Cap Bon Region Research Articles

Combined impacts of climate and land use scenarios on sediment loads in small agricultural watershed in Tunisia

This study investigates the impact of conservation practices, specifically mulching and terracing, on soil erosion in the Kamech watershed (2.6 km²) situated in the Cap Bon region of northeastern Tunisia, under both current and future climate scenarios. Utilizing the Soil and Water Assessment Tool (SWAT) model, the research evaluates the effectiveness of these Best Management Practices (BMPs) in reducing soil loss, revealing significant reductions of 1.5 t ha⁻¹ yr⁻¹ for mulching and 2 t ha⁻¹ yr⁻¹ for terracing. The findings indicate that these practices play a crucial role in enhancing soil conservation amidst climate change challenges, including altered precipitation patterns and increased temperatures. Additionally, the study highlights a notable decline in average monthly rainfall projected for the period 2051 to 2100 under the RCP8.5 scenario, further emphasizing the importance of implementing effective soil management strategies. This paper indicates that both methods, mulching and terracing, significantly contribute to reducing soil erosion under future scenarios (2051-2100). Their implementation proves essential for effective soil conservation, helping to mitigate the impacts of climate change and maintain soil health in the Kamech watershed. The results contribute to a better understanding of BMP performance in addressing soil erosion, water quality, and hydrological responses in the face of evolving environmental conditions. By enhancing our understanding of these interactions, this research aims to provide valuable insights for sustainable watershed management, particularly in regions prone to water scarcity and land degradation.

Setting up an inter-laboratory control in hematology in the region of capbon

Setting up an inter-laboratory control in hematology in the region of capbon

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia

As a semi-arid Mediterranean country, Tunisia is affected by the impacts of climate change, particularly the coastal regions like the Cap-Bon. Irregular rainfall, rising temperatures and the recurrence of extreme events are all indicators that affect ecosystems and populations and make them more vulnerable to the influence of climatic variables. Therefore, an analysis of the trends of climate variables can contribute to facilitating the development of effective adaptation strategies. In this matter, this study was conducted to assess the homogeneity and trends of minimum and maximum air temperature (Tmin and Tmax) and precipitation (P) in the Cap-Bon region. Daily data were collected from the meteorological station of Nabeul for the period of 1982–2020. Pettitt and SNHT tests for homogeneity were applied to identify the breakpoints in multi-time scales of Tmax, Tmin and P data series. The Mann–Kendall (MK) test was used to detect the change in the time-series trend. A modified Mann–Kendall (mMK) test was used to remove the autocorrelation effect from the data series. Both the MK and mMK tests were used at the 5% significant level. The magnitude of the climatic trend was estimated using the non-parametric Sen’s slope estimator. Contrary to Tmin and P, the results of the homogeneity tests revealed the existence of significant breakpoints in the annual, seasonal and monthly Tmax time series. For most cases, the breakpoint occurred around the year 2000. For Tmin, significant breakpoints were recorded in March and April, while a significant shift in the P time series was detected in December. The Mann–Kendall results show a significant warming trend in annual Tmax, with magnitudes equal to 0.065 and 0.045 °C/year before and after the breakpoint, respectively. Nevertheless, non-significant tendencies were observed in the annual Tmin and P time series. On the monthly time scale, Tmax exhibited a significant upward trend in June and August, before the observed breakpoints, with Sen’s slope values equal to 0.065 and 0.045 °C/year, respectively. Regarding the Tmin data, a significant positive trend was observed in July at a rate of 0.033 °C/year.

Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia

In the context of a changing climate, monitoring agricultural systems is becoming increasingly important. Remote sensing products provide essential information for the crop classification application, which is used to produce thematic maps. High-resolution and regional-scale maps of agricultural land are required to develop better adapted future strategies. Nevertheless, the performance of crop classification using large spatio-temporal data remains challenging due to the difficulties in handling huge amounts of input data (different spatial and temporal resolutions). This paper proposes an innovative approach of remote sensing data management that was used to prepare the input data for the crop classification application. This classification was carried out in the Cap Bon region, Tunisia, to classify citrus groves among two other crop classes (olive groves and open field) using multi-temporal remote sensing data from Sentinel- 1 and Sentinel-2 satellite platforms. Thus, we described the new QGIS plugin “Model Management Tool (MMT)”. This plugin was designed to manage large Earth observation (EO) data. This tool is based on the combination of two concepts: (i) the local nested grid (LNG) called Tuplekeys and (ii) Datacubes. Tuplekeys or special spatial regions were created within a LNG to allow a proper integration between the data of both sensors. The Datacubes concept allows to provide an arranged array of time-series multi-dimensional stacks (space, time and data) of gridded data. Two different classification processes were performed based on the selection of the input feature (the obtained time-series as input data: NDVI and NDVI + VV + VH) and on the most accurate algorithm for each scenario (22 tested classifiers). The obtained results revealed that the best classification performance and highest accuracy were obtained with the scenario using only optical-based information (NDVI), with an overall accuracy OA = 0.76. This result was obtained by support vector machine (SVM). As for the scenario relying on the combination of optical and SAR data (NDVI + VV + VH), it presented an OA = 0.58. Our results demonstrate the usefulness of the new data management tool in organizing the input classification data. Additionally, our results highlight the importance of optical data to provide acceptable classification performance especially for a complex landscape such as that of the Cap Bon. The information obtained from this work will allow the estimation of the water requirements of citrus orchards and the improvement of irrigation scheduling methodologies. Likewise, many future methodologies will certainly rely on the combination of Tuplekeys and Datacubes concepts which have been tested within the MMT tool.

Integrated decisional approach for watershed vulnerability prioritization using water and soil hazard index (WSHI) and AHP methods: Chiba watershed, Cap-Bon region, northeast Tunisia

Integrated decisional approach for watershed vulnerability prioritization using water and soil hazard index (WSHI) and AHP methods: Chiba watershed, Cap-Bon region, northeast Tunisia

Check list of piercing-sucking insects in North-eastern Tunisia in the region of Cap-Bon and their host plants

The study of the biodiversity of piercing-sucking insects on various crops and weeds in North-eastern Tunisia in the region of Cap-Bon, permitted to identify several pest species. In the different investigated regions and sampled crops, aphids were the most dominant and abundant compared to the others. Twelve aphid species were identified; Aphis gossypii, A. craccivora, A. spiraecola, A. punicae, A. fabae, A. illinoisensis, Toxoptera aurantii, Myzus persicae, Sitobion avenae, Pterocholorides persicae, Rhopalosiphum maidis and Schizaphis graminum. Regarding thrips, seven species were identified; Thrips angusticeps, T. tabaci, Bregmatothrips dimorphus, Anaphothrips sudanensis, Limothrips cerealium, Liothrips oleae and Frankliniella occidentalis, which was the most polyphagous thrips species. Five scale-insect species were recorded in the different investigated locations; Parlatoria ziziphi, Aonidiella aurantii, Icerya purchasi, S. oleae, and Planococcus citri. One species of Psyllidae was registered: Euphyllura olivina in all visited olive stations. One leafhopper species Empoasca vitis in grapevine orchards and finally two species of whiteflies that were reported; Dialeurodes citri in citrus orchard and Bemisia tabaci on pepper crops.

Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil clay content and mapping coverage

Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) remote sensing imagery is a useful tool for topsoil property mapping, but limited to bare soils pixels. With the increasing amount of freely available VNIR/SWIR satellite imagery (e.g. Landsat TM, ETM+, OLI and Sentinel-2A/B), extensive time series data can be exploited to increase the spatial coverage of bare soil derived information. The objective of this study was to evaluate the benefits of using a bare soil image created from the mean spectral reflectance from bare soil pixels along a time series, compared to a single-date image. The benefits were analyzed in term of (i) proportion of soil mapping and (ii) accuracy of clay content prediction. The study was conducted over the Cap-Bon region (Northern Tunisia) which is a pedologically contrasted and cultivated area. To this end, 262 topsoil samples and three Landsat-TM images acquired during the summer season were used. Multiple linear regression (MLR) models based on the multi-date and single-date Landsat-derived spectral dataset were performed to quantify clay soil content. Our results have shown that (1) a bare soil image created from only mean spectral reflectance from common bare soil pixels along a time series provided the best accuracy of clay content prediction (i.e., coefficient of determination of validation Rval2 of 0.75, a root mean square error of prediction (RMSEP) of 88 g/kg) with a moderate bare soil coverage (i.e., 23% of the study area); (2) a bare soil image created from a mix of mean spectral reflectance from common bare soil pixels along a time series and of spectral reflectance from bare soil pixels of single-date images provided acceptable accuracy of clay content prediction (i.e., Rval2 = 0.64, RMSEP = 109 g/kg) with a relatively high bare soil coverage (i.e., 44% of the study area); and (3) all the bare soil images provided similar spatial structures of the clay content predictions. With the actual availability of the VNIR/SWIR satellite imagery for the entire globe, this study offer a simple and accurate method for delivering accurate soil property maps over large areas, to the geoscience community.

Rabies in Tunisia: A spatio-temporal analysis in the region of CapBon-Nabeul

Human rabies is a significant public health concern in Tunisia. However, the spatiotemporal spread pattern of rabies in dogs, the major reservoir and vector, and its determinants are poorly understood. We collected geographic locations and timeline of reported animal rabies cases in the region of CapBon (study area), for the years 2015–2019 and integrated them in Geographical Information System (GIS) approach to explore the spatio-temporal pattern of dog rabies. The results show that roads and irrigated areas can act as ecological corridors to viral spread. Our study showed that there was a significant seasonal variation in the number of cases of rabies recorded, with a strong peak in spring and lower peak in winter and summer. These findings may play a role in updating and directing public health policy, as well as providing opportunities for authorities to explore control options in time and space. A better knowledge of the ecology and dog population dynamics is still necessary and important to achieve an effective rabies control.

Analysing the impact of soil spatial sampling on the performances of Digital Soil Mapping models and their evaluation: A numerical experiment on Quantile Random Forest using clay contents obtained from Vis-NIR-SWIR hyperspectral imagery

It has long been acknowledged that the soil spatial samplings used as inputs to DSM models are strong drivers – and often limiting factors – of the performances of such models. However, few studies have focused on evaluating this impact and identifying the related spatial sampling characteristics. In this study, a numerical experiment was conducted on this topic using the pseudo values of topsoil clay content obtained from an airborne Visible Near InfraRed-Short Wave InfraRed (Vis-NIR-SWIR) hyperspectral image in the Cap Bon region (Tunisia) as the source of the spatial sampling.Twelve thousand DSM models were built by running a Random Forest algorithm from soil spatial sampling of different sizes and average spacings (from 200 m to 2000 m) and different spatial distributions (from clustered to regularly distributed), aiming to mimic the various situations encountered when handling legacy data. These DSM models were evaluated with regard to both their prediction performances and their ability to estimate their overall and local uncertainties. Three evaluation methods were applied: a model-based one, a classical model-free one using 25% of the sites removed from the initial soil data, and a reference one using a set of 100,000 independent sites selected by stratified random sampling over the entire region.The results showed that: 1) While, as expected, the performances of the DSM models increased when the spacing of the sample increased, this increase was diminished for the smallest spacing as soon as 50% of the spatially structured variance was captured by the sampling, 2) Sampling that provided complete and even distributions in the geographical space and had as great spread of the target soil property as possible increased the DSM performances, while complete and even sampling distributions in the covariate space had less impacts, 3) Systematic underestimations of the overall uncertainty of DSM models were observed, that were all the more important that the sparse samplings poorly covered the real distribution of the target soil property and that the dense sampling were unevenly distributed in the geographical space, 4) The local uncertainties were underestimated for sparse sampling and over-estimated for dense sampling while being sensitive to the same sampling characteristics as overall uncertainty.Such finding have practical outcomes on sampling strategies and DSM model evaluation that are discussed.

