RTS,S/AS02 and the quest of the Holy Grail

High-profile programs under the WHO/Roll Back Malaria initiative, in addition to unravelling the human and malaria parasite genomes, have ensured that malaria vaccine research and development are enjoying an unprecedented boom. So far, the development of a vaccine against such a complex parasite has been elusive. Recently, there have also been concerns that imperfect vaccines could encourage the selection of more virulent parasite strains [1]. However, there is compelling evidence that, if an effective malaria vaccine was developed, it would prove to be protective because several studies have shown that: (1) immunity to malaria can develop of multiple Plasmodium infection; and (2) exposure to bites from irradiated Anopheles infected with Plasmodium falciparum can confer protection against infection for up to 10 months. Based on these findings, effector T-cell vaccines that rate pre-erythrocytic stages of the parasite in infected hepatocytes have been developed

Utilizing Statistical Dialogue Act Processing in Verbmobil

In this paper, we present a statistical approach for dialogue act processing in the dialogue component of the speech-to-speech translation system \vm. Statistics in dialogue processing is used to predict follow-up dialogue acts. As an application example we show how it supports repair when unexpected dialogue states occur.Comment: 6 pages; compressed and uuencoded postscript file; to appear in ACL-9

Flexible semiparametric mixed models

In linear mixed models the influence of covariates is restricted to a strictly parametric form. With the rise of semi- and nonparametric regression also the mixed model has been expanded to allow for additive predictors. The common approach uses the representation of additive models as mixed models. An alternative approach that is proposed in the present paper is likelihood based boosting. Boosting originates in the machine learning community where it has been proposed as a technique to improve classification procedures by combining estimates with reweighted observations. Likelihood based boosting is a general method which may be seen as an extension of L2 boost. In additive mixed models the advantage of boosting techniques in the form of componentwise boosting is that it is suitable for high dimensional settings where many influence variables are present. It allows to fit additive models for many covariates with implicit selection of relevant variables and automatic selection of smoothing parameters. Moreover, boosting techniques may be used to incorporate the subject-specific variation of smooth influence functions by specifying random slopes on smooth e ects. This results in flexible semiparametric mixed models which are appropriate in cases where a simple random intercept is unable to capture the variation of e ects across subjects

Smoothing sparse and unevenly sampled curves using semiparametric mixed models: An application to online auctions

Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data which can be difficult if the data is irregularly distributed. In this paper we present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. While the model can handle sparse and unevenly distributed data, it also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's online auctions. Online auctions produce monotonic increasing price curves that are often correlated across two auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also estimates the underlying increasing trend from the data without imposing model-constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an online auction, our approach also results in more accurate price predictions compared to standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants

Rapid Detection of Leishmania infantum Infection in Dogs: Comparative Study Using an Immunochromatographic Dipstick Test, Enzyme-Linked Immunosorbent Assay, and PCR

Current zoonotic visceral leishmaniasis (ZVL) control programs in Brazil include the culling of Leishmania infantum-infected reservoir dogs, a strategy that has failed to prevent a rise of canine and human ZVL cases over the past decade. One of the main reasons this strategy has failed is because of a long delay between sample collection, sample analysis, and control implementation. A rapid, sensitive, and specific diagnostic tool would be highly desirable, because it would allow control interventions to be implemented in situ. We compared an immunochromatographic dipstick test to enzyme-linked immunosorbent assay (ELISA) and PCR for detecting L. infantum infections in dogs from an area of ZVL endemicity in Brazil. The dipstick test was shown to have 61 to 75% specificity and 72 to 77% sensitivity, compared to 100% specificity for both ELISA and PCR and 71 to 88% and 51 to 64% sensitivity for ELISA and PCR, respectively. Of the field samples tested, 92 of 175 (53%), 65 of 175 (37%), and 47 of 175 (27%) were positive by dipstick, ELISA, and PCR, respectively. The positive and negative predictive values for the tested dipstick were 58 to 77% and 75%, respectively. Efforts should be made to develop a more specific dipstick test for diagnosis of leishmaniasis, because they may ultimately prove more cost-effective than currently used diagnostic tests when used in mass-screening surveys

A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System

We present the dialogue component of the speech-to-speech translation system VERBMOBIL. In contrast to conventional dialogue systems it mediates the dialogue while processing maximally 50% of the dialogue in depth. Special requirements like robustness and efficiency lead to a 3-layered hybrid architecture for the dialogue module, using statistics, an automaton and a planner. A dialogue memory is constructed incrementally.Comment: Postscript file, compressed and uuencoded, 15 pages, to appear in Proceedings of EACL-95, Dublin

Some experiments in speech act prediction

In this paper, we present a statistical approach for speech act prediction in the dialogue component of the speech-to-speech translation system Verbmobil. The prediction algorithm is based on work known from language modelling and uses N-gram information computed from a training corpus. We demonstrate the performance of this method with 10 experiments. These experiments vary in two dimensions, namely whether the N-gram information is updated while processing, and whether deviations from the standard dialogue structure are processed. Six of the experiments use complete dialogues, while four process only the speech acts of one dialogue partner. It is shown that the predictions are best when using the update feature and deviations are not processed. Even the processing of incomplete dialogues then yields acceptable results. Another experiment shows that a training corpus size of about 40 dialogues is sufficient for the prediction task, and that the structure of the dialogues of the Verbmobil corpus we use differs remarkably with respect to the predictions

Evaluation of PCR as a diagnostic mass-screening tool to detect Leishmania (Viannia) spp. in domestic dogs (Canis familiaris).

Several studies have suggested that the PCR could be used in epidemiological mass-screening surveys to detect Leishmania (Viannia) spp. infection in human and animal hosts. Dogs from an area of Leishmania braziliensis and Leishmania peruviana endemicity were screened for American cutaneous leishmaniasis (ACL) infection by established PCR-based and enzyme-linked immunosorbent antibody test (ELISA) protocols. PCR detected Leishmania (Viannia) infection in a total of 90 of 1,066 (8.4%) dogs: 32 of 368 (8.7%), 65 of 769 (8.5%), and 7 of 42 (16.7%) dogs were PCR positive by testing of whole blood, buffy coat, and bone marrow aspirates, respectively. ELISA detected infection in 221 of 1,059 (20.9%) tested dogs. The high prevalence of Leishmania (Viannia) detected by PCR and ELISA in both asymptomatic (7.5 and 19.2%, respectively) and symptomatic (32 and 62.5%, respectively) dogs is further circumstantial evidence for their suspected role as reservoir hosts of ACL. However, the low sensitivity of PCR (31%) compared to ELISA (81%) indicates that PCR cannot be used for mass screening of samples in ACL epidemiological studies. Unless more-sensitive PCR protocols were to be developed, its use should be restricted to the diagnosis of active (canine and human) cases and to the parasitological monitoring of patients after chemotherapy