performed a genome-wide siRNA screen for enhancers and suppressors of a Kinesin-5 inhibitor in human cells to elucidate cellular responses, and thus identify factors that might predict drug sensitivity in cancers. Because the drug's actions play out over several days, we developed an intermittent imaging screen. Live HeLa cells expressing GFP-tagged histone H2B were imaged at 0, 24 and 48 hours after drug addition, and images were analyzed using open-source software that incorporates machine learning. This screen effectively identified siRNAs that caused increased mitotic arrest at low drug concentrations (enhancers), and vice versa (suppressors), and we report siRNAs that caused both effects. We then classified the effect of siRNAs for 15 genes where 3 or 4 out of 4 siRNA oligos tested were suppressors as assessed by time lapse imaging, and by testing for suppression of mitotic arrest in taxol and nocodazole. This identified 4 phenotypic classes of drug suppressors, which included known and novel genes. Our methodology should be applicable to other screens, and the suppressor and enhancer genes we identified may open new lines of research into mitosis and checkpoint biology