Using and saving randomness

Randomness is ubiquitous and exceedingly useful in computer science. For example, in sparse recovery, randomized algorithms are more efficient and robust than their deterministic counterparts. At the same time, because random sources from the real world are often biased and defective with limited entropy, high-quality randomness is a precious resource. This motivates the studies of pseudorandomness and randomness extraction. In this thesis, we explore the role of randomness in these areas. Our research contributions broadly fall into two categories: learning structured signals and constructing pseudorandom objects. Learning a structured signal. One common task in audio signal processing is to compress an interval of observation through finding the dominating k frequencies in its Fourier transform. We study the problem of learning a Fourier-sparse signal from noisy samples, where [0, T] is the observation interval and the frequencies can be “off-grid”. Previous methods for this problem required the gap between frequencies to be above 1/T, which is necessary to robustly identify individual frequencies. We show that this gap is not necessary to recover the signal as a whole: for arbitrary k-Fourier-sparse signals under ℓ₂ bounded noise, we provide a learning algorithm with a constant factor growth of the noise and sample complexity polynomial in k and logarithmic in the bandwidth and signal-to-noise ratio. In addition to this, we introduce a general method to avoid a condition number depending on the signal family F and the distribution D of measurement in the sample vi complexity. In particular, for any linear family F with dimension d and any distribution D over the domain of F, we show that this method provides a robust learning algorithm with O(d log d) samples. Furthermore, we improve the sample complexity to O(d) via spectral sparsification (optimal up to a constant factor), which provides the best known result for a range of linear families such as low degree multivariate polynomials. Next, we generalize this result to an active learning setting, where we get a large number of unlabeled points from an unknown distribution and choose a small subset to label. We design a learning algorithm optimizing both the number of unlabeled points and the number of labels. Pseudorandomness. Next, we study hash families, which have simple forms in theory and efficient implementations in practice. The size of a hash family is crucial for many applications such as derandomization. In this thesis, we study the upper bound on the size of hash families to fulfill their applications in various problems. We first investigate the number of hash functions to constitute a randomness extractor, which is equivalent to the degree of the extractor. We present a general probabilistic method that reduces the degree of any given strong extractor to almost optimal, at least when outputting few bits. For various almost universal hash families including Toeplitz matrices, Linear Congruential Hash, and Multiplicative Universal Hash, this approach significantly improves the upper bound on the degree of strong extractors in these hash families. Then we consider explicit hash families and multiple-choice schemes in the classical problems of placing balls into bins. We construct explicit hash families of almost-polynomial size that derandomizes two classical multiple-choice schemes, which match the maximum loads of a perfectly random hash function.Computer Science

Global microRNA depletion suppresses tumor angiogenesis

MicroRNAs delicately regulate the balance of angiogenesis. Here we show that depletion of all microRNAs suppresses tumor angiogenesis. We generated microRNA-deficient tumors by knocking out Dicer1. These tumors are highly hypoxic but poorly vascularized, suggestive of deficient angiogenesis signaling. Expression profiling revealed that angiogenesis genes were significantly down-regulated as a result of the microRNA deficiency. Factor inhibiting hypoxia-inducible factor 1 (HIF-1), FIH1, is derepressed under these conditions and suppresses HIF transcription. Knocking out FIH1 using CRISPR/Cas9-mediated genome engineering reversed the phenotypes of microRNA-deficient cells in HIF transcriptional activity, VEGF production, tumor hypoxia, and tumor angiogenesis. Using multiplexed CRISPR/Cas9, we deleted regions in FIH1 3′ untranslated regions (UTRs) that contain microRNA-binding sites, which derepresses FIH1 protein and represses hypoxia response. These data suggest that microRNAs promote tumor responses to hypoxia and angiogenesis by repressing FIH1.Swedish Research CouncilHoward Hughes Medical Institute (International Student Research Fellowship)National Institutes of Health (U.S.) (grant number R01-CA133404)MIT-Harvard Center of Cancer Nanotechnology Excellence (grant no. U54-CA151884)David H. Koch Institute for Integrative Cancer Research at MIT (Marie D. and Pierre Casimir-Lambert Fund)National Cancer Institute (U.S.) (Koch Institute Support (core) Grant P30-CA14051))National Institutes of Health (U.S.) (grant EB016101-01A1)Damon Runyon Cancer Research Foundation (Research Fellow (DRG-2117-12)

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Search for the glueball candidates f0(1500) and fJ(1710) in gamma gamma collisions

