Methodology for Validating Artifact Removal Techniques for Physiological Signals

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a ground truth signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this ground truth, together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform. © 2012 IEEE.

Sperm DNA damage has a negative association with live birth rates after IVF.

Sperm DNA damage has a negative impact on pregnancy rates following assisted reproduction treatment (ART). The aim of the present study was to examine the relationship between sperm DNA fragmentation and live-birth rates after IVF and intracytoplasmic sperm injection (ICSI). The alkaline Comet assay was employed to measure sperm DNA fragmentation in native semen and in spermatozoa following density-gradient centrifugation in semen samples from 203 couples undergoing IVF and 136 couples undergoing ICSI. Men were divided into groups according to sperm DNA damage. Following IVF, couples with <25% sperm DNA fragmentation had a live-birth rate of 33%; in contrast, couples with >50% sperm DNA fragmentation had a much lower live-birth rate of 13%. Following ICSI, no significant differences in sperm DNA damage were found between any groups of patients. Sperm DNA damage was also associated with low live-birth rates following IVF in both men and couples with idiopathic infertility: 39% of couples and 41% of men with idiopathic infertility have high sperm DNA damage. Sperm DNA damage assessed by the Comet assay has a close inverse relationship with live-birth rates after IVF.

Sperm DNA damage has a negative impact on assisted reproduction treatment outcome, in particular, on pregnancy rates. The aim of the present study was to examine the relationship between sperm DNA fragmentation and live-birth rates after IVF and intracytoplasmic sperm injection (ICSI). The alkaline Comet assay was employed to measure sperm DNA fragmentation in native semen and in spermatozoa following density-gradient centrifugation in semen samples from 203 couples undergoing IVF and 136 couples undergoing ICSI. Men were divided into groups according to sperm DNA damage and treatment outcome. Following IVF, couples with <25% sperm DNA fragmentation had a live birth rate of 33%. In contrast, couples with >50% sperm DNA fragmentation had a much lower live-birth rate of 13% following IVF. Following ICSI, there were no significant differences in levels of sperm DNA damage between any groups of patients. Sperm DNA damage was also associated with the very low live-birth rates following IVF in both men and couples with idiopathic infertility: 39% of couples and 41% of men have high level of sperm DNA damage. Sperm DNA damage assessed by the Comet assay has a close inverse relationship with live-birth rates after IVF.

Where are all the men? The marginalization of men in social scientific research on infertility

There is a wealth of research exploring the psychological consequences of infertility and assisted reproduction technology: a substantial body of sociological and anthropological work on ‘reproductive disruptions’ of many kinds and a small but growing literature on patient perspectives of the quality of care in assisted reproduction. In all these fields, research studies are far more likely to be focused on the understandings and experiences of women than those of men. This paper discusses reasons for the relative exclusion of men in what has been called the ‘psycho-social’ literature on infertility, comments on research on men from psychological and social perspectives and recent work on the quality of patient care, and makes suggestions for a reframing of the research agenda on men and assisted reproduction. Further research is needed in all areas, including: perceptions of infertility and infertility treatment seeking; experiences of treatment; information and support needs; decisions to end treatment; fatherhood post assisted conception; and the motivation and experiences of sperm donors and men who seek fatherhood through surrogacy or co-parenting. This paper argues for multimethod, interdisciplinary research that includes broader populations of men which can contribute to improved clinical practice and support for users of assisted reproduction treatment

The impact of sperm DNA damage in assisted conception and beyond: recent advances in diagnosis and treatment

Abstract Sperm DNA damage is a useful biomarker for male infertility diagnosis and prediction of assisted reproduction outcomes.
It is associated with reduced fertilization rates, embryo quality and pregnancy rates, and higher rates of spontaneous miscarriage
and childhood diseases. This review provides a synopsis of the most recent studies from each of the authors, all of whom have major
track records in the field of sperm DNA damage in the clinical setting. It explores current laboratory tests and the accumulating body
of knowledge concerning the relationship between sperm DNA damage and clinical outcomes. The paper proceeds to discuss the
strengths, weaknesses and clinical applicability of current sperm DNA tests. Next, the biological significance of DNA damage in
the male germ line is considered. Finally, as sperm DNA damage is often the result of oxidative stress in the male reproductive tract,
the potential contribution of antioxidant therapy in the clinical management of this condition is discussed. DNA damage in human spermatozoa is an important attribute of semen quality. It should be part of the clinical work up and properly controlled trials
addressing the effectiveness of antioxidant therapy should be undertaken as a matter of urgency.

