Isolation of 13 novel highly polymorphic microsatellite loci for the Amazonian Palm Mauritia flexuosa L.f. (Arecaceae)

Mauritia flexuosa L.f. (Arecaceae) is a New World tropical palm that generally grows in isolated swamps along meandering rivers and is in danger of fragmentation through unsustainable harvest practices. To explore gene flow among populations of M. flexuosa in Amazonia, we developed 13 novel, polymorphic microsatellite loci for M. flexuosa. Further studies will employ these loci to investigate the impacts of artisanal gold mining and wild-harvest on gene flow among populations of M. flexuosa.     

Short report: Phylogenetic relationships of the anthropophilic Plasmodium falciparum malaria vectors in Africa

Malaria kills more than one million people a year, and understanding the historical association between its most notorious causative agent, Plasmodium falciparum, and its mosquito vectors is important in fighting the disease. We present a phylogenetic analysis of a number of species within the mosquito subgenus Cellia based on a selection of mitochondrial and nuclear genes. Although some of these relationships have been estimated in other studies, generally few species were included and/or statistical support at many nodes was low. Here we include two additional species of anthropophilic P. falciparum malaria vectors and reanalyze these relationships using a Bayesian method that allows us to simultaneously incorporate different models of evolution. We report data that indicate a paraphyletic relationship between five anthropophilic African mosquito vectors. Such a relationship suggests that these species can serve as independent natural experiments for anopheline immunologic responses to regular, prolonged contact with P. falciparum.     

Multiple leiomyomas of the vagina in a premenopausal woman

Case report A 32-year-old multiparous woman was seen for a history of dyspareunia and vaginal pain. She presented with two firm masses on the lower-medium left lateral wall of the vagina. Transvaginal ultrasonography showed two spherical smooth-walled masses, respectively of 33 and 35 mm in diameter. A transvaginal approach was used to remove the two tumors en bloc.Conclusion Histologically, the tumors were leiomyomas.     

Acute hepatitis C in Brazil: results of a national survey

The incidence of acute hepatitis C has decreased in the world. However, new cases are still reported. The objective of this study was to obtain data of acute hepatitis C in Brazil and to identify risk factors of transmission, diagnostic criteria, clinical presentation, evolution, and treatment. A questionnaire was sent to all members of the Brazilian Society of Hepatology. Sixteen centers participated with a total of 170 cases between 2000 and 2008. Among them, 37 had chronic renal failure on hemodialysis and were evaluated separately. The main diagnostic criterion in non‐uremic patients was ALT (alanine aminotransferase) elevation associated with risk factors. In patients with chronic renal failure, anti‐hepatitis C virus (HCV) seroconversion was the most frequent criterion. Among the 133 non‐uremic patients the main risk factors were hospital procedures, whereas in hemodialysis patients, dialysis was the single risk factor in 95% of the cases. Jaundice was more frequent in non‐uremic patients (82% vs. 13%; P < 0.001) and ALT levels were higher in these individuals (P < 0.001). Spontaneous clearance was more frequent in non‐uremic patients (51% vs. 3%; P < 0.001). Sixty‐five patients were treated: 39 non‐uremic patients and 26 on dialysis. Sustained virological response rates were 60% for non‐uremic and 58% for uremic patients (P = 0.98). There was no association of these rates with the study variables. These findings show that cases of acute hepatitis C are still occurring and have been related predominantly to hospital procedures. Measures to prevent nosocomial transmission should be adopted rigorously and followed to minimize this important source of infection observed in this survey. J. Med. Virol. 83:1738–1743, 2011. © 2011 Wiley‐Liss, Inc.     

Interferon-alpha therapy within the first year after acute hepatitis C infection in hemodialysis patients: efficacy and tolerance

BACKGROUND: Interferon monotherapy significantly reduces the chronicity rate of acute hepatitis C in nonuremic patients. In this clinical study, we evaluated the efficacy and tolerance of interferon-alpha therapy for acute hepatitis C in hemodialysis patients. METHODS: Patients with acute hepatitis C, established on the basis of seroconversion to anti-hepatitis C virus and the presence of hepatitis C virus RNA, received a low dose of interferon-alpha (3 MU three times per week) for 12 months or a high dose (5 MU three times per week, preceded by a daily induction dose) for 6 months. Response to treatment was defined as undetectable hepatitis C virus RNA at the end of treatment and sustained virological response was defined as persistent negative hepatitis C virus RNA 6 months after the end of treatment. RESULTS: Twenty-three patients were treated, 16 with a low dose of interferon-alpha and seven with a high dose. At the end of treatment, hepatitis C virus RNA was undetectable in 16/23 patients (70%). Of these, 6/23 patients (26%) relapsed and 10/23 (43%) maintained a sustained virological response (38% in lower doses vs. 57% in higher doses). Treatment was well tolerated and only three patients discontinued therapy (13%). CONCLUSION: Interferon-alpha within the first year after acute hepatitis C in hemodialysis patients was found to be safe and effective, inducing a sustained virological response in 43% of cases. This study supports the routine indication of acute hepatitis C treatment with interferon-alpha for hemodialysis patients, and higher doses administered for a shorter period of time should be tried according to the tolerance of the patients.     

