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

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

Aspergillus sclerotiorum fungus is lethal to both Western drywood (Incisitermes minor) and Western subterranean (Reticulitermes hesperus)termites

Termite control costs $1.5 billion per year in the United States alone, and methods for termite control usually consist of chemical pesticides. However, these methods have their drawbacks, which include the development of resistance, environmental pollution, and toxicity to other organisms. Biological termite control, which employs the use of living organisms to combat pests, offers an alternative to chemical pesticides. This study highlights the discovery of a fungus, termed “APU strain,” that was hypothesized to be pathogenic to termites. Phylogenetic and morphological analysis showed that the fungus is a strain of Aspergillus sclerotiorum, andexperiments showed that both western drywood (Incisitermes minor) and western subterranean (Reticulitermes hesperus) termites die in a dose-dependent manner exposed to fungal spores of A. sclerotiorum APU strain. In addition, exposure to the A. sclerotiorum Huber strain elicited death in a similar manner as the APU strain. The mechanism by which the fungus caused termite death is still unknown and warrants further investigation. While these results support that A. sclerotiorum is a termite pathogen, further studies are needed to determine whether the fungal species has potential as a biological control agent

Noise in the LIGO livingston gravitational wave observatory due to trains

Environmental seismic disturbances limit the sensitivity of LIGO gravitational wave detectors. Trains near the LIGO Livingston detector produce low frequency (0.5- 10 H z ) ground noise that couples into the gravitational wave sensitive frequency band (10- 100 H z ) through light reflected in mirrors and other surfaces. We investigate the effect of trains during the Advanced LIGO third observing run, and propose a method to search for narrow band seismic frequencies responsible for contributing to increases in scattered light. Through the use of the linear regression tool Lasso (least absolute shrinkage and selection operator) and glitch correlations, we identify the most common seismic frequencies that correlate with increases in detector noise as 0.6- 0.8 H z , 1.7- 1.9 H z , 1.8- 2.0 H z , and 2.3- 2.5 H z in the LIGO Livingston corner station

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

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

A molecular analysis by gene expression profiling reveals Bik/NBK overexpression in sporadic breast tumor samples of Mexican females

BACKGROUND: Breast cancer is one of the most frequent causes of death in Mexican women over 35 years of age. At molecular level, changes in many genetic networks have been reported as associated with this neoplasia. To analyze these changes, we determined gene expression profiles of tumors from Mexican women with breast cancer at different stages and compared these with those of normal breast tissue samples. METHODS: (32)P-radiolabeled cDNA was synthesized by reverse transcription of mRNA from fresh sporadic breast tumor biopsies, as well as normal breast tissue. cDNA probes were hybridized to microarrays and expression levels registered using a phosphorimager. Expression levels of some genes were validated by real time RT-PCR and immunohistochemical assays. RESULTS: We identified two subgroups of tumors according to their expression profiles, probably related with cancer progression. Ten genes, unexpressed in normal tissue, were turned on in some tumors. We found consistent high expression of Bik gene in 14/15 tumors with predominant cytoplasmic distribution. CONCLUSION: Recently, the product of the Bik gene has been associated with tumoral reversion in different neoplasic cell lines, and was proposed as therapy to induce apoptosis in cancers, including breast tumors. Even though a relationship among genes, for example those from a particular pathway, can be observed through microarrays, this relationship might not be sufficient to assign a definitive role to Bik in development and progression of the neoplasia. The findings herein reported deserve further investigation