Genetic Characterization of Tunisian Lime Genotypes Using Pomological Traits

Abstract Citrus genus includes a wide number of species that have been long cultivated and well adapted in Tunisia. It is represented by small number of plantations and considered as underutilized in Tunisia. Our goal was to genetically characterize Tunisian lime genotypes to obtain data useful for gene conservation and breeding purposes. The survey of genotypes was conducted in the Cap Bon region, where citrus cultivation is the most spread. Sixteen quantitative and 19 qualitative parameters were evaluated. The observed accessions belonged to three different species: Citrus limetta, Citrus latifolia (limes Byrsa), and Citrus limettioides (limes of Palestine) according to Tanaka classification. Principal component analysis confirmed these classifications. Four-cell analysis (FCA) was used to determine the most threatened genotypes. Quantitative traits were evaluated and allowed the discrimination between genotypes. Many quantitative traits of fruit and juice were highly positively and significantly correlated. Phenotypic diversity was determined using Shannon–Wiener diversity index (H’). The highest value of diversity index was observed for both vesicle thickness and thickness of segment walls (H’ = 0.98). Intermediate values were observed for both fruit axis (H’= 0.49) and pulp firmness (H’ = 0.43). However, fruit shape (H’ = 0.24), shape of fruit apex (H’ = 0.24), and vesicle length (H’ = 0.33) presented the lowest values of diversity index. Current findings will be useful to conserve threatened genotypes ex situ and on farm and also will guide strategic conservation on Citrus genetic resources for future breeding programs.

Temporal Changes in the Aphid–Natural Enemy Complex in Tunisian Citrus over Two Decades

Aphid species and their natural enemies were identified in a citrus orchard in the Cap Bon region of Tunisia in 2014–2016. Aphids within the same orchard had previously been studied in 1991–1992, allowing a comparison of the populations over the 20+ yr. Ten aphid species were identified in 1991–1992, while only four were observed in 2014–2016. This reduction in aphid diversity could be linked to observed temperature and rainfall changes attributed to climate change, interspecific competition, increased occurrence of hyperparasitoids, or any combination of these and other factors. Aphis spiraecola Patch and Aphis gossypii Glover increased in abundance and occurrence, which is of special concern because they are known as vectors of Citrus tristeza virus. Four species of parasitoids and two of predators were identified in 2014 samples, while an increase of hyperparasitism occurred.

Occurrence of Colletotrichum fruticola along with C. gloeosporioides in causing anthracnose disease on Citrus sinensis in Tunisia

Information on the Colletotrichum species associated with Citrus anthracnose was lacking. Surveys were conducted from January 2012 to February 2013 on different citrus species and varieties orchards in the Cap-Bon region (Tunisia) to investigate the prevalence of Colletotrichum species. Typical anthracnose symptoms such as dieback and wither-tip of twigs were noticed. The fungi associated with anthracnose were isolated and studied. Morphological features (mycelial and sporulation characterization), molecular identification by using ITS1 and ITS2 primers were done along with pathogenicity test monosporic cultures. Eight Colletotrichum isolates were collected from infected fruits, leaves and twigs and were identified as Colletotrichum gloeosporioides (seven isolates) and Colletotrichum fructicola (one isolate). All eight Colletotrichum isolates were pathogenic on Thomson, Malti and Meski orange varieties, Lunari lemon variety and sweet lime Bergamot detached fruit and leaves. This investigation revealed the prevalence, for the first time of C. fructicola, on Citrus in Tunisia.

