Transport and superconducting properties of Fe-based superconductors: SmFeAs(O1-x Fx) versus Fe1+y (Te1-x, Sex)

We present transport and superconducting properties - namely resistivity, magnetoresistivity, Hall effect, Seebeck effect, thermal conductivity, upper critical field - of two different families of Fe-based superconductors, which can be viewed in many respects as end members: SmFeAs(O1-xFx) with the largest Tc and the largest anisotropy and Fe1+y(Te1-x,Sex), with the largest Hc2, the lowest Tc and the lowest anisotropy. In the case of the SmFeAs(O1-xFx) series, we find that a single band description allows to extract an approximated estimation of band parameters such as carrier density and mobility from experimental data, although the behaviour of Seebeck effect as a function of doping demonstrates that a multiband description would be more appropriate. On the contrary, experimental data of the Fe1+y(Te1-x,Sex) series exhibit a strongly compensated behaviour, which can be described only within a multiband model. In the Fe1+y(Te1-x,Sex) series, the role of the excess Fe, tuned by Se stoichiometry, is found to be twofold: it dopes electrons in the system and it introduces localized magnetic moments, responsible for Kondo like scattering and likely pair-breaking of Cooper pairs. Hence, excess Fe plays a crucial role also in determining superconducting properties such as the Tc and the upper critical field Bc2. The huge Bc2 values of the Fe1+y(Te1-x,Sex) samples are described by a dirty limit law, opposed to the clean limit behaviour of the SmFeAs(O1-xFx) samples. Hence, magnetic scattering by excess Fe seems to drive the system in the dirty regime, but its detrimental pairbreaking role seems not to be as severe as predicted by theory. This issue has yet to be clarified, addressing the more fundamental issue of the interplay between magnetism and superconductivity

Optical study of orbital excitations in transition-metal oxides

The orbital excitations of a series of transition-metal compounds are studied by means of optical spectroscopy. Our aim was to identify signatures of collective orbital excitations by comparison with experimental and theoretical results for predominantly local crystal-field excitations. To this end, we have studied TiOCl, RTiO3 (R=La, Sm, Y), LaMnO3, Y2BaNiO5, CaCu2O3, and K4Cu4OCl10, ranging from early to late transition-metal ions, from t_2g to e_g systems, and including systems in which the exchange coupling is predominantly three-dimensional, one-dimensional or zero-dimensional. With the exception of LaMnO3, we find orbital excitations in all compounds. We discuss the competition between orbital fluctuations (for dominant exchange coupling) and crystal-field splitting (for dominant coupling to the lattice). Comparison of our experimental results with configuration-interaction cluster calculations in general yield good agreement, demonstrating that the coupling to the lattice is important for a quantitative description of the orbital excitations in these compounds. However, detailed theoretical predictions for the contribution of collective orbital modes to the optical conductivity (e.g., the line shape or the polarization dependence) are required to decide on a possible contribution of orbital fluctuations at low energies, in particular in case of the orbital excitations at about 0.25 eV in RTiO3. Further calculations are called for which take into account the exchange interactions between the orbitals and the coupling to the lattice on an equal footing.Comment: published version, discussion of TiOCl extended to low T, improved calculation of orbital excitation energies in TiOCl, figure 16 improved, references updated, 33 pages, 20 figure

Wind direction and proximity to larval sites determines malaria risk in Kilifi District in Kenya

Studies of the fine-scale spatial epidemiology of malaria consistently identify malaria hotspots, comprising clusters of homesteads at high transmission intensity. These hotspots sustain transmission, and may be targeted by malaria-control programmes. Here we describe the spatial relationship between the location of Anopheles larval sites and human malaria infection in a cohort study of 642 children, aged 1–10-years-old. Our data suggest that proximity to larval sites predict human malaria infection, when homesteads are upwind of larval sites, but not when homesteads are downwind of larval sites. We conclude that following oviposition, female Anophelines fly upwind in search for human hosts and, thus, malaria transmission may be disrupted by targeting vector larval sites in close proximity, and downwind to malaria hotspots

Spatio-temporal analysis of malaria incidence at the village level in a malaria-endemic area in Hainan, China

