Securely measuring the overlap between private datasets with cryptosets

Many scientific questions are best approached by sharing data--collected by different groups or across large collaborative networks--into a combined analysis. Unfortunately, some of the most interesting and powerful datasets--like health records, genetic data, and drug discovery data--cannot be freely shared because they contain sensitive information. In many situations, knowing if private datasets overlap determines if it is worthwhile to navigate the institutional, ethical, and legal barriers that govern access to sensitive, private data. We report the first method of publicly measuring the overlap between private datasets that is secure under a malicious model without relying on private protocols or message passing. This method uses a publicly shareable summary of a dataset's contents, its cryptoset, to estimate its overlap with other datasets. Cryptosets approach "information-theoretic" security, the strongest type of security possible in cryptography, which is not even crackable with infinite computing power. We empirically and theoretically assess both the accuracy of these estimates and the security of the approach, demonstrating that cryptosets are informative, with a stable accuracy, and secure

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

BACKGROUND: A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows. RESULTS: Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database. CONCLUSIONS: We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/ ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-015-0110-6) contains supplementary material, which is available to authorized users

Antecedents and performance consequences of international outsourcing

The outsourcing of intermediate products to international suppliers is believed to improve firm performance. We investigate this claim and test key dimensions of the decision to outsource internationally using survey data on 200 manufacturing firms located in the Netherlands. We find that most international outsourcing is intra-regional in nature. Furthermore international outsourcing is a consequence of a firm‟s ability to search and evaluate foreign suppliers, which is co-determined by its size, multinationality, and frequency of cross-border communications. Finally, no performance effects were observed for international or global outsourcing. We conclude international outsourcing is a balancing act between lower production costs abroad and lower transaction costs locally

Development and Implementation of a Youth Prayer Fellowship in Madras

Problem Many youth in the Adventist Church in Madras do not participate regularly in church activities. Additionally they suffer from the effects of poverty, unemployment, and family tensions. Task The task of this dissertation was to develop a youth prayer fellowship that would address the problems of youth in the Madras Adventist Church. Method The biblical concept of prayer groups was studied to establish a basis for the prayer fellowship. In addition, the organization and effects of five Indian prayer fellowships were studied: Friends Missionary Prayer Band, Indian Evangelical Mission Prayer Fellowship, Christian Missionary Society Prayer Band, New Assembly Prayer Fellowship and Inter-college Student Prayer Fellowship. On the basis of the theory and examples a prayer fellowship plan was made. After fasting and prayer, the program was launched in the Madras SDA Church. Eight volunteers responded, were trained, and formed fellowship groups. Results Less than one year after the groups were launched, there are now sixteen fellowship groups attended by some 150 people. A majority of those who have attended the meetings have improved their church attendance, have become more active in church-related activities, and express their satisfaction with the program

Development and Implementation of a Seminar to Combat the Effects of Pornography on Marriage in the Madison Community SDA Church and Strategies for Prevention

Problem. Recent statistics in the nation, research among Christian couples, and my own counseling experience with couples, “particularly men” who were addicted to pornography, revealed that pornography is one of the leading causes of divorce and family breakdown today. Thus, it needed to be addressed at the Madison Community Church in Madison, Wisconsin. The purpose of this research project was to develop, implement and evaluate a seminar on The Effects of Pornography on Marriage and Strategies for Prevention. The intent of the seminar was to help couples, and “particularly men,” to understand the harmful effects of pornography on marriage, and provide biblical strategies, boundary plans, and practical steps to help protect oneself from the influences of pornography or avoid the use of it. Method. The seminar, The Effects of Pornography on Marriage and Strategies for Prevention was designed over one year and implemented in one day consisting of three sessions to teach participants the addictive nature of pornography, how it distorts God’s gift of sex, its effects on marriage relationships, the best approach to avoid the temptations of pornography, and strategies for prevention. As part of the seminar, a survey was given both before and after the seminar to assess the participants’ knowledge of and attitude towards pornography and to evaluate changes that resulted from the seminar. Results. The survey results revealed that there were significant changes in most items on the survey. The individual comments received on the surveys showed that the seminar helped the participants know sexuality is a gift from God, discover that pornography is an enemy of intimacy and distorts God’s intentions for sex, learn pornography’s many effects and how it wrecks marriages, develop healthy intimacy, recognize the need for establishing boundaries, and develop accountability methods to guard themselves from the influences and temptations of pornography. Almost all the respondents believed that pornography has devastating effects on marriage relationships and that it must be avoided at all costs. Conclusions. Most of the seminar objectives were met. Among the factors that contributed to the success of the seminar are the support of the church board, the response of married couples to the two-question survey, my own journey for study and further understanding of the nature of pornography from the current psychological or social science literature, and an examination of the Scriptures that helped develop the theological foundation of pornography, lust and sexual intimacy. Yet, it was the implementation of the seminar that aided couples not only to understand the powerful allure of pornography lust and its devastating effects on marriage, but also provided valuable information, resources, and accountability methods to guard themselves from the influences of pornography

Understanding and mitigating the impact of race with adversarial autoencoders

BACKGROUND: Artificial intelligence carries the risk of exacerbating some of our most challenging societal problems, but it also has the potential of mitigating and addressing these problems. The confounding effects of race on machine learning is an ongoing subject of research. This study aims to mitigate the impact of race on data-derived models, using an adversarial variational autoencoder (AVAE). In this study, race is a self-reported feature. Race is often excluded as an input variable, however, due to the high correlation between race and several other variables, race is still implicitly encoded in the data. METHODS: We propose building a model that (1) learns a low dimensionality latent spaces, (2) employs an adversarial training procedure that ensure its latent space does not encode race, and (3) contains necessary information for reconstructing the data. We train the autoencoder to ensure the latent space does not indirectly encode race. RESULTS: In this study, AVAE successfully removes information about race from the latent space (AUC ROC = 0.5). In contrast, latent spaces constructed using other approaches still allow the reconstruction of race with high fidelity. The AVAE\u27s latent space does not encode race but conveys important information required to reconstruct the dataset. Furthermore, the AVAE\u27s latent space does not predict variables related to race (R CONCLUSIONS: Though we constructed a race-independent latent space, any variable could be similarly controlled. We expect AVAEs are one of many approaches that will be required to effectively manage and understand bias in ML