Use of AquaCrop model for estimating crop evapotranspiration and biomass production in hilly topography

The use of crop models in sloping areas is questionable when relief is not taken into account, as relief affects infiltration, radiation, and aerodynamic processes. The objective of this work is to evaluate the performance of FAO-AquaCrop model in simulating crop evapotranspiration, water balance, and biomass production in hilly areas using in situ measurements. The experiment was conducted in the Cap-Bon region, north-eastern Tunisia, on two wheat fields located on opposite sloping rims (A and B) and one control field (C) on a flat terrain: field A is SE-oriented with 5% slope and B is NW-oriented with 6% slope. Three flux stations were used to monitor automatically actual evapotranspiration (ET) and climatic factors whereas soil moisture and biomass production were measured manually. Model’s outputs were compared to actual measurements using statistical indicators: slope of the regression line, root mean squared error (RMSE), and the coefficient of determination (R2). Actual ET varied between 1 and 2 mm during crop initial stage and 3–4 mm during mid-season stage. The ET/ETo ratio during mid-season was 0.81, 0.74, and 1.03, respectively for fields A, B, and C, well below the commonly used value of 1.15. Comparison between measured and simulated ET shows a substantial overestimation of the model in sloping fields with 6–20% higher averages and a RMSE of 0.47–0.77 mm/day. AquaCrop seems to simulate reasonably well water balance, particularly in flat conditions. RMSE of water content in the top 100 cm soil-layer was in the range 41–67 mm/m, representing a relative error of 11–21%. Simulated and measured biomass values presented similar trends (R2 = 0.86–0.94) with a systematic difference, indicating that AquaCrop outputs could be improved by a correction factor.

Policy measures for reducing aquifer depletion in a context of climate change: the case of the coastal area of Cap-Bon

Groundwater resources are critically important for irrigated agriculture in Tunisia. However, excessive irrigation extractions where groundwater is slowly renewed are causing a widespread depletion of the aquifer systems, with the impacts of climate change expected to further exacerbate this problem. These circumstances call for the development of methodologies and analyses that can support the design of sustainable groundwater management policies. This paper presents a hydro-economic mathematical programming model that is used to evaluate the effects of different policy measures for reducing aquifer depletion in the Cap-Bon region of Tunisia. Three policies have been evaluated: a quota defining the maximum quantity of groundwater extractions, environmental taxation, and supply expansion with subsidized desalinated seawater. Overall, results highlight the economic and social tradeoffs among these different policy choices and the challenges facing the implementation of sustainable groundwater management in Tunisia. More specifically, results show the advantages of using subsidized desalinated seawater compared to the other two policy alternatives in terms of the value of agricultural production, farmers’ profits, and employment, despite of its sizeable budgetary burden.

Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data

ABSTRACT The potential of Visible Near-Infrared and Short-Wave Infrared (VNIR–SWIR, 400 nm–2500 nm) hyperspectral imagery for use in multivariate approaches and geostatistical techniques for mapping topsoil properties has been previously demonstrated. However, the use of VNIR–SWIR hyperspectral imagery remains costly, which limits the spatial scales over which it can be applied. This paper aims to evaluate the potential for substituting the more accessible Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) VNIR–SWIR multispectral data for hyperspectral imagery in mapping surface soil clay contents. This study used ASTER multispectral data (nine bands in the VNIR–SWIR spectral domain) acquired over the Cap-Bon region in Tunisia (2000 km2) and 262 surface soil samples collected within the ASTER scene that were subjected to laboratory analysis of the clay fraction (soil particles less than 2 µm). The approach followed two steps: i) estimation of surface soil clay contents for bare soil areas using a Multiple Linear Regression (MLR) model built from the 9 ASTER VNIR–SWIR bands and ii) spatial interpolation (co-kriging) of the soil sampling of measured points and the ASTER-estimates over the whole study area. The MLR model for estimating clay contents using ASTER multispectral data performed correctly ( = 0.60). In addition, this performance is only slightly lower than that obtained using hyperspectral imagery (specifically, an Airborne Imaging Spectrometer for Applications (AISA-DUAL) dual hyperspectral sensor) in a previous study. Moreover, the co-kriging process appeared to yield encouraging results for capturing the large range of variability of clay content values, although it was not able to represent the short scale variability ( = 0.43). Finally, the ASTER multispectral data, despite being underused in the mapping of soil properties, may open up new ways to perform more extensive mapping of surface soil properties in semi-arid contexts characterized by extensive bare and dry soil surfaces.

How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery

Digital Soil Map uncertainty is usually evaluated from a set of independent soil observations that are used to determine various uncertainty indicators. However, the number and locations of the sites that constitute these evaluations may impact the value of these indicators.In this paper, a numerical experiment on uncertainty indicators was performed using the pseudo values of topsoil clay content obtained from an airborne hyperspectral image in the Cap Bon region (Tunisia). These pseudo values form a soil pattern with a large extent (46% of 300 km2), high resolution (5 m) and good accuracy (R2val = 0.75) while being free of visible artefacts and pedologically plausible. Therefore, the dataset was considered a fair representation of reality while providing a quasi-unlimited choice of sites.The numerical experiment considered three Quantile Regression Forests as examples of DSM models by using inputs from relief soil covariates and geographical locations that were calibrated from 200, 2000 and 100,000 individuals respectively (low, medium and high quality models). Their uncertainty indicators were first evaluated by calculating four uncertainty indicators (ME, MSE, SSMSE and PICP) from a large independent validation set of 100,000 sites. These uncertainty indicators were then computed from independent evaluation sets of different sizes (from 50 to 500 sites) and from different locations (500 evaluation sets of each size). The independent evaluation sets were selected following a stratified random sampling using compact geographical strata.The numerical experiment showed that the values of the uncertainty indicators were highly variable across numbers and locations of sites. The largest variations were observed for evaluation sets with fewer than 100 sites, but non-negligible variations remained for larger evaluation datasets. This result suggested that evaluations from independent sets convey a non-negligible error on the uncertainty indicators, which increases as the number of sites decrease.Evaluations of DSM models from independent evaluation sets should be interpreted with care and uncertainty on validation results should be systematically estimated. For that, numerical experiments for benchmarking DSM models on known soil patterns across the world would be a valuable complement to the analytical expressions for the uncertainty indicators and the many DSM applications for which these analytical expressions are not valid. This would serve also to improve the sampling techniques for the calibration and evaluation datasets to reduce the error when estimating the uncertainty of a DSM product.

Validation of DIABSCORE in screening for Type 2 Diabetes and prediabetes in Tunisian population.

AimsTo perform a validation of DIABSCORE in a sample of Tunisian adults and find out the optimal cut-off point for screening of Type 2 diabetes (T2D) and prediabetes.Methods225 adults 18–75 years and a subgroup of 138 adults (18–54 years), with undiagnosed T2D from the region of Cap-Bon, Tunisia were included in the present study. The DIABSCORE was calculated based on: age, waist/height ratio, family history of T2D and gestational diabetes. Receiver operating characteristics (ROC) curves and areas under curve (AUC) were obtained. The T2D and prediabetes prevalences odds ratios (OR) between patients exposed and not exposed to DIABSCORE≥90 and DIABSCORE≥80, respectively were calculated in both age ranges.ResultsFor screening of T2D the best value was DIABSCORE = 90 with a highest sensitivity (Se), negative predictive value (NPV) and lower negative likelihood ratio in participants aged 18–75 yr (Se = 97%; NPV = 97%) when compared to participants aged 18–54 yr (Se = 95%; NPV = 97%); for prediabetes, the best Se and NPV were for DIABSCORE = 80 in both age groups, but it showed a disbalanced sensitivity-specificity. The ROC curves for T2D showed a similar AUC in both age ranges (AUC = 0.62 and AUC = 0.61 respectively). The ROC curves for prediabetes showed a highest AUC in those aged 18–54 years than the older ones (AUC = 0.62 and AUC = 0.57, respectively). The prevalences OR of T2D for DIABSCORE≥90 was higher than for DIABSCORE≥80 in both age ranges. Nevertheless, the prevalences OR of prediabetes for DIABSCORE≥90 was half of the detected for DIABSCORE≥80 in both age ranges.ConclusionThe DIABSCORE is a simple clinical tool and accurate method in screening for T2D and prediabetes in the adult Tunisian population.

