A Latent Dirichlet Framework for Relevance Modeling

Abstract. Relevance-based language models operate by estimating the probabilities of observing words in documents relevant (or pseudo relevant) to a topic. However, these models assume that if a document is relevant to a topic, then all tokens in the document are relevant to that topic. This could limit model robustness and effectiveness. In this study, we propose a Latent Dirichlet relevance model, which relaxes this assumption. Our approach derives from current research on Latent Dirichlet Allocation (LDA) topic models. LDA has been extensively explored, especially for generating a set of topics from a corpus. A key attraction is that in LDA a document may be about several topics. LDA itself, however, has a limitation that is also addressed in our work. Topics generated by LDA from a corpus are synthetic, i.e., they do not necessarily correspond to topics identified by humans for the same corpus. In contrast, our model explicitly considers the relevance relationships between documents and given topics (queries). Thus unlike standard LDA, our model is directly applicable to goals such as relevance feedback for query modification and text classification, where topics (classes and queries) are provided upfront. Thus although the focus of our paper is on improving relevance-based language models, in effect our approach bridges relevance-based language models and LDA addressing limitations of both. Finally, we propose an idea that takes advantage of “bagof-words” assumption to reduce the complexity of Gibbs sampling based learning algorithm

An information-theoretic framework for semantic-multimedia retrieval

This article is set in the context of searching text and image repositories by keyword. We develop a unified probabilistic framework for text, image, and combined text and image retrieval that is based on the detection of keywords (concepts) using automated image annotation technology. Our framework is deeply rooted in information theory and lends itself to use with other media types. We estimate a statistical model in a multimodal feature space for each possible query keyword. The key element of our framework is to identify feature space transformations that make them comparable in complexity and density. We select the optimal multimodal feature space with a minimum description length criterion from a set of candidate feature spaces that are computed with the average-mutual-information criterion for the text part and hierarchical expectation maximization for the visual part of the data. We evaluate our approach in three retrieval experiments (only text retrieval, only image retrieval, and text combined with image retrieval), verify the framework’s low computational complexity, and compare with existing state-of-the-art ad-hoc models

Study of the prevalence of hypersensitivity β-lactam antibiotics among the population of Ukraine

Алергічні реакції до ß-лактамних антибіотиків є найбільш частою причиною побічних медикаментозних реакцій, опосередкованих специфічними імунологічними механізмами. Метою даної роботи було визначення поширеності гіперчутливості до ß-лактамних антибіотиків серед населення шляхом дослідження анамнестичних даних і проведення алергологічного обстеження для підвищення безпеки антибіотикотерапії і поліпшення фармакоекономічного профілю лікування. Аллергические реакции к ß-лактамным антибиотикам является наиболее частой причиной побочных медикаментозных реакций, опосредованных специфическими иммунологическими механизмами. Целью данной работы было определение распространенности гиперчувствительности к ß-лактамным антибиотикам среди населения путем исследования анамнестических данных и проведение аллергологического обследования для повышения безопасности антибиотикотерапии и улучшения фармакоэкономического профиля лечения. Allergic reactions to β-lactam antibiotics are the most common cause of adverse drug reactions mediated by specific immunological mechanisms. The purpose of this work was to determine the prevalence of hypersensitivity to ß-lactam antibiotics among the population by studying anamnestic data and conducting an allergic examination to improve the safety of antibiotic therapy and improve the pharmacoeconomic profile of treatment

Fly's time

The struggle between public and private efforts to sequence the fly genome is the subject of Michael Ashburner's new book, Won for All: How the Drosophila Genome Was Sequence

No major flaws in "Identification of individuals by trait prediction using whole-genome sequencing data"

