Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder

The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contrast the what-if case of the opposite sensitive attribute. Along with this separation with the causality, a frequent assumption in the deep latent causal model defines a single latent variable to absorb the entire exogenous uncertainty of the causal graph. However, we claim that such structure cannot distinguish the 1) information caused by the intervention (i.e., sensitive variable) and 2) information correlated with the intervention from the data. Therefore, this paper proposes Disentangled Causal Effect Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling the exogenous uncertainty into two latent variables: either 1) independent to interventions or 2) correlated to interventions without causality. Particularly, our disentangling approach preserves the latent variable correlated to interventions in generating counterfactual examples. We show that our method estimates the total effect and the counterfactual effect without a complete causal graph. By adding a fairness regularization, DCEVAE generates a counterfactual fair dataset while losing less original information. Also, DCEVAE generates natural counterfactual images by only flipping sensitive information. Additionally, we theoretically show the differences in the covariance structures of DCEVAE and prior works from the perspective of the latent disentanglement

Excessive Exercise Habits in Marathoners as Novel Indicators of Masked Hypertension

Background. Excessive exercise such as marathon running increases the risk of cardiovascular events that may be related to myocardial infarction and sudden death. We aimed to investigate that the exercise characteristics can be used as a novel indicator of masked hypertension. Methods. A total of 571 middle-aged recreational male marathoners were assigned to a high blood pressure group (HBPG; = 214) or a normal blood pressure group (NBPG; = 357). A graded exercise test was used to examine the hemodynamic response and cardiac events, and the personal exercise characteristics were recorded. Results. Systolic blood pressure and diastolic blood pressure were higher in the HBPG than in the NBPG ( < 0.05, all). The marathon history, exercise intensity, and time were longer and higher, whereas the marathon completion duration was shorter in the HBPG than in NBPG ( < 0.05, all). HBPG showed a higher frequency of alcohol consumption than NBPG ( < 0.05). Conclusion. More excessive exercise characteristics than the normative individuals. If the individuals exhibit high blood pressure during rest as well as exercise, the exercise characteristics could be used as a novel indicator for masked hypertension

Anti-allergic and anti-inflammatory effects of butanol extract from Arctium Lappa L

Background: Atopic dermatitis is a chronic, allergic inflammatory skin disease that is accompanied by markedly increased levels of inflammatory cells, including eosinophils, mast cells, and T cells. Arctium lappa L. is a traditional medicine in Asia. This study examined whether a butanol extract of A. lappa (ALBE) had previously unreported anti-allergic or anti-inflammatory effects.Methods: This study examined the effect of ALBE on the release of ??-hexosaminidase in antigen-stimulated-RBL-2H3 cells. We also evaluated the ConA-induced expression of IL-4, IL-5, mitogen-activated protein kinases (MAPKs), and nuclear factor (NF)-??B using RT-PCR, Western blotting, and ELISA in mouse splenocytes after ALBE treatment.Results: We observed significant inhibition of ??-hexosaminidase release in RBL-2H3 cells and suppressed mRNA expression and protein secretion of IL-4 and IL-5 induced by ConA-treated primary murine splenocytes after ALBE treatment. Additionally, ALBE (100 ??g/mL) suppressed not only the transcriptional activation of NF-??B, but also the phosphorylation of MAPKs in ConA-treated primary splenocytes.Conclusions: These results suggest that ALBE inhibits the expression of IL-4 and IL-5 by downregulating MAPKs and NF-??B activation in ConA-treated splenocytes and supports the hypothesis that ALBE may have beneficial effects in the treatment of allergic diseases, including atopic dermatitis. ?? 2011 Sohn et al; licensee BioMed Central Ltd

Bis(di-2-pyridylmethane­diol-κ3 N,O,N′)nickel(II) dinitrate

The title compound, [Ni(C11H10N2O2)2](NO3)2, consists of an NiII atom coordinated by two tridentate chelating di-2-pyridylmethane­diol [(2-py)2C(OH)2] ligands. The NiII atom is located on an inversion center. The geometry around the NiII atom is distorted octa­hedral. The gem-diol (2-py)2C(OH)2 ligand adopts the coordination mode η1:η1:η1. The Ni—N and Ni—O bond lengths are typical for high-spin NiII in an octa­hedral environment [Ni—N = 2.094 (2) and 2.124 (3) Å, and Ni—O = 2.108 (3) Å]. One of the hydr­oxy H atoms is split over two positions which both inter­act with the nitrate anion. The occurence of different O—H⋯O hydrogen bonds leads to the formation of a layer parallel to the (101) plane

Methods for detecting associations between phenotype and aggregations of rare variants

Although genome-wide association studies have uncovered variants associated with more than 150 traits, the percentage of phenotypic variation explained by these associations remains small. This has led to the search for the dark matter that explains this missing genetic component of heritability. One potential explanation for dark matter is rare variants, and several statistics have been devised to detect associations resulting from aggregations of rare variants in relatively short regions of interest, such as candidate genes. In this paper we investigate the feasibility of extending this approach in an agnostic way, in which we consider all variants within a much broader region of interest, such as an entire chromosome or even the entire exome. Our method searches for subsets of variant sites using either Markov chain Monte Carlo or genetic algorithms. The analysis was performed with knowledge of the Genetic Analysis Workshop 17 answers

Grading system for periodontitis by analyzing levels of periodontal pathogens in saliva

