Midgut microbiota of the malaria mosquito vector Anopheles gambiae and Interactions with plasmodium falciparum Infection

The susceptibility of Anopheles mosquitoes to Plasmodium infections relies on complex interactions between the insect vector and the malaria parasite. A number of studies have shown that the mosquito innate immune responses play an important role in controlling the malaria infection and that the strength of parasite clearance is under genetic control, but little is known about the influence of environmental factors on the transmission success. We present here evidence that the composition of the vector gut microbiota is one of the major components that determine the outcome of mosquito infections. A. gambiae mosquitoes collected in natural breeding sites from Cameroon were experimentally challenged with a wild P. falciparum isolate, and their gut bacterial content was submitted for pyrosequencing analysis. The meta-taxogenomic approach revealed a broader richness of the midgut bacterial flora than previously described. Unexpectedly, the majority of bacterial species were found in only a small proportion of mosquitoes, and only 20 genera were shared by 80% of individuals. We show that observed differences in gut bacterial flora of adult mosquitoes is a result of breeding in distinct sites, suggesting that the native aquatic source where larvae were grown determines the composition of the midgut microbiota. Importantly, the abundance of Enterobacteriaceae in the mosquito midgut correlates significantly with the Plasmodium infection status. This striking relationship highlights the role of natural gut environment in parasite transmission. Deciphering microbe-pathogen interactions offers new perspectives to control disease transmission.Institut de Recherche pour le Developpement (IRD); French Agence Nationale pour la Recherche [ANR-11-BSV7-009-01]; European Community [242095, 223601]info:eu-repo/semantics/publishedVersio

Quick invariant signature extraction from binary images

It is shown how basic geometric notions can be used to extract an image signature independently of position, orientation and size. Simple primitives as lengths and slopes remain invariant by affine 2D similarity transformations. They can easily be used to define the invariant signature of an image. Contrary to the previous work in this area, images can be directly analyzed. This means that the extraction of interest points of the image is avoided. The method remains formal and no estimation or compression is needed. It is formally demonstrated that 100% of transformations are taken into consideration and that the signature of the image is totally invariant. The Quick Invariant Signature (QIS) extraction is a formal and fast method. It can be used either, only for signature extraction, or be integrated into a neural architecture for both extraction and classification. Unusual invariances such as cylindrical translation or toric translation are also defined by QIS

New approach for segmentation and quantification of two-dimensional gel electrophoresis images

Motivation: Detection of protein spots in two-dimensional gel electrophoresis images (2-DE) is a very complex task and current approaches addressing this problem still suffer from significant shortcomings. When quantifying a spot, most of the current software applications include a lot of background due to poor segmentation. Other software applications use a fixed window for this task, resulting in omission of part of the protein spot, or including background in the quantification. The approach presented here for the segmentation and quantification of 2-DE aims to minimize these problems. Results: Five sections from different gels are used to test the performance of the presented method concerning the detection of protein spots, and three gel sections are used to test the quantification of sixty protein spots. Comparisons with a state-of-the-art commercial software and an academic state-of-the-art approach are presented. It is shown that the proposed approach for segmentation and quantification of 2-DE images can compete with the available commercial and academic software packages.Peer reviewe

Information and communication technologies for people with disabilities

disabilitie

P-TRAP:A Panicle Trait Phenotyping tool

BACKGROUND: In crops, inflorescence complexity and the shape and size of the seed are among the most important characters that influence yield. For example, rice panicles vary considerably in the number and order of branches, elongation of the axis, and the shape and size of the seed. Manual low-throughput phenotyping methods are time consuming, and the results are unreliable. However, high-throughput image analysis of the qualitative and quantitative traits of rice panicles is essential for understanding the diversity of the panicle as well as for breeding programs. RESULTS: This paper presents P-TRAP software (Panicle TRAit Phenotyping), a free open source application for high-throughput measurements of panicle architecture and seed-related traits. The software is written in Java and can be used with different platforms (the user-friendly Graphical User Interface (GUI) uses Netbeans Platform 7.3). The application offers three main tools: a tool for the analysis of panicle structure, a spikelet/grain counting tool, and a tool for the analysis of seed shape. The three tools can be used independently or simultaneously for analysis of the same image. Results are then reported in the Extensible Markup Language (XML) and Comma Separated Values (CSV) file formats. Images of rice panicles were used to evaluate the efficiency and robustness of the software. Compared to data obtained by manual processing, P-TRAP produced reliable results in a much shorter time. In addition, manual processing is not repeatable because dry panicles are vulnerable to damage. The software is very useful, practical and collects much more data than human operators. CONCLUSIONS: P-TRAP is a new open source software that automatically recognizes the structure of a panicle and the seeds on the panicle in numeric images. The software processes and quantifies several traits related to panicle structure, detects and counts the grains, and measures their shape parameters. In short, P-TRAP offers both efficient results and a user-friendly environment for experiments. The experimental results showed very good accuracy compared to field operator, expert verification and well-known academic methods

Redundancy analysis for gut bacterial communities (taxonomic rank = class) in field and laboratory mosquitoes.

<p>The length of arrows indicates the strength of correlation between the variable and the ordination scores. Blue arrow: bacterial classes, green arrow: environmental variables. The Monte Carlo permutation test was used to test the statistical significance of the relationship between environmental variables and the bacterial classes. The “Flavo” (<i>Flavobacteriaceae</i>) segregates with “labo” environmental variable, “Alpha” (<i>Alphabacteriaceae</i>) with the “NKD” environmental variable (<i>P</i><0.05). All other bacterial classes segregate along the second axis, with the “Mvan” environmental variable.</p

Summary of pyrosequencing tags for the 3 amplified SSU rDNA regions.

<p>Summary of pyrosequencing tags for the 3 amplified SSU rDNA regions.</p

Comparison of bacterial diversity for the three 16S libraries at the genus level.

<p>Tag abundance was compared for three 16S libraries, and the graph shows the bacterial flora of six mosquito midguts. The three 16S libraries were obtained using primer sets targeting different 16S domains, as described in the <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002742#s4" target="_blank">Materials and Methods</a> section. Only the most abundant categories (>2%) were considered. The S1 library only reached 95%, showing this domain allowed identifications for a greater number of minor clades. For mosquito NKD97, S2 and S3 primer sets only allowed the identification at the <i>Enterobacteriaceae</i> family level, whereas S1 reached the assignment at the genus level, <i>Serratia</i>.</p

Relative abundance of the different bacterial classes within each mosquito midgut sample.

<p>Mvan and NKD (Nkolondon) indicate the geographical origin of mosquitoes, NG being mosquitoes of the laboratory colony Ngousso. Pf+ and Pf− designate the <i>P. falciparum</i> infection status of the challenged mosquitoes, positive and negative, respectively. Only class rank groups that represented >1% of the total reads, and identified in at least 30% of mosquitoes, are shown. “Unclass” represents tags that could not be assigned to the class level, and were grouped into a higher taxonomical rank.</p

Iterative illumination correction with implicit regularization

This paper presents a retrospective algorithm for correcting the uneven illumination field in microscopy images. The illumination field is iteratively made uniform using an increasing sequence of bivariate polynomials. At each iteration, the least squares problem of fitting a 2-D polynomial to a sampled image is solved by using QR decomposition with column pivoting, where image samples are obtained by dynamic programming or watershed transform. This incremental scheme allows the smoothness constraint of the estimated bias field to be implicitly satisfied. The proper number of iterations is determined by an automatic stopping criterion. The experimental results show the effectiveness of the proposed approach when compared to a set of different well-established methods