Detecting malaria sporozoites in live, field-collected mosquitoes

A method is described for identifying malaria-infected mosquitoes, without killing them or hampering their fitness. Individual mosquitoes were induced to salivate on coverslips, and sporozoites, deposited on the glass surface, were visualized by Giemsa staining. Of 21 mosquitoes found to contain sporozoites by salivary gland dissection, 13 had delivered sporozoites on coverslips. A positive correlation was found between the amount of saliva expelled and ejection of sporozoites, indicating that the sensitivity of the method may be increased by improving the probing behaviour of the mosquitoes. The procedure described may be suitable for selecting infected mosquitoes which are able to eject sporozoites during probing. Being applicable to wild Anopheles and to large numbers of mosquitoes, the method lends itself for use in field studies on malari

Detecting malaria sporozoites in live, field-collected mosquitoes

A method is described for identifying malaria-infected mosquitoes, without killing them or hampering their fitness. Individual mosquitoes were induced to salivate on coverslips, and sporozoites, deposited on the glass surface, were visualized by Giemsa staining. Of 21 mosquitoes found to contain sporozoites by salivary gland dissection, 13 had delivered sporozoites on coverslips. A positive correlation was found between the amount of saliva expelled and ejection of sporozoites, indicating that the sensitivity of the method may be increased by improving the probing behaviour of the mosquitoes. The procedure described may be suitable for selecting infected mosquitoes which are able to eject sporozoites during probing. Being applicable to wild Anopheles and to large numbers of mosquitoes, the method lends itself for use in field studies on malari

Hydrogeological behaviour and geochemical features of waters in evaporite-bearing low-permeability successions: A case study in Southern Sicily, Italy

Knowledge about the hydrogeological behaviour of heterogeneous low-permeability media is an important tool when designing anthropogenic works (e.g., landfills) that could potentially have negative impacts on the environment and on people’s health. The knowledge about the biogeochemical processes in these media could prevent “false positives” when studying groundwater quality and possible contamination caused by anthropogenic activities. In this research, we firstly refined knowledge about the groundwater flow field at a representative site where the groundwater flows within an evaporite-bearing low-permeability succession. Hydraulic measurements and tritium analyses demonstrated the coexistence of relatively brief to very prolonged groundwater pathways. The groundwater is recharged by local precipitation, as demonstrated by stable isotopes investigations. However, relatively deep groundwater is clearly linked to very high tritium content rainwater precipitated during the 1950s and 1960s. The deuterium content of some groundwater samples showed unusual values, explained by the interactions between the groundwater and certain gases (H2S and CH4), the presences of which are linked to sulfate-reducing bacteria and methanogenic archaea detected within the saturated medium through biomolecular investigations in the shallow organic reach clayey deposits. In a wider, methodological context, the present study demonstrates that interdisciplinary approaches provide better knowledge about the behaviour of heterogeneous low-permeability media and the meaning of each data type

Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides

A multi-scale regional landslide susceptibility assessment approach: the SUFRA_SICILIA (SUscettibilit\ue0 da FRAna in Sicilia) project

