Bioinformatical approaches to ranking of anti-HIV combination therapies and planning of treatment schedules

The human immunodeficiency virus (HIV) pandemic is one of the most serious health challenges humanity is facing today. Combination therapy comprising multiple antiretroviral drugs resulted in a dramatic decline in HIV-related mortality in the developed countries. However, the emergence of drug resistant HIV variants during treatment remains a problem for permanent treatment success and seriously hampers the composition of new active regimens. In this thesis we use statistical learning for developing novel methods that rank combination therapies according to their chance of achieving treatment success. These depend on information regarding the treatment composition, the viral genotype, features of viral evolution, and the patient's therapy history. Moreover, we investigate different definitions of response to antiretroviral therapy and their impact on prediction performance of our method. We address the problem of extending purely data-driven approaches to support novel drugs with little available data. In addition, we explore the prospect of prediction systems that are centered on the patient's treatment history instead of the viral genotype. We present a framework for rapidly simulating resistance development during combination therapy that will eventually allow application of combination therapies in the best order. Finally, we analyze surface proteins of HIV regarding their susceptibility to neutralizing antibodies with the aim of supporting HIV vaccine development.Die Humane Immundefizienz-Virus (HIV) Pandemie ist eine der schwerwiegendsten gesundheitlichen Herausforderungen weltweit. Kombinationstherapien bestehend aus mehreren Medikamenten führten in entwickelten Ländern zu einem drastischen Rückgang der HIV-bedingten Sterblichkeit. Die Entstehung von Arzneimittel-resistenten Varianten während der Behandlung stellt allerdings ein Problem für den anhaltenden Behandlungserfolg dar und erschwert die Zusammenstellung von neuen aktiven Kombinationen. In dieser Arbeit verwenden wir statistisches Lernen zur Entwicklung neuer Methoden, welche Kombinationstherapien bezüglich ihres erwarteten Behandlungserfolgs sortieren. Dabei nutzen wir Informationen über die Medikamente, das virale Erbgut, die Virus Evolution und die Therapiegeschichte des Patienten. Außerdem untersuchen wir unterschiedliche Definitionen für Therapieerfolg und ihre Auswirkungen auf die Güte unserer Modelle. Wir gehen das Problem der Erweiterung von daten-getriebenen Modellen bezüglich neuer Wirkstoffen an, und untersuchen weiterhin die Therapiegeschichte des Patienten als Ersatz für das virale Genom bei der Vorhersage. Wir stellen das Rahmenwerk für die schnelle Simulation von Resistenzentwicklung vor, welches schließlich erlaubt, die bestmögliche Reihenfolge von Kombinationstherapien zu suchen. Schließlich analysieren wir das HIV Oberflächenprotein im Hinblick auf seine Anfälligkeit für neutralisierende Antikörper mit dem Ziel die Impfstoff Entwicklung zu unterstützen

Bioinformatical approaches to ranking of anti-HIV combination therapies and planning of treatment schedules