<p>Abstract</p> <p>Background</p> <p>Malaria incidence in China's Hainan province has dropped significantly, since Malaria Programme of China Global Fund Round 1 was launched. To lay a foundation for further studies to evaluate the efficacy of Malaria Programme and to help with public health planning and resource allocation in the future, the temporal and spatial variations of malaria epidemic are analysed and areas and seasons with a higher risk are identified at a fine geographic scale within a malaria endemic county in Hainan.</p> <p>Methods</p> <p>Malaria cases among the residents in each of 37 villages within hyper-endemic areas of Wanning county in southeast Hainan from 2005 to 2009 were geo-coded at village level based on residence once the patients were diagnosed. Based on data so obtained, purely temporal, purely spatial and space-time scan statistics and geographic information systems (GIS) were employed to identify clusters of time, space and space-time with elevated proportions of malaria cases.</p> <p>Results</p> <p>Purely temporal scan statistics suggested clusters in 2005,2006 and 2007 and no cluster in 2008 and 2009. Purely spatial clustering analyses pinpointed the most likely cluster as including three villages in 2005 and 2006 respectively, sixteen villages in 2007, nine villages in 2008, and five villages in 2009, and the south area of Nanqiao town as the most likely to have a significantly high occurrence of malaria. The space-time clustering analysis found the most likely cluster as including three villages in the south of Nanqiao town with a time frame from January 2005 to May 2007.</p> <p>Conclusions</p> <p>Even in a small traditional malaria endemic area, malaria incidence has a significant spatial and temporal heterogeneity on the finer spatial and temporal scales. The scan statistics enable the description of this spatiotemporal heterogeneity, helping with clarifying the epidemiology of malaria and prioritizing the resource assignment and investigation of malaria on a finer geographical scale in endemic areas.</p

Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data

BACKGROUND: In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings. METHODS: 1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests. RESULTS: ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model. CONCLUSIONS: ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population

Malaria risk factors in north-east Tanzania

BACKGROUND: Understanding the factors which determine a household's or individual's risk of malaria infection is important for targeting control interventions at all intensities of transmission. Malaria ecology in Tanzania appears to have reduced over recent years. This study investigated potential risk factors and clustering in face of changing infection dynamics. METHODS: Household survey data were collected in villages of rural Muheza district. Children aged between six months and thirteen years were tested for presence of malaria parasites using microscopy. A multivariable logistic regression model was constructed to identify significant risk factors for children. Geographical information systems combined with global positioning data and spatial scan statistic analysis were used to identify clusters of malaria. RESULTS: Using an insecticide-treated mosquito net of any type proved to be highly protective against malaria (OR 0.75, 95% CI 0.59-0.96). Children aged five to thirteen years were at higher risk of having malaria than those aged under five years (OR 1.71, 95% CI 1.01-2.91). The odds of malaria were less for females when compared to males (OR 0.62, 95% CI 0.39-0.98). Two spatial clusters of significantly increased malaria risk were identified in two out of five villages. CONCLUSIONS: This study provides evidence that recent declines in malaria transmission and prevalence may shift the age groups at risk of malaria infection to older children. Risk factor analysis provides support for universal coverage and targeting of long-lasting insecticide-treated nets (LLINs) to all age groups. Clustering of cases indicates heterogeneity of risk. Improved targeting of LLINs or additional supplementary control interventions to high risk clusters may improve outcomes and efficiency as malaria transmission continues to fall under intensified control

Estimating Individual Exposure to Malaria Using Local Prevalence of Malaria Infection in the Field