The SUFRA project is based on a three level susceptibility mapping. According to the availability of more detailed data, the three scale for susceptibility mapping are increased respect to the ones suggested by the TIER group to 1:100,000, 1:50,000 and 1:25,000/1:10,000. The mapping levels exploit climatic, soil use (CORINE2009) and seismic informative layers, differentiating in the details of the core data (geology and topography), in the quality and resolution of the landslide inventory and in the modelling approach (Tab. 1). SUFRA_100 is based on a heuristic approach which is applied by processing a geologic layer (produced by ARTA integrating pre-CARG 1:100,000 geologic maps); the DEM exploited are IGMI 250m and the mapping units are 1km side square cells. Models are validated with respect to the PAI LIPs (Landslide Identification Points) which are reclassified adopting a simplified scheme. Output cuts of SUFRA100 will be referred to administrative boundaries (provinces). SUFRA50 is based on statistical analysis of new CARG geologic maps and 20m (ITA2000) - 2m (ATA2007) DEM. The mapping units are 500m and 50m cells, hydrographic and hydro-morphometric units. The landslide inventory is the IFFI2012_LIPs (first level) which is the result of the conversion in IFFI format of the PAI archive, which will be supported by remote landslide mapping (exploiting the ATA2007 aerial photos), according to the IFFI first level approach. Validation of the models will be performed exploiting both random spatial partition and temporal partition methods. Output cuts of SUFRA50 will be based on physiographic (basin) and administrative (municipalities) boundaries. SUFRA10/25 is based on statistical analysis of new CARG geologic maps (remotely and field adapted) and 2m (ATA2007) DEM. The mapping units are the slope units (SLUs) which are derived by further partitioning the hydro-morphometric units so to obtain closed morphodynamic units. The landslide inventories is the IFFI2012 which is the results of a field supported (on focus) landslide remote systematic mapping, according to the IFFI full level approach. Examples of SUFRA_100, SUFRA_50 and SUFRA_10 are presented for some representative key sector of Sicily. First results attest for the feasibility and goodness of the proposed protocol. The SUFRA program aims at enabling the regional governmental administration to cope with landslide prevision, which is the required operational concept in land management and planning. PAI has been a great advance with respect to the \u201cpre-SARNO\u201d conditions, but it is very exposed to fail: it is a blind approach for new activations; it is critically dependent on the quality of the landslide inventories; it cannot project the susceptibility outside the landslide area

Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017

Landslides are among the most dangerous natural processes. Debris avalanches and debris flows in particular have often caused casualties and severe damage to infrastructures in a wide range of environments. The assessment of susceptibility to these phenomena may help policy makers in mitigating the associated risk and thus it has attracted special attention in the last decades. In this experiment, we assessed susceptibility to debris-avalanche and -flow landslides by using a stochastic approach. Two different modeling techniques were employed: i) Multivariate Adaptive Regression Splines (MARS) and ii) Logistic Regression (LR). Both MARS and LR allow for calculating the probability of landslide occurrence by building statistical relationships between a set of environmental variables and the target variable, i.e. presence/absence of the landslide event. The target variable was extracted from an inventory of debris-avalanche and - flow landslides which were triggered by the tropical storm that hit the area of Mocoa (Colombia) on 1 April 2017. As predictor variables, we employed nine terrain attributes derived from a 5-m resolution DEM (i.e. elevation, slope angle, northness, eastness, upslope slope angle, convergence index, topographic position index, valley depth and topographic wetness index), in addition to lithology, distance from faults and presence/absence of soil creep processes. In our experiment, we used three different landslide datasets which contain i) the highest point of each recognized landslide crown-lines (dataset LIP), ii) the highest 10% of cells of each landslide area (dataset SOURCE), and iii) the entire landslide areas, which include initiation and accumulation zones (dataset MASS). In order to evaluate their predictive ability, LR and MARS models were submitted to k-fold spatial cross-validation strategy, which consists in extracting random training and test subsets from k spatially disjoint sub-areas. The results of model validation, expressed in terms of Area Under the ROC Curve (AUC), demonstrate better predictive performance of MARS models with respect to LR models, for all the three landslide datasets. The mean AUC values calculated for the datasets LIP, SOURCE and MASS of the MARS models are 0.776, 0.788 and 0.768, respectively, whereas AUC values of the LR models are 0.748, 0.751 and 0.703, respectively. Model validation also shows that the predictive skill of the models is better when landslide data are sampled from the highest portions of the landslides (dataset SOURCE). Maps of susceptibility to debris-avalanche and -flow landslides for the Mocoa area were produced by using both LR and MARS and the three landslide datasets. The analysis of the distribution of events versus the susceptibility classes of the maps confirm that MARS and the dataset SOURCE provide the best ability to discriminate between event and non-event cells

Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017

Landslides are among the most dangerous natural processes. Debris avalanches and debris flows in particular have often caused casualties and severe damage to infrastructures in a wide range of environments. The assessment of susceptibility to these phenomena may help policy makers in mitigating the associated risk and thus it has attracted special attention in the last decades. In this experiment, we assessed susceptibility to debris-avalanche and -flow landslides by using a stochastic approach. Two different modeling techniques were employed: i) Multivariate Adaptive Regression Splines (MARS) and ii) Logistic Regression (LR). Both MARS and LR allow for calculating the probability of landslide occurrence by building statistical relationships between a set of environmental variables and the target variable, i.e. presence/absence of the landslide event. The target variable was extracted from an inventory of debris-avalanche and - flow landslides which were triggered by the tropical storm that hit the area of Mocoa (Colombia) on 1 April 2017. As predictor variables, we employed nine terrain attributes derived from a 5-m resolution DEM (i.e. elevation, slope angle, northness, eastness, upslope slope angle, convergence index, topographic position index, valley depth and topographic wetness index), in addition to lithology, distance from faults and presence/absence of soil creep processes. In our experiment, we used three different landslide datasets which contain i) the highest point of each recognized landslide crown-lines (dataset LIP), ii) the highest 10% of cells of each landslide area (dataset SOURCE), and iii) the entire landslide areas, which include initiation and accumulation zones (dataset MASS). In order to evaluate their predictive ability, LR and MARS models were submitted to k-fold spatial cross-validation strategy, which consists in extracting random training and test subsets from k spatially disjoint sub-areas. The results of model validation, expressed in terms of Area Under the ROC Curve (AUC), demonstrate better predictive performance of MARS models with respect to LR models, for all the three landslide datasets. The mean AUC values calculated for the datasets LIP, SOURCE and MASS of the MARS models are 0.776, 0.788 and 0.768, respectively, whereas AUC values of the LR models are 0.748, 0.751 and 0.703, respectively. Model validation also shows that the predictive skill of the models is better when landslide data are sampled from the highest portions of the landslides (dataset SOURCE). Maps of susceptibility to debris-avalanche and -flow landslides for the Mocoa area were produced by using both LR and MARS and the three landslide datasets. The analysis of the distribution of events versus the susceptibility classes of the maps confirm that MARS and the dataset SOURCE provide the best ability to discriminate between event and non-event cells

Permethrin-impregnated curtains in malaria control

The impact of permethrin-impregnated curtains on the incidence of malaria episodes, parasitaemia and splenomegaly was assessed during a 22 month period in 2 groups of children aged 0.5-6 years. One group lived in houses where permethrin-impregnated curtains had been installed, the other group lived in houses without curtains. A significant reduction of incidence of malaria episodes, mean parasite density, parasite prevalence and splenomegaly was consistently observed in the intervention group towards the end of the period of moderate transmission, whereas no clear-cut impact could be demonstrated during the high transmission period. The influence of malaria pressure and community utilization on the protective efficiency of curtains is discussed. Because of their acceptability and the ease of reimpregnation, curtains proved to be a suitable technique for integration into primary health care

Geomorphological setting of Madonie Geopark (Italy)

The Madonie Natural Park is characterized by relevant zoological and botanic aspects and by geological features so remarkable that since 2001 it has been incorporated into the European Geoparks Network. The Park is marked by a wide massif know as Madonie Mountains. In this area, segment of the Maghrebide-Apenninic chain, successions of Meso-Cenozoic lithologies and late- and post-orogenic deposits occur. The geomorphological setting is extremely varied and includes many landscapes characterising several Sicilian areas; it results from the interaction between geomorphological processes, tectonic movements and climatic changes. It is possible to identify five distinct sectors, each corresponding to a particular landscape unit marked by a typical assemblage of landforms, related to the geological and structural setting and to a distinctive geomorphological agent