Towards Poisoning Fair Representations

Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female. Recently, fair representation learning (FRL) trained by deep neural networks has demonstrated superior performance, whereby representations containing no demographic information are inferred from the data and then used as the input to classification or other downstream tasks. Despite the development of FRL methods, their vulnerability under data poisoning attack, a popular protocol to benchmark model robustness under adversarial scenarios, is under-explored. Data poisoning attacks have been developed for classical fair machine learning methods which incorporate fairness constraints into shallow-model classifiers. Nonetheless, these attacks fall short in FRL due to notably different fairness goals and model architectures. This work proposes the first data poisoning framework attacking FRL. We induce the model to output unfair representations that contain as much demographic information as possible by injecting carefully crafted poisoning samples into the training data. This attack entails a prohibitive bilevel optimization, wherefore an effective approximated solution is proposed. A theoretical analysis on the needed number of poisoning samples is derived and sheds light on defending against the attack. Experiments on benchmark fairness datasets and state-of-the-art fair representation learning models demonstrate the superiority of our attack

The roles of serum vitamin D and tobacco smoke exposure in insomnia: a cross-sectional study of adults in the United States

AimTobacco smoke exposure and vitamin D (VD) status were both associated with insomnia. However, the combined effect of smoking and VD on insomnia has not been discussed. This study aimed to explore the role of VD in the association between tobacco smoke exposure and insomnia.MethodsData on adults were extracted from the National Health and Nutrition Examination Surveys (NHANES) database in 2005–2008 for this cross-sectional study. Weighted univariate and multivariate logistic regression analyses were used to explore the associations between serum cotinine, serum VD, and insomnia. A surface diagram was drawn to reflect the effect of VD on the association between serum cotinine and insomnia. In addition, the potential regulating effect of VD in subgroups of smoking status was also performed. The evaluation index was odds ratios (ORs) with 95% confidence intervals (CIs).ResultsAmong the eligible participants, 1,766 had insomnia. After adjusting for covariates, we found that elevated serum cotinine levels were associated with higher odds of insomnia [OR = 1.55, 95% CI: (1.22, 1.97)]. However, the relationship between serum VD level and insomnia was not significant (P = 0.553). Higher serum cotinine levels were also associated with higher odds of insomnia [OR = 1.52, 95% CI: (1.17, 1.98)] when serum VD level was <75 nmol/L; however, this relationship became non-significant when serum VD concentration was elevated (P = 0.088). Additionally, the potential regulating effect of VD was also found in adults who were not smoking.ConclusionVD may play a potential regulative role in the association between tobacco smoke exposure and insomnia. Further studies are needed to clarify the causal relationships between VD, tobacco smoke exposure, and insomnia

Experimental verification and identifying biomarkers related to insomnia

IntroductionInsomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to apply the SD data to insomnia. Although many studies have explained the possible mechanisms associated with insomnia, no previous studies have considered the key genes associated with insomnia or the relationship between insomnia and immune cells. In this study, we analyzed the relationship between key genes and immune cells by identifying biomarkers for the diagnosis of insomnia. Next, we verified the efficacy of these biomarkers experimentally.MethodsFirst, we downloaded four microarrays (GSE11755, GSE12624, GSE28750, and GSE48080) from the Gene Expression Omnibus (GEO) database, which included data from 239 normal human blood samples and 365 blood specimens from patients with SD. Then, we analyzed two groups of differentially expressed genes (DEGs) and used Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to investigate these key genes. Next, we used CIBERSORT to investigate the composition of 22 immune cell components of key genes in SD patients. Finally, the expression levels of key biomarkers in sleep-deprived patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Salmonella infection, NOD-like receptor (NLR) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Th17 cell differentiation were significant. Based on machine learning, we identified C2CD2L, SPINT2, APOL3, PKNOX1, and A2M as key genes for SD; these were confirmed by receiver operating characteristic (ROC) analysis. Immune cell infiltration analysis showed that C2CD2L, SPINT2, APOL3, PKNOX1, and A2M were related in different degrees to regulatory T cells (Tregs), follicular T helper cells, CD8 cells, and other immune cells. The qRT-PCR experiments confirmed that the expression levels of C2CD2L concurred with the results derived from machine learning, but PKNOX1 and APOL3 did not.DiscussionIn summary, we identified a key gene (C2CD2L) that may facilitate the development of biomarkers for insomnia

The influence of summer hypoxia on sedimentary phosphorus biogeochemistry in a coastal scallop farming area, North Yellow Sea