BACKGROUND: Heterogeneity in malaria exposure complicates survival analyses of vaccine efficacy trials and confounds the association between immune correlates of protection and malaria infection in longitudinal studies. Analysis may be facilitated by taking into account the variability in individual exposure levels, but it is unclear how exposure can be estimated at an individual level. METHOD AND FINDINGS: We studied three cohorts (Chonyi, Junju and Ngerenya) in Kilifi District, Kenya to assess measures of malaria exposure. Prospective data were available on malaria episodes, geospatial coordinates, proximity to infected and uninfected individuals and residence in predefined malaria hotspots for 2,425 individuals. Antibody levels to the malaria antigens AMA1 and MSP1(142) were available for 291 children from Junju. We calculated distance-weighted local prevalence of malaria infection within 1 km radius as a marker of individual's malaria exposure. We used multivariable modified Poisson regression model to assess the discriminatory power of these markers for malaria infection (i.e. asymptomatic parasitaemia or clinical malaria). The area under the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of the models. Local malaria prevalence within 1 km radius and AMA1 and MSP1(142) antibodies levels were independently associated with malaria infection. Weighted local malaria prevalence had an area under ROC curve of 0.72 (95%CI: 0.66-0.73), 0.71 (95%CI: 0.69-0.73) and 0.82 (95%CI: 0.80-0.83) among cohorts in Chonyi, Junju and Ngerenya respectively. In a small subset of children from Junju, a model incorporating weighted local malaria prevalence with AMA1 and MSP1(142) antibody levels provided an AUC of 0.83 (95%CI: 0.79-0.88). CONCLUSION: We have proposed an approach to estimating the intensity of an individual's malaria exposure in the field. The weighted local malaria prevalence can be used as individual marker of malaria exposure in malaria vaccine trials and longitudinal studies of natural immunity to malaria

Why Are There So Few Rickettsia conorii conorii-Infected Rhipicephalus sanguineus Ticks in the Wild?

The bacterium Rickettsia conorii conorii is the etiological agent of Mediterranean spotted fever (MSF), which is a life-threatening infectious disease that is transmitted by Rhipicephalus sanguineus, the brown dog tick. Rh. sanguineus-R. conorii conorii relationships in the wild are still poorly understood one century after the discovery of the disease. In this study, we collected naturally infected ticks from the houses of people afflicted by MSF in Algeria. Colonies of both infected and non-infected ticks were maintained in our laboratory, and we studied the effect of temperature variations on the infected and non-infected ticks. We did not observe any major differences between the biological life cycle of the infected and non-infected ticks held at 25°C. However, a comparatively higher mortality relative to the control group was noticed when R. conorii conorii-infected engorged nymphs and adults were exposed to a low temperature (4°C) or high temperature (37°C) for one month and transferred to 25°C. R. conorii conorii-infected Rh. sanguineus may maintain and serve as reservoirs for the Rickettsia if they are not exposed to cold temperatures. New populations of ticks might become infected with Rickettsiae when feeding on a bacteremic animal reservoir

Spatially Explicit Analyses of Anopheline Mosquitoes Indoor Resting Density: Implications for Malaria Control

Background: The question of sampling and spatial aggregation of malaria vectors is central to vector control efforts and estimates of transmission. Spatial patterns of anopheline populations are complex because mosquitoes' habitats and behaviors are strongly heterogeneous. Analyses of spatially referenced counts provide a powerful approach to delineate complex distribution patterns, and contributions of these methods in the study and control of malaria vectors must be carefully evaluated. Methodology/Principal Findings: We used correlograms, directional variograms, Local Indicators of Spatial Association (LISA) and the Spatial Analysis by Distance IndicEs (SADIE) to examine spatial patterns of Indoor Resting Densities (IRD) in two dominant malaria vectors sampled with a 565 km grid over a 2500 km(2) area in the forest domain of Cameroon. SADIE analyses revealed that the distribution of Anopheles gambiae was different from regular or random, whereas there was no evidence of spatial pattern in Anopheles funestus (Ia = 1.644, Pa0.05, respectively). Correlograms and variograms showed significant spatial autocorrelations at small distance lags, and indicated the presence of large clusters of similar values of abundance in An. gambiae while An. funestus was characterized by smaller clusters. The examination of spatial patterns at a finer spatial scale with SADIE and LISA identified several patches of higher than average IRD (hot spots) and clusters of lower than average IRD (cold spots) for the two species. Significant changes occurred in the overall spatial pattern, spatial trends and clusters when IRDs were aggregated at the house level rather than the locality level. All spatial analyses unveiled scale-dependent patterns that could not be identified by traditional aggregation indices. Conclusions/Significance: Our study illustrates the importance of spatial analyses in unraveling the complex spatial patterns of malaria vectors, and highlights the potential contributions of these methods in malaria control

Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk

BACKGROUND: In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. METHODS: ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in R(N), classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R(2), based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan™. RESULTS: The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22–67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, CI95% [47.51–60.75]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. CONCLUSION: ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters