A Target Sequential Effect on the Forced-Choice Prime Visibility Test in Unconscious Priming Studies: A Caveat for Researchers

In unconscious priming studies, most researchers adopt a combination of subjective and objective measures to assess the visibility of the prime. Although some carry out the visibility test at the end of the experiment separately from the unconscious priming task, others suggest that the forced-choice visibility test should be conducted immediately after the response to the target within each trial. In the present study, the influence of prime and target on the forced-choice prime discrimination was assessed within each trial. The results showed that the target affected the response in the forced-choice prime visibility test. Participants tended to make the same response or avoid repeating the same response they made to the target as in Experiments 1 and 3 rather than randomly guessing. However, even when the forcedchoice visibility test was conducted separately from the priming experiment, the problem was not completely solved, because some participants tended to make one same response in the forced-choice visibility test as in Experiments 2. From another point of view, using these strategies in the forced-choice task can be seen as a helpless move by the participants when they are unaware of the stimuli. Furthermore, the results revealed that the forced-choice test performed immediately after the response to the target within each trial could possibly impair the unconscious priming as well as produce misleading visibility test results. Therefore, it is suggested that the forced-choice prime visibility test and the unconscious priming task may better be conducted separately

Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure

Sequential recommendation (SR) models are typically trained on user-item interactions which are affected by the system exposure bias, leading to the user preference learned from the biased SR model not being fully consistent with the true user preference. Exposure bias refers to the fact that user interactions are dependent upon the partial items exposed to the user. Existing debiasing methods do not make full use of the system exposure data and suffer from sub-optimal recommendation performance and high variance. In this paper, we propose to debias sequential recommenders through Distributionally Robust Optimization (DRO) over system exposure data. The key idea is to utilize DRO to optimize the worst-case error over an uncertainty set to safeguard the model against distributional discrepancy caused by the exposure bias. The main challenge to apply DRO for exposure debiasing in SR lies in how to construct the uncertainty set and avoid the overestimation of user preference on biased samples. Moreover, how to evaluate the debiasing effect on biased test set is also an open question. To this end, we first introduce an exposure simulator trained upon the system exposure data to calculate the exposure distribution, which is then regarded as the nominal distribution to construct the uncertainty set of DRO. Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples. Finally, we design a debiased self-normalized inverse propensity score (SNIPS) evaluator for evaluating the debiasing effect on the biased offline test set. We conduct extensive experiments on two real-world datasets to verify the effectiveness of the proposed methods. Experimental results demonstrate the superior exposure debiasing performance of proposed methods. Codes and data are available at \url{https://github.com/nancheng58/DebiasedSR_DRO}.Comment: Accept by WSDM 202

Chloride Ion Penetration Resistance of Reactive Powder Concrete with Mineral Admixtures

This study employed the rapid chloride ion penetration test and the salt spray erosion method to examine electric flux changes in mineral-admixed reactive powder concrete (RPC). Variations in the chloride ion content and diffusion coefficient under different erosion durations and depths were also investigated. The impact of mineral admixtures on the chloride penetration resistance was explored. Notably, after mixing fly ash (FA) and granulated blast furnace slag (GGBS), the electric flux values of RPC of each group were significantly reduced, and the electric flux values of RPC of the mixed group were significantly lower than those of the single mixed group and the reference group, in which the electric flux of FA10G10 was reduced by 85.2% compared to the control group; at the same erosion cycle and depth, the chloride ion content and diffusion coefficient of the mixed group were significantly lower than the control group. It shows that the reasonable compounding of mineral admixtures can better exert the "superposition effect", improve the compactness inside the matrix, and effectively reduce the chloride ion penetration rate. Considering comprehensively, the FA10G10 group has the best chloride penetration ion resistance effect

Diptoindonesin G is a middle domain HSP90 modulator for cancer treatment

HSP90 inhibitors can target many oncoproteins simultaneously, but none have made it through clinical trials due to dose-limiting toxicity and induction of heat shock response, leading to clinical resistance. We identified diptoindonesin G (dip G) as an HSP90 modulator that can promote degradation of HSP90 clients by binding to the middle domain of HSP90 (

Identification of genetic susceptibility for Chinese migraine with depression using machine learning

BackgroundMigraine is a common primary headache that has a significant impact on patients’ quality of life. The co-occurrence of migraine and depression is frequent, resulting in more complex symptoms and a poorer prognosis. The evidence suggests that depression and migraine comorbidity share a polygenic genetic background.ObjectiveThe aim of this study is to identify related genetic variants that contribute to genetic susceptibility to migraine with and without depression in a Chinese cohort.MethodsIn this case-control study, 263 individuals with migraines and 223 race-matched controls were included. Eight genetic polymorphism loci selected from the GWAS were genotyped using Sequenom’s MALDI-TOF iPLEX platform.ResultsIn univariate analysis, ANKDD1B rs904743 showed significant differences in genotype and allele distribution between migraineurs and controls. Furthermore, a machine learning approach was used to perform multivariate analysis. The results of the Random Forest algorithm indicated that ANKDD1B rs904743 was a significant risk factor for migraine susceptibility in China. Additionally, subgroup analysis by the Boruta algorithm showed a significant association between this SNP and migraine comorbid depression. Migraineurs with depression have been observed to have worse scores on the Beck Anxiety Inventory (BAI) and the Migraine Disability Assessment Scale (MIDAS).ConclusionThe study indicates that there is an association between ANKDD1B rs904743 and susceptibility to migraine with and without depression in Chinese patients

The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility

Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40–69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5–10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1–T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4–28.7) μg/m3; NOx, 41.3 (36.2–46.7) μg/m3; PM10, 15.9 (15.4–16.4) μg/m3; PM2.5, 9.9 (9.5–10.3) μg/m3; PM2.5 absorbance, 1.1 (1.0–1.2) per metre; and PM2.5–10, 6.1 (5.9–6.3) μg/m3. Compared with the low group, the high group's APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5–10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5–10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants