Cost-Effectiveness of an Intervention to Reduce HIV/STI Incidence and Promote Condom Use among Female Sex Workers in the Mexico–US Border Region

Previous research demonstrated efficacy of a brief behavioral intervention to reduce incidence of HIV and sexually transmitted infections (STIs) among female sex workers (FSWs) in Tijuana and Ciudad Juarez, Mexico, cities on Mexico's border with the US. We assessed this intervention's cost-effectiveness.A life-time Markov model was developed to estimate HIV cases prevented, changes in quality-adjusted life expectancy (QALE), and costs per additional quality-adjusted life year gained (QALY), comparing (in US$2,009) no intervention to a once-only and annual intervention. Future costs and health benefits were discounted annually at 3$183. For FSWs receiving the intervention annually, there were 29 additional HIV cases prevented and 4.5 additional months of QALE compared to the once-only intervention. The additional cost per QALY was US$1,075. When highly active antiretroviral therapy (HAART) was included in the model, the annual intervention strategy resulted in net savings and dominated both once-only and no intervention strategies, and remained robust across extensive sensitivity analyses. Even when considering clinical benefits from HAART, ignoring added costs, the cost per QALY gained remained below three times the Mexican GDP per capita, and below established cost-effectiveness thresholds.This brief intervention was shown to be cost-effective among FSWs in two Mexico-US border cities and may have application for FSWs in other resource-limited settings.ClinicalTrials.gov NCT00338845

A Feature-Based Approach to Modeling Protein–DNA Interactions

Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF–DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/

Leveraging Genetic Variability across Populations for the Identification of Causal Variants

Genome-wide association studies have been performed extensively in the last few years, resulting in many new discoveries of genomic regions that are associated with complex traits. It is often the case that a SNP found to be associated with the condition is not the causal SNP, but a proxy to it as a result of linkage disequilibrium. For the identification of the actual causal SNP, fine-mapping follow-up is performed, either with the use of dense genotyping or by sequencing of the region. In either case, if the causal SNP is in high linkage disequilibrium with other SNPs, the fine-mapping procedure will require a very large sample size for the identification of the causal SNP. Here, we show that by leveraging genetic variability across populations, we significantly increase the localization success rate (LSR) for a causal SNP in a follow-up study that involves multiple populations as compared to a study that involves only one population. Thus, the average power for detection of the causal variant will be higher in a joint analysis than that in studies in which only one population is analyzed at a time. On the basis of this observation, we developed a framework to efficiently search for a follow-up study design: our framework searches for the best combination of populations from a pool of available populations to maximize the LSR for detection of a causal variant. This framework and its accompanying software can be used to considerably enhance the power of fine-mapping studies

Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters

Este artículo contiene 25 páginas, 9 figuras.In the Anthropocene, watershed chemical transport is increasingly dominated by novel combinations of elements, which are hydrologically linked together as ‘chemical cocktails.’ Chemical cocktails are novel because human activities greatly enhance elemental concentrations and their probability for biogeochemical interactions and shared transport along hydrologic flowpaths. A new chemical cocktail approach advances our ability to: trace contaminant mixtures in watersheds, develop chemical proxies with high-resolution sensor data, and manage multiple water quality problems. We explore the following questions: (1) Can we classify elemental transport in watersheds as chemical cocktails using a new approach? (2) What is the role of climate and land use in enhancing the formation and transport of chemical cocktails in watersheds? To address these questions, we first analyze trends in concentrations of carbon, nutrients, metals, and salts in fresh waters over 100 years. Next, we explore how climate and land use enhance the probability of formation of chemical cocktails of carbon, nutrients, metals, and salts. Ultimately, we classify transport of chemical cocktails based on solubility, mobility, reactivity, and dominant phases: (1) sieved chemical cocktails (e.g., particulate forms of nutrients, metals and organic matter); (2) filtered chemical cocktails (e.g., dissolved organic matter and associated metal complexes); (3) chromatographic chemical cocktails (e.g., ions eluted from soil exchange sites); and (4) reactive chemical cocktails (e.g., limiting nutrients and redox sensitive elements). Typically, contaminants are regulated and managed one element at a time, even though combinations of elements interact to influence many water quality problems such as toxicity to life, eutrophication, infrastructure corrosion, and water treatment. A chemical cocktail approach significantly expands evaluations of water quality signatures and impacts beyond single elements to mixtures. High-frequency sensor data (pH, specific conductance, turbidity, etc.) can serve as proxies for chemical cocktails and improve real-time analyses of water quality violations, identify regulatory needs, and track water quality recovery following storms and extreme climate events. Ultimately, a watershed chemical cocktail approach is necessary for effectively co-managing groups of contaminants and provides a more holistic approach for studying, monitoring, and managing water quality in the Anthropocene.This work was funded by USDA (award # 2016-67019-25280) and NSF-EPSCoR (#1641157) for supporting collaborations at the AGU Chapman Conference on Extreme Climate Events. Significant funding for data collection/analyses in this paper was provided by NSF EAR1521224, NSF CBET1058502, NSF Coastal SEES1426844, NSF DEB-0423476 and DEB-1027188, NSF RI EPSCoR NEWRnet Grant No. IIA-1330406, EPA ORD, Chesapeake Bay Trust, and Multi-state Regional Hatch Project S-1063.Peer reviewe