In a recently published PNAS article, we studied the identifiability of genomic samples using machine learning methods [Lippert et al., 2017]. In a response, Erlich [2017] argued that our work contained major flaws. The main technical critique of Erlich [2017] builds on a simulation experiment that shows that our proposed algorithm, which uses only a genomic sample for identification, performed no better than a strategy that uses demographic variables. Below, we show why this comparison is misleading and provide a detailed discussion of the key critical points in our analyses that have been brought up in Erlich [2017] and in the media. Further, not only faces may be derived from DNA, but a wide range of phenotypes and demographic variables. In this light, the main contribution of Lippert et al. [2017] is an algorithm that identifies genomes of individuals by combining multiple DNA-based predictive models for a myriad of traits

Results of a 3 month treatment with metformin in patients with type 2 diabetes mellitus associated with ischaemic heart disease

MSEI of Ukraine «Ukrainian Medical Stomatological Academy«, Poltava the aim of the research was to investigate the effectiveness of mid-term inclusion of Metformin in the complex therapy in patients with diabetes type 2 in combination with IHD for the rational justification of timing, doses of the drug. Materials and methods. 52 man suffering from diabetes type 2 on background of ischaemic heart disease have been observed. An effect of included Metformin at mid-term rate (3 months) was estimated. The blood rates are observed (total cholesterol (TC), HDL-cholesterol, total lipids (TL), triglycerides (TG), β-lipoproteins, glycated hemoglobin, C-peptide) and anti inflammatory markers (IL-1β, IL-6, IL-8 and TNF-α). Results. In patients with diabetes type 2 on background of ischaemic heart disease during 3 months under the action of metformin significant decrease in body weight, waist circumference, BMI, concentration of total cholesterol, C-peptide and insulin resistance index and cytokines level IL-1β, IL-6 and TNF-α were observed. Conclusion. The obtained results indicate the purpose of Metformin prescription in patients with diabetes type 2 and IHD in the continuation of 3 months; it is an effective and safe method of treatment of such patients

Pharmacogenetic features of the effect of metformin in patients with coronary heart disease in the presence of metabolic syndrome and type 2 diahetes mellitus in terms of PPAR-γ 2 gene polymorphism

Цель исследования. Определить фармакогенетические особенности действия метформина у пациентов, страдающих ишемической болезнью сердца (ИБС) на фоне метаболического синдрома (МС) или сахарного диабета (СД) 2-го типа, с учетом полиморфизма Pro 1 2Ala гена PPAR-γ 2. To define the pharmacogenetics features of the effect of metformin in coronary heart disease (CHD) patients with metabolic syndrome (MS) or type 2 diabetes mellitus (T2DM), by taking into consideration PPAR-γ 2 Pro 1 Ala polymorphism

The action of metformin on the production of pro-inflammatory cytokines and insulin resistance (NF-kB signal pathway

Цель данного исследования – оценка роли NF-kB-сигнального пути в реализации противовоспалительного и инсулиносенситайзерного действия метформина у больных ишемической болезнью сердца (ИБС) на фоне метаболического синдрома (МС). Включение ежемесячного курса метформина в комплексную терапию ИБС снижало продукцию провоспалительных цитокинов ИЛ-1β, ИЛ-6, ИЛ-8 и ФНО-α и уменьшало концентрацию С-пептида в сыворотке, действуя на NF-kB-сигнальный путь. Включение метформина в комплексную терапию ИБС при МС препятствует активации NF-kB под действием эндогенных провоспалительных цитокинов. The objective of the present study was to estimate the role of the NF-kB signal pathway in the realization of the anti-inflammatory and insulin-synthesizing activities of metformin in the patients presenting with coronary heart disease (CHD) associated with metabolic syndrome (MS). It was shown that the introduction of a one-month course of metformin therapy in the combined treatment of CHD resulted in the decreased production of pro-inflammatory cytokines, such as IL-1-beta, IL-6, IL-8, and TNF-alpha. Simultaneously, the serum C-peptide level decreased. These effects of metformin were mediated through the NF-kB-signal pathway. It is concluded that the inclusion of metformin in the combined treatment of coronary heart disease in the patients with metabolic syndrome prevent activation of NF-kB under the influence of endogeneous pro-inflammatory cytokin