Bacteria microarrays as sensitive tools for exploring pathogen surface epitopes and recognition by host receptors

We describe a protocol for the generation and validation of bacteria microarrays and their application to the study of specific features of the pathogen's surface and interactions with host receptors. Bacteria were directly printed on nitrocellulose-coated glass slides, using either manual or robotic arrayers, and printing quality, immobilization efficiency and stability of the arrays were rigorously controlled by incorporating a fluorescent dye into the bacteria. A panel of wild type and mutant strains of the human pathogen Klebsiella pneumoniae, responsible for nosocomial and community-acquired infections, was selected as model bacteria, and SYTO-13 was used as dye. Fluorescence signals of the printed bacteria were found to exhibit a linear concentration-dependence in the range of 1 x 10(8) to 1 x 10(9) bacteria per ml. Similar results were obtained with Pseudomonas aeruginosa and Acinetobacter baumannii, two other human pathogens. Successful validation of the quality and applicability of the established microarrays was accomplished by testing the capacity of the bacteria array to detect recognition by anti-Klebsiella antibodies and by the complement subcomponent C1q, which binds K. pneumoniae in an antibody-independent manner. The biotin/AlexaFluor-647-streptavidin system was used for monitoring binding, yielding strain-and dose-dependent signals, distinctive for each protein. Furthermore, the potential of the bacteria microarray for investigating specific features, e.g. glycosylation patterns, of the cell surface was confirmed by examining the binding behaviour of a panel of plant lectins with diverse carbohydrate-binding specificities. This and other possible applications of the newly developed arrays, as e.g. screening/evaluation of compounds to identify inhibitors of host-pathogen interactions, make bacteria microarrays a useful and sensitive tool for both basic and applied research in microbiology, biomedicine and biotechnology.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.

QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics

Background: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. Results: We describe QUADrATiC (http://go.qub.ac.uk/QUADrATiC), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts.Conclusions: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.

Control of uncertain compartmental systems

Os sistemas compartimentais são frequentemente usados na modelação de diversos processos em várias áreas, tais como a biomedicina, ecologia, farmacocinética, entre outras. Na maioria das aplicações práticas, nomeadamente, aquelas que dizem respeito à administração de drogas a pacientes sujeitos a cirurgia, por exemplo, a presença de incertezas nos parâmetros do sistema ou no estado do sistema é muito comum. Ao longo dos últimos anos, a análise de sistemas compartimentais tem sido bastante desenvolvida na literatura. No entanto, a análise da sensibilidade da estabilidade destes sistemas na presença de incertezas tem recebido muito menos atenção. Nesta tese, consideramos uma lei de controlo por realimentação do estado com restrições de positividade e analisamos a sua robustez quando aplicada a sistemas compartimentais lineares e invariantes no tempo com incertezas nos parâmetros. Além disso, para sistemas lineares e invariantes no tempo com estado inicial desconhecido, combinamos esta lei de controlo com um observador do estado e a robustez da lei de controlo resultante também é analisada. O controlo do bloqueio neuromuscular por meio da infusão contínua de um relaxante muscular pode ser modelado como um sistema compartimental de três compartimentos e tem sido objecto de estudo por diversos grupos de investigação. Nesta tese, os nossos resultados são aplicados a este problema de controlo e são fornecidas estratégias para melhorar os resultados obtidos.

Self-referencing thermometry in the nanoscale

Na ´ultima d´ecada emergiu uma linha de investiga¸c˜ao muito activa em term´ometros n˜ao invasivos e precisos que possam determinar temperatura `a escala nanom´etrica. Esta investiga¸c˜ao foi fortemente estimulada pelas numerosas solicita¸c˜oes da nanotecnologia e da biomedicina, por exemplo. Uma das abordagens mais promissoras prop˜oe o uso de i˜oes trivalentes de lantan´ıdeos que apresenta propriedades fotoluminescentes que dependem da temperatura. Neste trabalho demonstra-se que esta t´ecnica combina as vantagens de te um limite de detec¸c˜ao de 0.5 graus com sensibilidade at´e 4.5 % · K−1. Este term´ometro molecular pode ser processado em filmes finos ou nanopart´ıculas, abrindo os campos de aplica¸c˜ao a diferentes utiliza¸c˜oes. As nanopart´ıculas de s´ılica produzidas s˜ao caracterizadas na presen¸ca e na ausˆencia de i˜oes lantan´ıdeos. Sem o metal, as nanopart´ıculas de APTES/TEOS demonstram ser luminescentes sob excita¸c˜ao UV sem necessidade de utilizar qualquer tratamento t´ermico. O rendimento quˆantico de emiss˜ao depende apenas da propor¸c˜ao dos silanos e pode atingir o valor de 0.15 ± 0.02. A co-dopagem destas nanopart´ıculas com Eu3+ e Tb3+ permite obter sondas com resposta raciom´etrica, com a possibilidade de ajustar a gama de temperaturas de opera¸c˜ao e a sensibilidade, via desenho inteligente da matriz de suporte e dos ligandos de β-dicetona que est˜ao coordenados ao i˜ao met´alico. Quando processados como filmes, este term´ometro permite o mapeamento de temperaturas com resolu¸c˜ao espacial 1.8 μm. A racionaliza¸c˜ao da dependˆencia de temperatura ´e uma ferramenta ´util para desenvolver term´ometros que operam em gamas de temperatura espec´ıficos (e.g. gama de temperatura fisiol´ogica, 290-340 K) com sensibilidade acima de 0.5 % · K−1. A combina¸c˜ao de esfor¸cos de um grande n´umero de diversas disciplinas ir´a previsivelmente permitir o surgimento de term´ometros moleculares novos e sofisticados, preenchendo os principais requisitos das nanociencias.