C. O158, P155 Ostrozhenkova, E. P45 O’Sullivan, J. P93 Ouellet, V. P33, P159 Ouisse, L.-H. O107 Ousset, M. P44 O’Valle Ravassa, F. O185 Øyan, A. M. O181, P132 Oyasu, M. P221 Ozer, J. P45 Pagano, A. P192 Page, M. P2 Pagès, F. P176 Pakdaman, S. P202 Palermo, C. O96 Pallardy, M. O86 Palmqvist, R. P146, P149, P164 Pancre, V. O48, P194 Papadopoulou, A. O68 Paradowska, A. P18 Parent, L. P209 Pargger, M. P53 Park, D. P155 JNJ-26481585 datasheet Park, R.-W. P197 Park, S. I. O171 Park, S.-Y. P198 Park, Y. P133 Parker, M. W. P66 Parkin, S. P157 Parteli, J. P91 Pasca di Magliano, M. P175 Pasupulati, S. P56 Patel, K. P220 Paterson, E. L. P28 Patsialou, A. O166 Paulsson, J. P57, P98 Pazolli, E. P29 Pearsall, A. P206 Pearsall, S. P206 Pebrel-Richard, C. P68 Pedersen, P.-H. P64 Peeters, M. O87 Peeters, P. J. P124 Peled, M. O115 Peluffo, G. O145 Peña, C. P10, P99 Penault-Llorca, F. P214 Penfold, M. E. T. P202 Peng, S.-B. O178 Peng, S. O175 A-1331852 solubility dmso Pennesi, G. O146 Pépin, F. P33, P155 Peralta-Leal,

A. O185 Perbal, B. P159 Pereira, M. C. P26 Pereira, P. P171 Persano, L. O23 Pesce, S. P166 Pestell, R. G. O184 Peter, H. O173 Petri, M. P18 Pettersson, S. O109 Pettigrew, J. O118 Pfeffer, U. O146 Pienta, K. O171 Pierré, A. P69 Pietras, K. O39 Pietzsch, J. P96, click here P180 Piktel, D. O99 Pinault, É P182 Pines, M. O183 Pink, D. O170 Pinte, S. P161 Piot, O. P134 Piura, P. P121 Piwnica-Worms, D. P29 Placencio, V. P100 Platonova, S. O106, P62, P101 Plaza-Calonge, M. C. P30 Pobre, E. P206 Pocard, M. O66, P69 Poirier, A. O32 Poletti, A. P46 Pollard, J. W. O1, P104 Polyak, K. O33, O145 Pomeranz, M. P112 Pommerencke, T. P78 Ponath, E. O92 Ponzoni, M. O116 Popel, A. P207 Porcasi, R. P163 Porchet, N. P14 Porquet, N. O32 Port, E. O160 Porta, C. O46 Postovit, L.-M. O6 Potiron, L. O107 Pouniotis, D. P102 Poupon, M.-F. O66 Poupot, M. P88 Pouysségur, J. O7, O59 Pradelli, E. P199, P202 Prébois, C. P42 Prestegarden, L. O181 Prévost, G. P69 Prevot, S. O86 Prieto,

V. O108 Pringels, S. O87 Prior, J. L. P29 Pritchard, ifoxetine M. A. P106 Proust, F. P63 Psaila, B. P119 Puapairoj, A. P114 Pucci, S. O61, O163 Pucelle, M. O84 Pusceddu, I. O23 Pyonteck, S. P103 Pyronnet, S. O84 Qayum, N. O176 Qian, B. P104 Querleu, D. P88 Quinn, D. P190 Raab, S. O12 Radenkovic, S. P105 Rafii, A. P88 Rafii, D. O160 Rafii, S. P119 Raghavan, D. P185 Rahat, M. A. O136 Rahav, G. P5 Rajoria, S. O76 Rakshit, S. P175 Ramirez, A. P172 Ranga, R. P56 Räsänen, K. P48, P160 Rath-Wolfson, L. P169 Ratti, C. P163 Raz, A. O3 Rechavi, O. O5 Redjimi, N. O86 Reed, R. K. P83, P132 Rehemtulla, A. P56 Reichle, A. O123, P200 Reiniš, M. O44, P162 Reitkopf, S. O12 Reka, A. K. P128 Rennie, P. P195 Rescigno, M. O64 Ressler, S. O65 Ricci, J.-E. P199 Ricciardelli, C. O173, P106 Rice, L. P205 Rich, C. P1 Richard-Fiardo, P. P203 Richon, S. O66 Rimoldi, M. O46 Rinerio, V. G. O105 Rio, M.-C.

The association between the incidence of clinical malaria attacks and independent Vistusertib variables, i.e. presence of antibodies to allelic families, age, haemoglobin type or ethnic group, was tested. Statistical analysis Yearly distribution of the 524 PCR fragments by allelic family was analysed by Pearson Chi2 with the assumption that the alleles co-infecting

the same individual were independent. Allelic family distribution by gender, age, Hb type, ABO group, Rhesus group and by month was analysed by Fisher’s exact test. The allelic family infection rate (percentage of infected individuals harbouring one or more alleles from that family) by gender, β-globin type, ABO or Rhesus blood group, by age (0-1 y, 2-5 y, 6-9 y, 10-19 y and ≥20 y) and by season in the year was analysed by Fisher’s

exact test. For the analysis of 7-Cl-O-Nec1 ic50 seasonality, the year was divided into three periods based on the rains, the vectors present and the entomological inoculation rate. The mean entomological inoculation rate was 32, 140 and 39 infected bites/person/year in February-May (dry season), June-October (rainy season), and November-January, respectively. The estimated multiplicity of infection was first analysed using a zero-truncated Poisson regression model, with the assumption of a constant probability to detect an additional allele in a homogeneous carrier population. The mean predicted estimated moi was 1.193 allele/infected individual. The predicted distribution was calculated, grouping the classes with estimated moi ≥ 4 and did not differ from the observed one (51.6% vs. 51.9%, www.selleckchem.com/products/Romidepsin-FK228.html 29.4% vs. 31%, 15.0% vs. 12.3%, 3.9% vs. 3.7% for observed vs. predicted estimated moi 1, 2, 3 and ≥4, respectively (Chi2 test, 3 df ≥ 2.53, p = 0.47). Estimated moi distribution by age group (0-1 y, 2-5 y, 6-9 y, 10-19 y and ≥20 y), gender, Hb type, ABO group, Rhesus blood group, year, month of the year and season was analysed by non parametric Kruskal-Wallis test. Acknowledgements We are indebted to the Dielmo villagers for their invaluable help and commitment to participate in the longitudinal study. The dedication of Hilaire Bouganali

in Quinapyramine microscopy slide reading deserves special thanks. We also thank the field medical staff, the village workers and the entomology team for their dedication over the ten year period, in particular Didier Fontenille, Laurence Lochouarn and Ibrahima Dia. We thank Thierry Fandeur for insightful comments on the manuscript. This work was funded by the Prix Louis D of the French Academy of Sciences as well as by the Génopole, Institut Pasteur. NN was supported by a PhD fellowship from the Royal Golden Jubilee, Thailand Research Fund and from the EU-funded grant QLK2-CT-2002-01503 (RESMALCHIP). Electronic supplementary material Additional file 1: Distribution frequency of Pfmsp1 block2 fragment size in Dielmo, Senegal.

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