The human immunodeficiency virus (HIV) pandemic is one of the most serious health challenges humanity is facing today. Combination therapy comprising multiple antiretroviral drugs resulted in a dramatic decline in HIV-related mortality in the developed countries. However, the emergence of drug resistant HIV variants during treatment remains a problem for permanent treatment success and seriously hampers the composition of new active regimens. In this thesis we use statistical learning for developing novel methods that rank combination therapies according to their chance of achieving treatment success. These depend on information regarding the treatment composition, the viral genotype, features of viral evolution, and the patient's therapy history. Moreover, we investigate different definitions of response to antiretroviral therapy and their impact on prediction performance of our method. We address the problem of extending purely data-driven approaches to support novel drugs with little available data. In addition, we explore the prospect of prediction systems that are centered on the patient's treatment history instead of the viral genotype. We present a framework for rapidly simulating resistance development during combination therapy that will eventually allow application of combination therapies in the best order. Finally, we analyze surface proteins of HIV regarding their susceptibility to neutralizing antibodies with the aim of supporting HIV vaccine development.Die Humane Immundefizienz-Virus (HIV) Pandemie ist eine der schwerwiegendsten gesundheitlichen Herausforderungen weltweit. Kombinationstherapien bestehend aus mehreren Medikamenten führten in entwickelten Ländern zu einem drastischen Rückgang der HIV-bedingten Sterblichkeit. Die Entstehung von Arzneimittel-resistenten Varianten während der Behandlung stellt allerdings ein Problem für den anhaltenden Behandlungserfolg dar und erschwert die Zusammenstellung von neuen aktiven Kombinationen. In dieser Arbeit verwenden wir statistisches Lernen zur Entwicklung neuer Methoden, welche Kombinationstherapien bezüglich ihres erwarteten Behandlungserfolgs sortieren. Dabei nutzen wir Informationen über die Medikamente, das virale Erbgut, die Virus Evolution und die Therapiegeschichte des Patienten. Außerdem untersuchen wir unterschiedliche Definitionen für Therapieerfolg und ihre Auswirkungen auf die Güte unserer Modelle. Wir gehen das Problem der Erweiterung von daten-getriebenen Modellen bezüglich neuer Wirkstoffen an, und untersuchen weiterhin die Therapiegeschichte des Patienten als Ersatz für das virale Genom bei der Vorhersage. Wir stellen das Rahmenwerk für die schnelle Simulation von Resistenzentwicklung vor, welches schließlich erlaubt, die bestmögliche Reihenfolge von Kombinationstherapien zu suchen. Schließlich analysieren wir das HIV Oberflächenprotein im Hinblick auf seine Anfälligkeit für neutralisierende Antikörper mit dem Ziel die Impfstoff Entwicklung zu unterstützen

DIREITO AMBIENTAL E NANOTECNOLOGIAS: DESAFIOS AOS NOVOS RISCOS DA INOVAÇÃO

Propõe-se avaliar a complexidade que as novas tecnologias representam, sobretudo na utilização das nanotecnologias em inúmeros produtos que, a priori, foram desenvolvidos para proporcionar melhorias e não para causar danos futuros. Por meio de uma pesquisa descritiva e bibliográfica, objetiva-se conhecer os instrumentos tradicionais do Direito Ambiental e avaliar as possibilidades de novos caminhos aptos a conciliar a necessária evolução tecnológica com a prudência em relação às possíveis resultantes imprevisíveis da utilização de elementos desenvolvidos na escala nano. A tutela pelo sistema do Direito Ambiental se desenvolveu sobre uma base antropocêntrica e apresenta uma série de mecanismos classificados dentro das três esferas de tutela, a administrativa, a penal e a civil. Com o advento de novas demandas, sobretudo a nanotecnologia e suas possíveis repercussões no futuro, alternativas precisam ser construídas para evitar o impedimento do desenvolvimento tecnológico e, ao mesmo tempo, proporcionar segurança. É possível concluir que a ausência de norma especifica para tutelar a utilização de produtos com nanotecnologia, não pode ser justificativa para não lançar mão de novos instrumentos capazes de realizar essa complexa tarefa. Não obstante a necessidade de uma regra específica, a aplicação de princípios de Direito Ambiental, como precaução, prevenção, poluidor-pagador e outras fontes do Direito, representa um caminho viável

Genome‐Wide Association Study of Pericardial Fat Area in 28 161 UK Biobank Participants

BACKGROUND: Pericardial adipose tissue (PAT) is the visceral adipose tissue compartment surrounding the heart. Experimental and observational research has suggested that greater PAT deposition might mediate cardiovascular disease, independent of general or subcutaneous adiposity. We characterize the genetic architecture of adiposity‐adjusted PAT and identify causal associations between PAT and adverse cardiac magnetic resonance imaging measures of cardiac structure and function in 28 161 UK Biobank participants. METHODS AND RESULTS: The PAT phenotype was extracted from cardiac magnetic resonance images using an automated image analysis tool previously developed and validated in this cohort. A genome‐wide association study was performed with PAT area set as the phenotype, adjusting for age, sex, and other measures of obesity. Functional mapping and Bayesian colocalization were used to understand the biologic role of identified variants. Mendelian randomization analysis was used to examine potential causal links between genetically determined PAT and cardiac magnetic resonance–derived measures of left ventricular structure and function. We discovered 12 genome‐wide significant variants, with 2 independent sentinel variants (rs6428792, P =4.20×10 −9 and rs11992444, P =1.30×10 −12 ) at 2 distinct genomic loci, that were mapped to 3 potentially causal genes: T‐box transcription factor 15 ( TBX15 ), tryptophanyl tRNA synthetase 2, mitochondrial ( WARS2 ) and early B‐cell factor‐2 ( EBF2 ) through functional annotation. Bayesian colocalization additionally suggested a role of RP4‐712E4.1. Genetically predicted differences in adiposity‐adjusted PAT were causally associated with adverse left ventricular remodeling. CONCLUSIONS: This study provides insights into the genetic architecture determining differential PAT deposition, identifies causal links with left structural and functional parameters, and provides novel data about the pathophysiological importance of adiposity distribution

Only Slight Impact of Predicted Replicative Capacity for Therapy Response Prediction

BACKGROUND: Replication capacity (RC) of specific HIV isolates is occasionally blamed for unexpected treatment responses. However, the role of viral RC in response to antiretroviral therapy is not yet fully understood. MATERIALS AND METHODS: We developed a method for predicting RC from genotype using support vector machines (SVMs) trained on about 300 genotype-RC pairs. Next, we studied the impact of predicted viral RC (pRC) on the change of viral load (VL) and CD4(+) T-cell count (CD4) during the course of therapy on about 3,000 treatment change episodes (TCEs) extracted from the EuResist integrated database. Specifically, linear regression models using either treatment activity scores (TAS), the drug combination, or pRC or any combination of these covariates were trained to predict change in VL and CD4, respectively. RESULTS: The SVM models achieved a Spearman correlation (rho) of 0.54 between measured RC and pRC. The prediction of change in VL (CD4) was best at 180 (360) days, reaching a correlation of rho = 0.45 (rho = 0.27). In general, pRC was inversely correlated to drug resistance at treatment start (on average rho = -0.38). Inclusion of pRC in the linear regression models significantly improved prediction of virological response to treatment based either on the drug combination or on the TAS (t-test; p-values range from 0.0247 to 4 10(-6)) but not for the model using both TAS and drug combination. For predicting the change in CD4 the improvement derived from inclusion of pRC was not significant. CONCLUSION: Viral RC could be predicted from genotype with moderate accuracy and could slightly improve prediction of virological treatment response. However, the observed improvement could simply be a consequence of the significant correlation between pRC and drug resistance

Solar neutrino oscillation from large extra dimensions

A plausible explanation for the existence of additional light sterile neutrinos is that they correspond to modulini, fermionic partners of moduli, which propagate in new large dimensions. We discuss the phenomenological implications of such states and show that solar neutrino oscillation is well described by small angle MSW oscillation to the tower of Kaluza Klein states associated with the modulini. In the optimal case the recoil electron energy spectrum agrees precisely with the measured one, in contrast to the single sterile neutrino case which is disfavoured. We also consider how all oscillation phenomena can be explained in a model including bulk neutrino states. In particular, we show that a naturally maximal mixing for atmospheric neutrinos can be easily obtained.Comment: 15 pages, 2 figure

Predicting the Response to Combination Antiretroviral Therapy: Retrospective Validation of geno2pheno-THEO on a Large Clinical Database

BackgroundExpert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure MethodsWe retrospectively validated the statistical model used by g2p-THEO in ∼7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega ResultsThe difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P<.001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed ConclusionFinding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.or