In situ field investigations coupled with laboratory incubations were employed to explore the surface sedimentary phosphorus (P) cycle in a mariculture area adjacent to the Yangma Island suffering from summer hypoxia in the North Yellow Sea. Five forms of P were fractionated, namely exchangeable P (Ex-P), iron-bound P (Fe-P), authigenic apatite (Ca-P), detrital P (De-P) and organic P (OP). Total P (TP) varied from 13.42 to 23.88 mu mol g(-1) with the main form of inorganic P (IP). The benthic phosphate (DIP) fluxes were calculated based on incubation experiments. The results show that the sediment was an important source of P in summer with similar to 39% of the bioavailable P (Bio-P) recycled back into the water column. However, the sediment acted a sink of P in autumn. The benthic DIP fluxes were mainly controlled by the remobilizing of Fe-P, Ex-P and OP under contrasting redox conditions. In August (hypoxia season), similar to 0.92 mu mol g(-1) of Fe-P and similar to 0.52 mu mol g(-1) of OP could be transformed to DIP and released into water, while similar to 0.36 mu mol g(-1) of DIP was adsorbed to clay minerals. In November (non-hypoxia season), however, similar to 0.54 mu mol g(-1) of OP was converted into DIP, while similar to 0.55 mu mol g(-1) and similar to 0.28 mu mol g(-1) of DIP was adsorbed to clay minerals and bind to iron oxides. Furthermore, scallop farming activities also affected the P mobilization through biological deposition and reduced hydrodynamic conditions. The burial fluxes of P varied from 11.67 to 20.78 mu mol cm(-2) yr(-1) and its burial efficiency was 84.7-100%, which was consistent with that in most of the marginal seas worldwide. This study reveals that hypoxia and scallop farming activities can significantly promote sedimentary P mobility, thereby causing high benthic DIP flux in coastal waters. (C) 2020 Elsevier B.V. All rights reserved

The combination of body mass index and fasting plasma glucose is associated with type 2 diabetes mellitus in Japan: a secondary retrospective analysis

BackgroundBody mass index (BMI) and fasting plasma glucose (FPG) are known risk factors for type 2 diabetes mellitus (T2DM), but data on the prospective association of the combination of BMI and FPG with T2DM are limited. This study sought to characterize the association of the combination of BMI and FPG (ByG) with T2DM.MethodsThe current study used the NAGALA database. We categorized participants by tertiles of ByG. The association of ByG with T2DM was expressed with hazard ratios (HRs) with 95% confidence intervals (CIs) after adjustment for potential risk factors.ResultsDuring a median follow-up of 6.19 years in the normoglycemia cohort and 5.58 years in the prediabetes cohort, the incidence of T2DM was 0.75% and 7.79%, respectively. Following multivariable adjustments, there were stepwise increases in T2DM with increasing tertiles of ByG. After a similar multivariable adjustment, the risk of T2DM was 2.57 (95% CI 2.26 - 2.92), 1.97 (95% CI 1.53 - 2.54) and 1.50 (95% CI 1.30 - 1.74) for a per-SD change in ByG in all populations, the normoglycemia cohort and the prediabetes cohort, respectively.ConclusionByG was associated with an increased risk of T2DM in Japan. The result reinforced the importance of the combination of BMI and FPG in assessing T2DM risk

Decanoic acid-palmitic acid/SiO2@TiO2 phase change microcapsules based on RBF model with excellent photocatalysis performance and humidity control property