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized F_ST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

Landscape genetics—explicitly quantifying the effects of a heterogenous landscape on gene flow—is an important tool for both conservation biology and the control of invasive species and disease vectors including the “yellow fever mosquito” (Aedes aegypti) (1, 2). We demonstrate that current limitations in landscape genetics can be addressed with a machine-learning approach integrated into an iterative optimization process. Isolation by distance (IBD) is a classical model in population genetics that assumes dispersal is limited in proportion to geographic distance, resulting in increasing genetic differentiation with increasing geographic distance between populations (3–5). Although this pattern is commonly seen in nature, factors such as history and dispersal limitations caused by the environment (i.e., “isolation by resistance”) (6) can produce deviations from IBD. Landscape resistance (alias friction) and its inverse, connectivity, determine how organisms move through a landscape (7). Modeling landscape connectivity can be used to identify the environmental variables that affect the organisms’ gene flow and genetic structure; predict how climate and land use change will affect their gene flow and distribution in the future; and inform conservation, vector control, and other management decisions (1, 8–13). Our goals are to use environmental data (the predictors) to build a model of genetic connectivity (the observed data) that improves on IBD and to identify environmental drivers of gene flow patterns.We implement a machine-learning approach that offers a number of advantages over classical methods in landscape genetics: The machine-learning approach is more objective, it allows the inclusion of correlated variables, and it is able to account for different shapes and magnitudes of correlations between predictor and response variables at different locations in the landscape (14–17). In comparison, a common approach in landscape genetics called resistance surface mapping involves the subjective process of creating resistance surfaces for environmental variables, in which each pixel represents a hypothesized resistance to the organism’s movement often based on expert opinion (6, 18). Effective landscape distances through the resistance surfaces can be found with least cost path or circuit theory analysis (19) and then analyzed for associations with genetic distance (20).One option to circumvent the subjectivity of creating resistance surfaces is to model genetic connectivity directly from environmental data. Bouyer et al. (7) took this approach and used a maximum-likelihood method to integrate genetic data and environmental data to map landscape resistance in tsetse flies. Additionally, they introduced an iterative optimization approach in which each subsequent iteration used least cost path lines through the previously predicted resistance surface—an improvement over modeling organism movement as straight lines (16, 17). While this presented a major advance, the maximum-likelihood methodology requires exclusion of correlated data, establishing the relationship between environmental variables and genetic distance before building the model, and transforming or discretizing nonlinear relationships. Additionally, this approach assumes one relationship between each environmental variable and the genetic data across the whole landscape. To build on previous advances while overcoming some of their limitations, we combine iterative optimization with a machine-learning method called random forest (RF).RF is a nonlinear classification and regression tree analysis that can handle many inputs, including redundant or irrelevant variables, as well as continuous and categorical data types (14, 15). RF creates many internal training/testing subdatasets and aggregates the predictors, resulting in stable and consistent results that generally do not overfit the data and can be evaluated through validation processes (14). It is easier to tune and less likely to overfit noisy data than another machine-learning method we considered, gradient boosting (21). Additionally, RF has been successfully incorporated into ecological studies (22) and a small number of landscape genetics studies (16, 17, 23). These studies considered only the environmental predictor values at the genetic collection sites (23) or along straight lines between each pair of sites (16, 17), in contrast to the least cost path analysis we implement here (7).We demonstrate the efficacy of our method to map landscape connectivity for an important disease vector. Ae. aegypti is highly invasive and the primary vector of yellow fever, Zika, dengue, and chikungunya. Except for yellow fever, there are no reliable, widely used vaccines for these diseases, so vector control is essential. Ae. aegypti originated in Africa and is now found throughout the tropics and increasingly in temperate regions (24–26). The species is temperature constrained, preferring warm, humid areas close to humans (the females’ preferred source for bloodmeals outside their native African range) (27). In the United States, it has a patchy distribution throughout southern states, especially Texas, Florida, and California (28). Although Ae. aegypti can disperse >1 km, its usual lifetime dispersal is only around 200 m (29–32). Passive “hitchhiking” via human transportation networks is responsible for long-distance invasions and worldwide spread of Ae. aegypti and its close relative (33–35). Climate change is also expanding the range of Aedes species, which could expose nearly 1 billion additional people to diseases carried by these mosquitoes for the first time (26).Although IBD is common in nature and a helpful null model in landscape genetics (20), geographic distance is often an inadequate sole predictor of genetic distance (as in the case of our dataset; SI Appendix, Fig. S1). Therefore, a more complex model is needed to explain and predict genetic distance and corresponding landscape connectivity. In this paper we introduce an iterative machine-learning approach to integrate environmental predictors and genetic observation data and apply it to map landscape connectivity for the Ae. aegypti mosquito in North America. We also find and examine the most important variables for building the connectivity model and provide validation of our proposed method.     

A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification

Breast radiological density is a determinant of breast cancer risk and of mammography sensitivity and may be used to personalize screening approach. We first analyzed the reproducibility of visual density assessment by eleven experienced radiologists classifying a set of 418 digital mammograms: reproducibility was satisfactory on a four (BI-RADS D1-2-3-4: weighted kappa = 0.694-0.844) and on a two grade (D1-2 vs D3-4: kappa = 0.620-0.851), but subjects classified as with dense breast would range between 25.1 and 50.5% depending on the classifying reader. Breast density was then assessed by computer using the QUANTRA software which provided systematically lower density percentage values as compared to visual classification. In order to predict visual classification results in discriminating dense and non-dense breast subjects on a two grade scale (D3-4 vs, D1-2) the best fitting cut off value observed for QUANTRA was ≤22.0%, which correctly predicted 88.6% of D1-2, 89.8% of D3-4, and 89.0% of total cases. Computer assessed breast density is absolutely reproducible, and thus to be preferred to visual classification. Thus far few studies have addressed the issue of adjusting computer assessed density to reproduce visual classification, and more similar comparative studies are needed.