Implantação de unidade demonstrativa para cultivo de oliveiras no Planalto Norte Catarinense

The olive tree culture has been planted in small and large properties, in addition to many Brazilian regions that have a subtropical or temperate climate. Faced with the need to evaluate the adaptation of the olive tree culture and observe its agronomic performance, a demonstrative unit was implemented for the production of olive trees in the North Plateau of Santa Catarina. The project is a public-private partnership, with the participation of the Federal Institute of Santa Catarina – Campus Canoinhas, Agromillora – Production and Trade of Plant Seedlings and Casa Dom Pedro de Canoinhas/SC. The olive tree seedlings were planted in April 2022. Five varieties were planted, namely: Arbequina, Arbosana, Koroneiki, Lecciana and Oliana, spacing 4.0 m between rows x 1.50 m between plants. In August 2022, the first vegetative evaluation of the plants was carried out. The demonstrative unit, in addition to generating technical-scientific data, is of relevant importance for the exchange of experiences, serving as a reference for rural extension workers, students and professionals in the area. The demonstration unit is available for scheduled visits, to learn about the culture and monitor the development of the plants. Preliminary plant development data are promising, however, there is a need to conduct and evaluate the adaptation of the crop in the region for a longer period, in order to have greater security for its indication, as well as the most adapted cultivars for the region and management practices to be adopted.A cultura da oliveira tem sido plantada em pequenas e grandes propriedades, além de muitas regiões brasileiras que apresentam clima subtropical ou temperado. Diante da necessidade de avaliar a adaptação da cultura da oliveira e observar seu desempenho agronômico, implantou-se uma unidade demonstrativa para produção de oliveiras no Planalto Norte Catarinense. O projeto é uma parceria público-privada, com a participação do Instituto Federal de Santa Catarina – Campus Canoinhas, Agromillora – Produção e Comércio de Mudas Vegetais e Casa Dom Pedro de Canoinhas/SC. O plantio das mudas de oliveira ocorreu em abril de 2022. Realizou-se o plantio de cinco variedades, sendo: Arbequina, Arbosana, Koroneiki, Lecciana e Oliana, num espaçamento de 4,0 m entre filas x 1,50 m entre plantas. Em agosto de 2022, foi executado a primeira avaliação vegetativa das plantas. A unidade demonstrativa, além de gerar dados técnico-científicos, tem uma importância relevante para troca de experiências, servindo de referência para extensionistas rurais, estudantes e profissionais da área. A unidade demonstrativa fica disponível a visitações agendadas, para conhecer sobre a cultura e acompanhar o desenvolvimento das plantas. Os dados preliminares de desenvolvimento das plantas são promissores, no entanto, há a necessidade de conduzir e avaliar a adaptação da cultura na região por um período maior, a fim de haver maior segurança para sua indicação, bem como das cultivares mais adaptadas para a região e práticas de manejo a serem adotadas

Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org

Neutrinos below 100 TeV from the southern sky employing refined veto techniques to IceCube data

Many Galactic sources of gamma rays, such as supernova remnants, are expected to produce neutrinos with a typical energy cutoff well below 100 TeV. For the IceCube Neutrino Observatory located at the South Pole, the southern sky, containing the inner part of the Galactic plane and the Galactic Center, is a particularly challenging region at these energies, because of the large background of atmospheric muons. In this paper, we present recent advancements in data selection strategies for track-like muon neutrino events with energies below 100 TeV from the southern sky. The strategies utilize the outer detector regions as veto and features of the signal pattern to reduce the background of atmospheric muons to a level which, for the first time, allows IceCube searching for point-like sources of neutrinos in the southern sky at energies between 100 GeV and several TeV in the muon neutrino charged current channel. No significant clustering of neutrinos above background expectation was observed in four years of data recorded with the completed IceCube detector. Upper limits on the neutrino flux for a number of spectral hypotheses are reported for a list of astrophysical objects in the southern hemisphere