DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines

Heterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I² statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Utilizing the Gene Set Enrichment Analysis software and the ConsensusPath Database visualization tool, we generate integrated gene networks to study biological relations of heterogeneous genes. This analysis has allowed us to contribute 19 potential breast cancer biomarker genes to cancer databases by locating “hub genes” – heterogeneous genes of significant biological interactions, selected from numerous cancer modules. We have discovered a considerable relationship between these hub genes and heterogeneously methylated oncogenes. Our results have many implications for further heterogeneity analyses of methylation patterns and early detection of breast cancer susceptibility

Pruning, Pushdown Exception-Flow Analysis

Statically reasoning in the presence of exceptions and about the effects of exceptions is challenging: exception-flows are mutually determined by traditional control-flow and points-to analyses. We tackle the challenge of analyzing exception-flows from two angles. First, from the angle of pruning control-flows (both normal and exceptional), we derive a pushdown framework for an object-oriented language with full-featured exceptions. Unlike traditional analyses, it allows precise matching of throwers to catchers. Second, from the angle of pruning points-to information, we generalize abstract garbage collection to object-oriented programs and enhance it with liveness analysis. We then seamlessly weave the techniques into enhanced reachability computation, yielding highly precise exception-flow analysis, without becoming intractable, even for large applications. We evaluate our pruned, pushdown exception-flow analysis, comparing it with an established analysis on large scale standard Java benchmarks. The results show that our analysis significantly improves analysis precision over traditional analysis within a reasonable analysis time.Comment: 14th IEEE International Working Conference on Source Code Analysis and Manipulatio

Reconstruction of tokamak plasma safety factor profile using deep learning

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep learning-based surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system

Fast Flow Analysis with Godel Hashes

Abstract—Flow analysis, such as control-flow, data-flow, and exception-flow analysis, usually depends on relational operations on flow sets. Unfortunately, set related operations, such as inclusion and equality, are usually very expensive. They can easily take more than 97 % of the total analyzing time, even in a very simple analysis. We attack this performance bottleneck by proposing Gödel hashes to enable fast and precise flow analysis. Gödel hashes is an ultra compact, partial-order-preserving, fast and perfect hashing mechanism, inspired by the proofs of Gödel’s incompleteness theorems. Compared with array-, tree-, traditional hash-, and bit vector-backed set implementations, we find Gödel hashes to be tens or even hundreds of times faster for performance in the critical operations of inclusion and equality. We apply Gödel hashes in real-world analysis for object-oriented programs. The instrumented analysis is tens of times faster than the one with original data structures on DaCapo benchmarks. I

Attributions of emission-reduction and meteorological conditions to typical heavy pollution episodes in a cold metropolis, northeast China

Heavy pollution episodes frequently occurred in winter in northeast China due to the multiple anthropogenic emissions coupled with adverse meteorological conditions, which increased the difficulty of environmental pollution control. To better enact strategies for mitigating air pollution in the post-pandemic era, daily pollutant concentration monitoring and meteorological data were used to evaluate the changes and meteorological factors of air pollutants before (2019) and during (2020) the lockdown in Harbin City, northeast China. Moreover, typical pollution episodes under COVID-19 lockdown were identified, and their emission sources, meteorology conditions, and regional pollution transportation were analyzed. The results showed significant decreases in NO2, PM10 and CO, while O3 increased, and no differences in PM2.5 and SO2 during the lockdown compared with non-lockdown periods. It indicated that reduced activities of transportation resulted in reductions of NO2 concentrations by 16%, and stationary emission sources were less affected. Correlation between PM2.5 and O3 tended to change from positive to negative as the threshold of PM2.5 = 90 μg m−3, with the main controlling factor changed from their common gaseous precursors to meteorological conditions (temperature <0°C and wind speed <2 m s−1). Pollution days were concentrated in the COVID-19 lockdown period with PM2.5 as the primary pollutant. SO2 dominant pollution and PM2.5 dominant pollution were distinguished from six sustained heavy pollution events. PM2.5 and SO2 played essential roles in SO2 dominant pollution, which derived from local emissions of coal combustion and firework discharge. PM2.5 dominant pollution might be chemical transformed from coal burning, vehicle exhaust, and other secondary precursors, which was affected and aggravated by CO, NO2, high relative humidity and low wind speed affected by local emission and long-distance transport

How old is this mutation? - a study of three Ashkenazi Jewish founder mutations

Abstract Background Several founder mutations leading to increased risk of cancer among Ashkenazi Jewish individuals have been identified, and some estimates of the age of the mutations have been published. A variety of different methods have been used previously to estimate the age of the mutations. Here three datasets containing genotype information near known founder mutations are reanalyzed in order to compare three approaches for estimating the age of a mutation. The methods are: (a) the single marker method used by Risch et al., (1995); (b) the intra-allelic coalescent model known as DMLE, and (c) the Goldgar method proposed in Neuhausen et al. (1996), and modified slightly by our group. The three mutations analyzed were MSH2*1906 G->C, APC*I1307K, and BRCA2*6174delT. Results All methods depend on accurate estimates of inter-marker recombination rates. The modified Goldgar method allows for marker mutation as well as recombination, but requires prior estimates of the possible haplotypes carrying the mutation for each individual. It does not incorporate population growth rates. The DMLE method simultaneously estimates the haplotypes with the mutation age, and builds in the population growth rate. The single marker estimates, however, are more sensitive to the recombination rates and are unstable. Mutation age estimates based on DMLE are 16.8 generations for MSH2 (95% credible interval (13, 23)), 106 generations for I1037K (86-129), and 90 generations for 6174delT (71-114). Conclusions For recent founder mutations where marker mutations are unlikely to have occurred, both DMLE and the Goldgar method can give good results. Caution is necessary for older mutations, especially if the effective population size may have remained small for a long period of time