Periodontitis is an infectious disease that is associated with microorganisms that colonize the tooth surface. Clinically, periodontal condition stability reflects dynamic equilibrium between bacterial challenge and host response. Therefore, periodontal pathogen assessment can assist in the early detection of periodontitis. Here we developed a grading system called the periodontal pathogen index (PPI) by analyzing the copy numbers of multiple pathogens both in healthy and chronic periodontitis patients. We collected 170 mouthwash samples (64 periodontally healthy controls and 106 chronic periodontitis patients) and analyzed the salivary 16S rRNA levels of nine pathogens using multiplex, quantitative real-time polymerase chain reaction. Except for Aggregatibacter actinomycetemcomitans, copy numbers of all pathogens were significantly higher in chronic periodontitis patients. We classified the samples based on optimal cut-off values with maximum sensitivity and specificity from receiver operating characteristic curve analyses (AUC = 0.91, 95% CI: 0.87-0.96) into four categories of PPI: Healthy (1-40), Moderate (41-60), At Risk (61-80), and Severe (81-100). PPI scores were significantly higher in all chronic periodontitis patients than in the controls (odds ratio: 31.7, 95% CI: 13.41-61.61) and were associated with age, scaling as well as clinical characteristics including clinical attachment level and plaque index. Our PPI grading system can be clinically useful for the early assessment of pathogenic bacterial burden and follow-up monitoring after periodontitis treatment

Correlation between Histological Activity and Endoscopic, Clinical, and Serologic Activities in Patients with Ulcerative Colitis

Objectives. Recent studies suggest that histological healing is a treatment goal in ulcerative colitis (UC). We aimed to evaluate the correlation between histological activity and clinical, endoscopic, and serologic activities in patients with UC. Methods. We retrospectively reviewed medical records from patients with UC who underwent colonoscopy or sigmoidoscopy with biopsies. The Mayo endoscopic subscore was used to assess endoscopic activity. Biopsy specimens were reviewed by two blinded pathologists and scored using the Geboes scoring system. Results. We analyzed 154 biopsy specimens from 82 patients with UC. Histological scores exhibited strong correlation with endoscopic subscores (Spearman’s rank correlation coefficient r=0.774, p<0.001) and moderate correlation with C-reactive protein levels (r=0.422, p<0.001) and partial Mayo scores (r=0.403, p<0.001). Active histological inflammation (Geboes score ≥ 3.1) was observed in 6% (2 of 33) of the endoscopically normal mucosa samples, 66% (19 of 29) of mild disease samples, and 98% (90 of 92) of moderate-to-severe disease samples. Conclusions. Histological activity was closely correlated with the endoscopic, clinical, and serologic UC activities. However, several patients with mild or normal endoscopic findings exhibited histological evidence of inflammation. Therefore, histological assessment may be helpful in evaluating treatment outcomes and determining follow-up strategies

A Study Using a Monte Carlo Method of the Optimal Configuration of a Distribution Network in Terms of Power Loss Sensing

Recently there have been many studies of power systems with a focus on “New and Renewable Energy” as part of “New Growth Engine Industry” promoted by the Korean government. “New And Renewable Energy”—especially focused on wind energy, solar energy and fuel cells that will replace conventional fossil fuels—is a part of the Power-IT Sector which is the basis of the SmartGrid. A SmartGrid is a form of highly-efficient intelligent electricity network that allows interactivity (two-way communications) between suppliers and consumers by utilizing information technology in electricity production, transmission, distribution and consumption. The New and Renewable Energy Program has been driven with a goal to develop and spread through intensive studies, by public or private institutions, new and renewable energy which, unlike conventional systems, have been operated through connections with various kinds of distributed power generation systems. Considerable research on smart grids has been pursued in the United States and Europe. In the United States, a variety of research activities on the smart power grid have been conducted within EPRI’s IntelliGrid research program. The European Union (EU), which represents Europe’s Smart Grid policy, has focused on an expansion of distributed generation (decentralized generation) and power trade between countries with improved environmental protection. Thus, there is current emphasis on a need for studies that assesses the economic efficiency of such distributed generation systems. In this paper, based on the cost of distributed power generation capacity, calculations of the best profits obtainable were made by a Monte Carlo simulation. Monte Carlo simulations that rely on repeated random sampling to compute their results take into account the cost of electricity production, daily loads and the cost of sales and generate a result faster than mathematical computations. In addition, we have suggested the optimal design, which considers the distribution loss associated with power distribution systems focus on sensing aspect and distributed power generation

Serial Assessment of Myocardial Properties Using Cyclic Variation of Integrated Backscatter in an Adriamycin-Induced Cardiomyopathy Rat Model

Although adriamycin (Doxorubicin) is one of the most effective and useful antineoplastic agents for the treatment of a variety of malignancies, its repeated administration can induce irreversible myocardial damage and resultant heart failure. Currently, no marker to detect early cardiac damage is available. The purpose of this study was to investigate whether an assessment of the acoustic properties of the myocardium could enable the earlier detection of myocardial damage after adriamycin chemotherapy. Forty Wistar rats were treated with adriamycin (2 mg/kg, i.v.) once a week for 2, 4, 6 or 8 weeks consecutively. Left ventricular ejection fraction (LVEF) was calculated using M-mode echocardiography data. The magnitude of cardiac cycle dependent variation of integrated backscatter (CVIB) of the myocardium was measured in the mid segment of the septum and in the posterior wall of the left ventricle, using a real time two dimensional integrated backscatter imaging system. LVEF was significantly lower in the adriamycin-treated 8-week group than in the controls (75 ± 9 vs 57 ± 8%, p < 0.05). Myocyte damage was only seen in the 8-week adriamycin-treated group. However, no significant changes of CVIB were observed between baseline or during follow-up in the ADR or control group. In conclusion, serial assessment of the acoustic properties of the myocardium may not be an optimal tool for the early detection of myocardial damage after doxorubicin chemotherapy in a rat model

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression???17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment