Multiscale stochastic optimization: modeling aspects and scenario generation

Real-world multistage stochastic optimization problems are often characterized by the fact that the decision maker may take actions only at specific points in time, even if relevant data can be observed much more frequently. In such a case there are not only multiple decision stages present but also several observation periods between consecutive decisions, where profits/costs occur contingent on the stochastic evolution of some uncertainty factors. We refer to such multistage decision problems with encapsulated multiperiod random costs, as multiscale stochastic optimization problems. In this article, we present a tailor-made modeling framework for such problems, which allows for a computational solution. We first establish new results related to the generation of scenario lattices and then incorporate the multiscale feature by leveraging the theory of stochastic bridge processes. All necessary ingredients to our proposed modeling framework are elaborated explicitly for various popular examples, including both diffusion and jump models

S4S8-RPA phosphorylation as an indicator of cancer progression in oral squamous cell carcinomas.

Oral cancers are easily accessible compared to many other cancers. Nevertheless, oral cancer is often diagnosed late, resulting in a poor prognosis. Most oral cancers are squamous cell carcinomas that predominantly develop from cell hyperplasias and dysplasias. DNA damage is induced in these tissues directly or indirectly in response to oncogene-induced deregulation of cellular proliferation. Consequently, a DNA Damage response (DDR) and a cell cycle checkpoint is activated. As dysplasia transitions to cancer, proteins involved in DNA damage and checkpoint signaling are mutated or silenced decreasing cell death while increasing genomic instability and allowing continued tumor progression. Hyperphosphorylation of Replication Protein A (RPA), including phosphorylation of Ser4 and Ser8 of RPA2, is a well-known indicator of DNA damage and checkpoint activation. In this study, we utilize S4S8-RPA phosphorylation as a marker for cancer development and progression in oral squamous cell carcinomas (OSCC). S4S8-RPA phosphorylation was observed to be low in normal cells, high in dysplasias, moderate in early grade tumors, and low in late stage tumors, essentially supporting the model of the DDR as an early barrier to tumorigenesis in certain types of cancers. In contrast, overall RPA expression was not correlative to DDR activation or tumor progression. Utilizing S4S8-RPA phosphorylation to indicate competent DDR activation in the future may have clinical significance in OSCC treatment decisions, by predicting the susceptibility of cancer cells to first-line platinum-based therapies for locally advanced, metastatic and recurrent OSCC

Sequential Effects in Judgements of Attractiveness: The Influences of Face Race and Sex

In perceptual decision-making, a person’s response on a given trial is influenced by their response on the immediately preceding trial. This sequential effect was initially demonstrated in psychophysical tasks, but has now been found in more complex, real-world judgements. The similarity of the current and previous stimuli determines the nature of the effect, with more similar items producing assimilation in judgements, while less similarity can cause a contrast effect. Previous research found assimilation in ratings of facial attractiveness, and here, we investigated whether this effect is influenced by the social categories of the faces presented. Over three experiments, participants rated the attractiveness of own- (White) and other-race (Chinese) faces of both sexes that appeared successively. Through blocking trials by race (Experiment 1), sex (Experiment 2), or both dimensions (Experiment 3), we could examine how sequential judgements were altered by the salience of different social categories in face sequences. For sequences that varied in sex alone, own-race faces showed significantly less opposite-sex assimilation (male and female faces perceived as dissimilar), while other-race faces showed equal assimilation for opposite- and same-sex sequences (male and female faces were not differentiated). For sequences that varied in race alone, categorisation by race resulted in no opposite-race assimilation for either sex of face (White and Chinese faces perceived as dissimilar). For sequences that varied in both race and sex, same-category assimilation was significantly greater than opposite-category. Our results suggest that the race of a face represents a superordinate category relative to sex. These findings demonstrate the importance of social categories when considering sequential judgements of faces, and also highlight a novel approach for investigating how multiple social dimensions interact during decision-making

A direct test of the unequal-variance signal detection model of recognition memory

Analyses of the receiver operating characteristic (ROC) almost invariably suggest that, on a recognition memory test, the standard deviation of memory strengths associated with the lures (sigma(lure)) is smaller than that of the targets (sigma(target)). Often, sigma(lure)/ sigma(target) approximately = 0.80. However, that conclusion is based on a model that assumes that the memory strength distributions are Gaussian in form. In two experiments, we investigated this issue in a more direct way by asking subjects to simply rate the memory strengths of targets and lures using a 20-point or a 99-point strength scale. The results showed that the standard deviation of the ratings made to the targets (S(target)) was, indeed, larger than the standard deviation of the ratings made to the lures (S(lure)). Moreover, across subjects, the ratio S(lure)/ S(target) correlated highly with the estimate of sigma(lure)/ sigma(target) obtained from ROC analysis, and both estimates were, on average, approximately equal to 0.80.</p

Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications

Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of Advanced LIGO. We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory

Identification of genes and pathways associated with cytotoxic T lymphocyte infiltration of serous ovarian cancer

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are predictors of disease-specific survival (DSS) in ovarian cancer. It is largely unknown what factors contribute to lymphocyte recruitment. Our aim was to evaluate genes and pathways contributing to infiltration of cytotoxic T lymphocytes (CTLs) in advanced-stage serous ovarian cancer. METHODS: For this study global gene expression was compared between low TIL (n=25) and high TIL tumours (n=24). The differences in gene expression were evaluated using parametric T-testing. Selectively enriched biological pathways were identified with gene set enrichment analysis. Prognostic influence was validated in 157 late-stage serous ovarian cancer patients. Using immunohistochemistry, association of selected genes from identified pathways with CTL was validated. RESULTS: The presence of CTL was associated with 320 genes and 23 pathways (P<0.05). In addition, 54 genes and 8 pathways were also associated with DSS in our validation cohort. Immunohistochemical evaluation showed strong correlations between MHC class I and II membrane expression, parts of the antigen processing and presentation pathway, and CTL recruitment. CONCLUSION: Gene expression profiling and pathway analyses are valuable tools to obtain more understanding of tumour characteristics influencing lymphocyte recruitment in advanced-stage serous ovarian cancer. Identified genes and pathways need to be further investigated for suitability as therapeutic targets