Data taken with the ALEPH detector at LEP1 have been used to search for gamma gamma production of the glueball candidates f0(1500) and fJ(1710) via their decay to pi+pi-. No signal is observed and upper limits to the product of gamma gamma width and pi+pi- branching ratio of the f0(1500) and the fJ(1710) have been measured to be Gamma_(gamma gamma -> f0(1500)). BR(f0(1500)->pi+pi-) < 0.31 keV and Gamma_(gamma gamma -> fJ(1710)). BR(fJ(1710)->pi+pi-) < 0.55 keV at 95% confidence level.Comment: 10 pages, 3 figure

Inhibition of Hypoxia-Inducible Factor-1α (HIF-1α) Protein Synthesis by DNA damage inducing agents

10.1371/journal.pone.0010522PLoS ONE55

Results based on 124 cases of breast cancer and 97 controls from Taiwan suggest that the single nucleotide polymorphism (SNP309) in the MDM2 gene promoter is associated with earlier onset and increased risk of breast cancer

<p>Abstract</p> <p>Background</p> <p>It has been suggested that the single nucleotide polymorphism 309 (SNP309, T -> G) in the promoter region of the MDM2 gene is important for tumor development; however, with regards to breast cancer, inconsistent associations have been reported worldwide. It is speculated that these conflicting results may have arisen due to different patient subgroups and ethnicities studied. For the first time, this study explores the effect of the MDM2 SNP309 genotype on Taiwanese breast cancer patients.</p> <p>Methods</p> <p>Genomic DNA was obtained from the whole blood of 124 breast cancer patients and 97 cancer-free healthy women living in Taiwan. MDM2 SNP309 genotyping was carried out by restriction fragment length polymorphism (RFLP) assay. The multivariate logistic regression and the Kaplan-Meier method were used for analyzing the risk association and significance of age at diagnosis among different MDM2 SNP309 genotypes, respectively.</p> <p>Results</p> <p>Compared to the TT genotype, an increased risk association with breast cancer was apparent for the GG genotype (OR = 3.05, 95% CI = 1.04 to 8.95), and for the TG genotype (OR = 2.12, 95% CI = 0.90 to 5.00) after adjusting for age, cardiovascular disease/diabetes, oral contraceptive usage, and body mass index, which exhibits significant difference between cases and controls. Furthermore, the average ages at diagnosis for breast cancer patients were 53.6, 52 and 47 years for those harboring TT, TG and GG genotypes, respectively. A significant difference in median age of onset for breast cancer between GG and TT+TG genotypes was obtained by the log-rank test (p = 0.0067).</p> <p>Conclusion</p> <p>Findings based on the current sample size suggest that the MDM2 SNP309 GG genotype may be associated with both the risk of breast cancer and an earlier age of onset in Taiwanese women.</p

Genomic analysis and temperature-dependent transcriptome profiles of the rhizosphere originating strain Pseudomonas aeruginosa M18

<p>Abstract</p> <p>Background</p> <p>Our previously published reports have described an effective biocontrol agent named <it>Pseudomonas </it>sp. M18 as its 16S rDNA sequence and several regulator genes share homologous sequences with those of <it>P. aeruginosa</it>, but there are several unusual phenotypic features. This study aims to explore its strain specific genomic features and gene expression patterns at different temperatures.</p> <p>Results</p> <p>The complete M18 genome is composed of a single chromosome of 6,327,754 base pairs containing 5684 open reading frames. Seven genomic islands, including two novel prophages and five specific non-phage islands were identified besides the conserved <it>P. aeruginosa </it>core genome. Each prophage contains a putative chitinase coding gene, and the prophage II contains a <it>capB </it>gene encoding a putative cold stress protein. The non-phage genomic islands contain genes responsible for pyoluteorin biosynthesis, environmental substance degradation and type I and III restriction-modification systems. Compared with other <it>P. aeruginosa </it>strains, the fewest number (3) of insertion sequences and the most number (3) of clustered regularly interspaced short palindromic repeats in M18 genome may contribute to the relative genome stability. Although the M18 genome is most closely related to that of <it>P. aeruginosa </it>strain LESB58, the strain M18 is more susceptible to several antimicrobial agents and easier to be erased in a mouse acute lung infection model than the strain LESB58. The whole M18 transcriptomic analysis indicated that 10.6% of the expressed genes are temperature-dependent, with 22 genes up-regulated at 28°C in three non-phage genomic islands and one prophage but none at 37°C.</p> <p>Conclusions</p> <p>The <it>P. aeruginosa </it>strain M18 has evolved its specific genomic structures and temperature dependent expression patterns to meet the requirement of its fitness and competitiveness under selective pressures imposed on the strain in rhizosphere niche.</p