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Introduction: The assessment of Volatile Organic Compounds (VOCs) in exhaled breath or sweat represents a potential non-invasive and rapid diagnostic tool for respiratory diseases., Objective: To determine if trained dogs can reliably identify the odour associated with COVID19., Methods: This is a monocentric prospective study carried out in the Emergency Department (ED) of a university hospital fromJulyto November 2021.Axillary sweat samples from all patients were collected bytwo trained health care professionals. The samples were collected in the form of sterile gauze swabs placed under the armpits for at least 4 h for each patient.Then, Tubes wereshiftedto the double-blind dog training centre for VOC detection by two individuals., Results: Dogs were tested using a total of 129 axillary sweat samples; 69 of the 107 patients who tested positive for COVID-19 based on their odours had a positive PCR/Antigen test and 19 of the 22 patients who were tested negative for COVID-19 by the dogs had a negative PCR test. The sniffer dog infection detection method had a sensitivity of 95.83% and a specificity of 33.33%. The PPV was 64.49% and the NPVwas 86.36%. The measurement of the intensity of the connection between the two variables (disease/sign) was very strong ( Q  = 0.84). This link is statistically significant (X 2 = 19.13) with a probability p ≤ 0.001., Conclusion: Overall, the use of trained detection dogs as a screening method for SARS-CoV-2 is an interesting avenue of research that warrants further exploration and validation.

Soil salinization is a critical environmental problem in arid and semiarid regions of the world. The aim of the present study was to evaluate the effect of an algae-based biostimulant on germination and seedling vigour of durum wheat ( Triticum turgidum L. subsp. durum (Desf.) Husn.), under different saline conditions (0, 100, and 200 mM NaCl). The experiment was carried out under controlled-environment conditions. Seeds were sprayed with a solution containing a combination of fungicide and different concentrations of Codium fragile (Suringar) Hariot algae (0% w / v , 10% w / v , 20% w / v , and 30% w / v ). All experimental units were placed in a germination cabinet. The effect of the seaweed extract (SWE) on seed germination and seedling performance under salinity stress was evaluated over a period of 8 days. Coleoptile length and biomass were found to be significantly and positively affected by the application of different SWE doses as compared to the control treatment (0% algae). As for germination traits, seeds treated with SWE showed a final germination (from 82% to 88%), under severe saline conditions, significantly higher than that observed in the control treatment (61%). Our findings indicate that the appropriate dose of biostimulant can markedly improve the germination and the seedlings vigour of durum wheat seeds under saline conditions. Additional studies will be needed to understand the mechanism of action of this biostimulant and its effectiveness in longer studies under field conditions.

The olive tree ( Olea europaea L.) is the main fruit tree in most of the arid and semi-arid regions of Tunisia, which is where the problem of salinity is more pronounced. Salinity is one of the main factors that affects the productivity of olive trees, so the objective of this experiment was to study the effects of salinity on the photosynthesis, water relations, mineral status, and enzymatic activity of two cultivars of Olea europaea L., 'Chemlali' and 'Koroneiki'. The trial was conducted under controlled conditions in a greenhouse for a period of 49 days and included two treatments: T0 control and T100 (irrigation with 100 mM of NaCl solution). Under salinity stress, the photosynthesis, stomatal conductance, and leaves of both cultivars were negatively affected. 'Chemlali' showed greater tolerance to NaCl salinity, based on a progressive decrease in osmotic potential (Ψπ) followed by a progressive and synchronous decrease in gs, without a comparable decrease in photosynthesis. The water use efficiency (WUE) improved as a result. In addition, the K + /Na + ratio in 'Chemlali' rose. This appears to be crucial for managing stress. Conversely, enzymatic activity showed an accumulation of glutathione peroxidase (GPX) in stressed plants. The catalase (CAT) and ascorbate peroxidase (APX) content decreased in both stressed varieties. It can be concluded that the cultivar 'Koroneiki' is more susceptible to salt stress than the cultivar 'Chemlali', because the accumulation of GPX and the decreases in CAT and APX were more pronounced in this cultivar.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R 2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R 2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R 2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R 2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)