Hydrochemical characterization and geospatial analysis of groundwater quality in Cap Bon region, northeastern Tunisia

The hydrogeochemical characteristics of shallow groundwater in the Grombalia region, northeastern Tunisia, were investigated to evaluate suitability for irrigation and other uses and to determine the main processes that control its chemical composition. A total of 21 groundwater samples were collected from existing wells in January–February 2015 and were analyzed for the major cations and anions concentrations. Conductivity, pH, T°, O2 and salinity were also measured. Interrelationships between chemical parameters were determined by using the scatter matrix method. The suitability of groundwater for irrigation and other uses was assessed by determining the sodium adsorption ratio, soluble-sodium percentage, total dissolved solids, total hardness, Kelly’s index and permeability index values of water samples. The spatial distribution of key parameters was assessed using a GIS-based spatial gridding technique. This analysis indicated that the chemical composition of groundwater in the study area is of Cl–SO4–Na–Ca mixed facies with concentrations of many chemical constituents exceeding known guideline values for irrigation. The salinity of groundwater is controlled by most dominant cation and anion (Na–Cl). A correlation analysis shows that Na+ is the dominant cation and that reverse ion exchange is a dominant process that controls the hydrogeochemical evolution of groundwater in the area. Geospatial mapping of hydrochemical parameters and indices analyzed with the USSL and Wilcox diagrams show distinctive areas of irrigation suitability. In contrast, 76.2% of samples fall in the highly doubtful to unsuitable category and indicate that the central and north-eastern parts of the study area are unsuitable for irrigation due to a high salinity and alkalinity.

Anatomopathological profile of breast cancer in cape bon, Tunisia

Le cancer du sein est le cancer le plus fréquent de la femme en Tunisie et dans le monde. Dans le Cap Bon tunisien, les particularités anatomopathologiques de ce cancer n'ont pas été précisées auparavant. Leur connaissance est nécessaire pour l'adaptation des systèmes de prévention et de soins dans la région. Le but de notre étude était de déterminer le profil anatomopathologique des carcinomes mammaires dans l'unique laboratoire d'anatomie pathologique publique de la région. Il s'agissait d'une étude descriptive rétrospective des carcinomes mammaires diagnostiqués chez 116 malades dans notre laboratoire sur une période de 5 ans de Juillet 2010 à Juillet 2015. Notre étude a inclus 116 patientes. L'âge moyen était de 51 ans. La taille tumorale histologique moyenne était de 31 mm. Le diagnostic initial était posé sur pièce de tumorectomie dans 83% des cas. Le carcinome infiltrant de type non spécifique était le type histologique le plus fréquent. Le grade SBR III était majoritaire. L'invasion lympho-vasculaire était présente dans 33% des cas. Le curage axillaire était positif dans 72% des cas. Les récepteurs hormonaux étaient positifs dans 73% des cas. Les récepteurs de l'Her2-Neu étaient surexprimés dans 19% des cas. Le ki67 était ≥ 14% dans 38%. Le sous-type moléculaire le plus fréquent était le luminal A. Le carcinome mammaire dans la région du Cap Bon se caractérise par sa survenue à un âge jeune, son importante taille tumorale et la fréquence de facteurs histopronostiques péjoratifs.