The decanoic acid-palmitic acid composite phase change material compounds with SiO2 and TiO2 to prepare decanoic acid-palmitic acid/SiO2@TiO2 phase change microcapsules (D-P-SiO2@TiO2 PCM). The D-P-SiO2@TiO2 PCM could show efficient temperature regulation, remove pollutants through photocatalysis, and control air humidity. However, it is difficult to obtain the best experimental scheme directly using the traditional experimental setup due to the complicated photocatalytic-humidity performance. The radial basis function (RBF) model optimized the uniform experimental design parameters, and the D-P-SiO2@TiO2 PCM showed enhanced photocatalytic-humidity performance. The RBF-calculated preparation parameters were as follows: the molar ratio of decanoic acid-palmitic acid to tetraethyl silicate was 0.42, pH was 1.83, the molar ratio of deionized water to tetraethyl silicate was 98.15, while the molar rate of tetrabutyl titanate to tetraethyl silicate was 0.76. The degradation rate of gaseous formaldehyde by the RBF-optimized D-P-SiO2@TiO2 PCM was 69.57% after 6 h, and the moisture content was between 0.0923 and 0.0940 g·g−1 at 43.16–75.29% relative humidity (RH). The comparison between model optimization and the experiment sample prepared using the optimized parameters showed that the theoretical photocatalytic-humidity performance target value was 2.0502, and the tested target value was 2.0757. The error calculated from these two values was only 1.24%, and both were better than the best value of uniform experimental calculation. RBF mathematical model was proved to be an effective, convenient, and economic-saving method to simulate and predict D-P-SiO2@TiO2 PCM experimental design parameters. SEM and TEM analyses of the RBF-optimized D-P-SiO2@TiO2 PCM showed a uniform spherical structure, and the particle size analysis analyses was about 200 nm. The DSC analysis showed the phase transition temperature range was between 16.97 and 28.94 °C, within the comfort range of the human body. The UV–Vis investigations showed the absorption edge of the RBF-optimized D-P-SiO2@TiO2 PCM was 380 nm, in line with the band gap structure of the TiO2 anatase phase. The thermogravimetric investigations showed that this composite was stable at normal temperature and pressure. After a 100 times hot–cold cycle, the quality of the RBF-optimized D-P-SiO2@TiO2 PCM maintained its stability, as the photocatalytic-humidity performance was almost the same. The N2-adsorption analysis showed it had a high specific surface area and irregular pore structures, which could help it regulate air humidity. Considering these results, the D-P-SiO2@TiO2 PCM, a new ecological functional material, would be used in the construction industry to improve the architectural ecological environment.</p

Causal relationship between the gut microbiota and insomnia: a two-sample Mendelian randomization study

BackgroundChanges in the gut microbiota are closely related to insomnia, but the causal relationship between them is not yet clear.ObjectiveTo clarify the relationship between the gut microbiota and insomnia and provide genetic evidence for them, we conducted a two-sample Mendelian randomization study.MethodsWe used a Mendelian randomized two-way validation method to discuss the causal relationship. First, we downloaded the data of 462,341 participants relating to insomnia, and the data of 18,340 participants relating to the gut microbiota from a genome-wide association study (GWAS). Then, we used two regression models, inverse-variance weighted (IVW) and MR-Egger regression, to evaluate the relationship between exposure factors and outcomes. Finally, we took a reverse MR analysis to assess the possibility of reverse causality.ResultsThe combined results show 19 gut microbiotas to have a causal relationship with insomnia (odds ratio (OR): 1.03; 95% confidence interval (CI): 1.01, 1.05; p=0.000 for class. Negativicutes; OR: 1.03; 95% CI: 1.01, 1.05; p=0.000 for order.Selenomonadales; OR: 1.01; 95% CI: 1.00, 1.02; p=0.003 for genus.RikenellaceaeRC9gutgroup). The results were consistent with sensitivity analyses for these bacterial traits. In reverse MR analysis, we found no statistical difference between insomnia and these gut microbiotas.ConclusionThis study can provide a new direction for the causal relationship between the gut microbiota (class.Negativicutes, order.Selenomonadales, genus.Lactococcus) and insomnia and the treatment or prevention strategies of insomnia

The Relationship between Physical Activity and College Students&rsquo; Mobile Phone Addiction: The Chain-Based Mediating Role of Psychological Capital and Social Adaptation

The purpose of this study was to investigate the effects and mechanisms of physical activity on mobile phone addiction among college students. A total of 9406 students, ranging from freshmen to seniors, from 35 colleges in four regions of Jiangsu Province were selected using the whole group sampling method. Questionnaires, particularly the International Physical Activity Questionnaire&mdash;Long Form (IPAQ), the positive psychological capital scale (PPQ), the social adjustment diagnostic questionnaire (SAFS), and the mobile phone addiction index scale (MPAI), were administered. We found that physical activity negatively predicted mobile phone addiction among university students. Social adaptation partially mediates between physical activity and mobile phone addiction among university students, with separate mediation of psychological capital playing no indirect role. Psychological capital and social adjustment mediate the chain between physical activity and mobile phone dependence among college students. Our findings suggest that physical activity is an important external factor influencing college students&rsquo; mobile phone dependence, and it indirectly affects university students&rsquo; mobile phone addiction through psychological capital and social adaptation. Improving the physical activity level of college students, enhancing their psychological capital, and promoting improved social adaptation are important ways to prevent mobile phone addiction among college students

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification

Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks

Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results