Optimal Single-Choice Prophet Inequalities from Samples

We study the single-choice Prophet Inequality problem when the gambler is given access to samples. We show that the optimal competitive ratio of $1/2$ can be achieved with a single sample from each distribution. When the distributions are identical, we show that for any constant $\varepsilon > 0$, $O(n)$ samples from the distribution suffice to achieve the optimal competitive ratio ($\approx 0.745$) within $(1+\varepsilon)$, resolving an open problem of Correa, D\"utting, Fischer, and Schewior.Comment: Appears in Innovations in Theoretical Computer Science (ITCS) 202

Variance Reduction Techniques in Monte Carlo Methods

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develop ever more realistic models, so that the net result has not been faster execution of simulation experiments; e.g., some modern simulation models need hours or days for a single ’run’ (one replication of one scenario or combination of simulation input values). Moreover there are some simulation models that represent rare events which have extremely small probabilities of occurrence), so even modern computer would take ’for ever’ (centuries) to execute a single run - were it not that special VRT can reduce theses excessively long runtimes to practical magnitudes.common random numbers;antithetic random numbers;importance sampling;control variates;conditioning;stratied sampling;splitting;quasi Monte Carlo

Exploiting Machine Learning to Subvert Your Spam Filter

Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1 % of the training messages. We further demonstrate a new class of focused attacks that successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.

DHODH modulates transcriptional elongation in the neural crest and melanoma

Melanoma is a tumour of transformed melanocytes, which are originally derived from the embryonic neural crest. It is unknown to what extent the programs that regulate neural crest development interact with mutations in the BRAF oncogene, which is the most commonly mutated gene in human melanoma1. We have used zebrafish embryos to identify the initiating transcriptional events that occur on activation of human BRAF(V600E) (which encodes an amino acid substitution mutant of BRAF) in the neural crest lineage. Zebrafish embryos that are transgenic for mitfa:BRAF(V600E) and lack p53 (also known as tp53) have a gene signature that is enriched for markers of multipotent neural crest cells, and neural crest progenitors from these embryos fail to terminally differentiate. To determine whether these early transcriptional events are important for melanoma pathogenesis, we performed a chemical genetic screen to identify small-molecule suppressors of the neural crest lineage, which were then tested for their effects on melanoma. One class of compound, inhibitors of dihydroorotate dehydrogenase (DHODH), for example leflunomide, led to an almost complete abrogation of neural crest development in zebrafish and to a reduction in the self-renewal of mammalian neural crest stem cells. Leflunomide exerts these effects by inhibiting the transcriptional elongation of genes that are required for neural crest development and melanoma growth. When used alone or in combination with a specific inhibitor of the BRAF(V600E) oncogene, DHODH inhibition led to a marked decrease in melanoma growth both in vitro and in mouse xenograft studies. Taken together, these studies highlight developmental pathways in neural crest cells that have a direct bearing on melanoma formation

mTOR Kinase Inhibition Effectively Decreases Progression of a Subset of Neuroendocrine Tumors that Progress on Rapalog Therapy and Delays Cardiac Impairment

Inhibition of mTOR signaling using the rapalog everolimus is an FDA-approved targeted therapy for patients with lung and gastroenteropancreatic neuroendocrine tumors (NET). However, patients eventually progress on treatment, highlighting the need for additional therapies. We focused on pancreatic NETs (pNET) and reasoned that treatment of these tumors upon progression on rapalog therapy, with an mTOR kinase inhibitor (mTORKi), such as CC-223, could overcome a number of resistance mechanisms in tumors and delay cardiac carcinoid disease. We performed preclinical studies using human pNET cells in vitro and injected them subcutaneously or orthotopically to determine tumor progression and cardiac function in mice treated with either rapamycin alone or switched to CC-223 upon progression. Detailed signaling and RNA sequencing analyses were performed on tumors that were sensitive or progressed on mTOR treatment. Approximately 57% of mice bearing pNET tumors that progressed on rapalog therapy showed a significant decrease in tumor volume upon a switch to CC-223. Moreover, mice treated with an mTORKi exhibited decreased cardiac dilation and thickening of heart valves than those treated with placebo or rapamycin alone. In conclusion, in the majority of pNETs that progress on rapalogs, it is possible to reduce disease progression using an mTORKi, such as CC-223. Moreover, CC-223 had an additional transient cardiac benefit on valvular fibrosis compared with placebo- or rapalog-treated mice. These results provide the preclinical rationale to further develop mTORKi clinically upon progression on rapalog therapy and to further test their long-term cardioprotective benefit in those NET patients prone to carcinoid syndrome

DRD4 genotype predicts longevity in mouse and human

Longevity is influenced by genetic and environmental factors. The brain's dopamine system may be particularly relevant, since it modulates traits (e.g., sensitivity to reward, incentive motivation, sustained effort) that impact behavioral responses to the environment. In particular, the dopamine D4 receptor (DRD4) has been shown to moderate the impact of environments on behavior and health. We tested the hypothesis that the DRD4 gene influences longevity and that its impact is mediated through environmental effects. Surviving participants of a 30-year-old population-based health survey (N = 310; age range, 90-109 years; the 90+ Study) were genotyped/resequenced at the DRD4 gene and compared with a European ancestry-matched younger population (N = 2902; age range, 7-45 years). We found that the oldest-old population had a 66% increase in individuals carrying the DRD4 7R allele relative to the younger sample (p = 3.5 × 10(-9)), and that this genotype was strongly correlated with increased levels of physical activity. Consistent with these results, DRD4 knock-out mice, when compared with wild-type and heterozygous mice, displayed a 7-9.7% decrease in lifespan, reduced spontaneous locomotor activity, and no lifespan increase when reared in an enriched environment. These results support the hypothesis that DRD4 gene variants contribute to longevity in humans and in mice, and suggest that this effect is mediated by shaping behavioral responses to the environment.Fil: Grady, Deborah L.. University of California. College of Medicine. Department of Biological Chemistry; Estados UnidosFil: Thanos, Panayotis K.. National Institute on Alcohol Abuse and Alcoholism. Laboratory of Neuroimaging; Estados Unidos. Brookhaven National Laboratory. Medical Department. Behavioral Neuropharmocology and Neuroimaging Laboratory; Estados Unidos. Stony Brook University. Department of Psychology; Estados UnidosFil: Corrada, Maria M.. University of California. Department of Neurology; Estados UnidosFil: Barnett Jr., Jeffrey C.. Brookhaven National Laboratory. Medical Department. Behavioral Neuropharmocology and Neuroimaging Laboratory; Estados UnidosFil: Ciobanu, Valentina. University of California. College of Medicine. Department of Biological Chemistry; Estados UnidosFil: Shustarovich, Diana. Brookhaven National Laboratory. Medical Department. Behavioral Neuropharmocology and Neuroimaging Laboratory; Estados UnidosFil: Napoli, Anthony. Brookhaven National Laboratory. Medical Department. Behavioral Neuropharmocology and Neuroimaging Laboratory; Estados UnidosFil: Moyzis, Alexandra G.. University of California. College of Medicine. Department of Biological Chemistry; Estados UnidosFil: Grandy, David. Oregon Health Sciences University. Physiology and Pharmacology; Estados UnidosFil: Rubinstein, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular; ArgentinaFil: Wang, Gene-Jack. Brookhaven National Laboratory. Medical Department. Behavioral Neuropharmocology and Neuroimaging Laboratory; Estados UnidosFil: Kawas, Claudia H.. University of California. Department of Neurology; Estados UnidosFil: Chen, Chuansheng. University of California. Department of Psychology and Social Behavior; Estados UnidosFil: Dong, Qi. Beijing Normal University. National Key Laboratory of Cognitive Neuroscience and Learning; ChinaFil: Wang, Eric. University of California. College of Medicine. Department of Biological Chemistry; Estados Unidos. Aria Diagnostics Inc.; Estados Unidos. University of California. Institute of Genomics and Bioinformatics; Estados UnidosFil: Volkow, Nora D.. National Institute on Alcohol Abuse and Alcoholism. Laboratory of Neuroimaging; Estados Unidos. Brookhaven National Laboratory. Medical Department. Behavioral Neuropharmocology and Neuroimaging Laboratory; Estados Unidos. National Institute on Drug Abuse; Estados UnidosFil: Moyzis, Robert K.. University of California. College of Medicine. Department of Biological Chemistry; Estados Unidos. Beijing Normal University. National Key Laboratory of Cognitive Neuroscience and Learning; China. University of California. Institute of Genomics and Bioinformatics; Estados Unido

The relationships between health information behavior and neural processing in african americans with prehypertension

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150533/1/asi24098_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150533/2/asi24098.pd