A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa

Fatty acid composition as an indicator of purity suggests that linolenic acid content could be used as a parameter for the detection of extra/virgin olive oil fraud with 5% of soybean oil. The adulteration could also be detected by the increase of the trans-fatty acid contents with 3% of soybean oil, 2% of corn oil, and 4% of sunflower oil. The use of the ΔECN42 proved to be effective in Chemlali extra-virgin olive oil adulteration even at low levels: 1% of sunflower oil, 3% of soybean oil, and 3% of corn oil. The sterol profile is almost decisive in clarifying the adulteration of olive oils with other cheaper ones: 1% of sunflower oil could be detected by the increase of Δ7-stigmastenol and 4% of corn oil by the increase of campesterol. Linear discriminant analysis could represent a powerful tool for faster and cheaper evaluation of extra-virgin olive oil adulteration.

Refined olive, corn, soybean, and sunflower oils were used as cooking oils for deep-frying at two different temperatures, 160 and 190 °C, and for pan-frying of potatoes at 180 °C for 10 successive sessions under the usual domestic practice. Several chemical parameters were assayed during frying operations to evaluate the status of the frying oils. Refined olive oil, as frying oil, was found to be more stable than the refined seed oils. In fact, this oil has proven the greatest resistance to oxidative deterioration, and its trans-fatty acid contents and percentages of total polar compounds were found to be lower at 160 °C during deep-frying. Finally, chemometric analysis has demonstrated that the lowest deterioration of the quality of all refined oils occurred in the refined olive oil during deep-frying at 160 °C and the highest deterioration occurred in the refined sunflower oil during pan-frying at 180 °C.

Introduction The majority of autoimmune diseases such as rheumatoid arthritis (RA) and autoimmune thyroid diseases (AITDs) are characterized by a striking female predominance superimposed on a predisposing genetic background. The role of extremely skewed X-chromosome inactivation (XCI) has been questioned in the pathogenesis of several autoimmune diseases.

In crop species, most QTL (quantitative trait loci) mapping strategies use segregating populations derived from an initial cross between two lines. However, schemes including more than two parents could also be used. We propose an approach using a high-density restriction fragment length polymorphism (RFLP) map established on six F 2 populations derived from diallel crosses among four inbred lines and the phenotypic performances of two types of replicated progenies (F 3 and topcross). The QTL is supposed to be on the marker locus considered. Three linear model tests for the detection of QTL effects (T 1, T 2 and T 3) are described and their power studied for the two types of progeny. T 1 tests the global genetic effects of the QTL (additivity and dominance) and T 2 tests only additive effects assuming dominance is absent when it could exist. The models of these two tests assume that the main effects of QTL alleles are constant in different genetic backgrounds. The additive model of test T 3 considers the six F 2 populations independently, and T 3 is the equivalent of the classical mean comparison test if we neglect dominance; it uses only contrasts between the homozygote marker classes. The results show that T 2 is much more powerful than T 3. The power of T 1 and T 2 depends on the relative sizes of the additive and dominance effects, and their comparison is not easy to establish. Nevertheless, T 2 seems to be the more powerful in most situations, indicating that it is often more interesting to ignore dominance when testing for a QTL effect. For a given size of genetic effects, the power is affected by the total number of individuals genotyped in F 2 and the recombination rate between the marker locus and the putative QTL. The approach presented in this paper has some drawbacks but could be easily generalized to other sizes of diallels and different progeny types.

In this study we evaluate whether the pattern of spatial variability of the macro-epiphyte assemblages of leaves of Posidonia oceanica differed in relation to anthropogenic interference in the Gulf of Gabes (southern coast of Tunisia). A hierarchical sampling design was used to compare epiphytic assemblages at 5 m depth in terms of abundance and spatial variability at disturbed and control locations. The results indicate that the biomass and mean percentage cover decreased at locations near the point of sewage outlet in comparison to control locations. These losses were related to the distance from the source of disturbance. This study revealed that the diversity is reduced in disturbed locations by the loss of biomass and the mean percentage cover, explained by means of a multiple-stressor model which plays an important role in the macro-epiphytes' setting. It is urgent to propose the best management plans to save the remaining P. oceanica meadow in the Gulf of Gabes and its associated epiphytes.

We compared the powers of two methods for detection of quantitative trait loci (QTL) using genetic markers, in the simple case of an interval between two codominant markers and a backcross population. The first method is the interval mapping approach, based on the use of likelihood ratio tests performed in many positions within the interval considered and the second is the classical analysis of variance (ANOVA) testing only on the positions of the two markers. For both approaches we took into account the correlation between tests performed at different markers or positions in the interval. Appropriate thresholds and powers of tests were then calculated using analytical formulations. Simulations were also done to check the validity of the approximations used to calculate the power of the interval mapping test. Results show that the interval mapping test is slightly more powerful (about 5%) than ANOVA for small intervals (less than 20 cM) and that, for quite large effects of the QTL, the advantage of interval mapping increases as the distance between markers increases. It is more than 30% for intervals of about 70 cM.

Thirty-four durum wheat cultivars representing the Tunisian durum (Triticum durum Desf.) wheat collection and seven wild species of wheat relatives (Triticum turgidum L., T. dicoccon Schrank., T. dicoccoides (Kö rn) Schweinf., T. araraticum Jakubz., T. monococcum L., Aegilops geniculata Roth, and Aegilops ventricosa Tausch) were analysed with amplified fragment length polymorphism (AFLP) and microsatellite (SSR) markers. Both marker systems used were able to differentiate durum wheat cultivars from the wild relatives and to specifically fingerprint each of the genotypes studied. However, the two marker systems differed in the amount of detected polymorphisms. The 15 SSR markers were highly polymorphic across all the genotypes. The total number of amplified fragments was 156 and the number of alleles per locus ranged from 3 to 24 with an average of 10.4. Two SSR markers alone, Xwms47 and Xwms268, were sufficient to distinguish all 34 durum wheat genotypes. The five AFLP primer pair combinations analysed yielded a total of 293 bands, of which 31% were polymorphic. The highest polymorphic information content (PIC) value was observed for SSRs (0.68) while the highest marker index (MI) value was for AFLPs (7.16) reflecting the hypervariability of the first and the distinctive nature of the second system. For durum wheat cultivars, the genetic similarity values varied between 31.3 and 81% for AFLPs (with an average of 54.2%), and between 3.6 and 72.7% for SSRs (with an average of 19.9%). The rank correlation between the two marker systems was moderate, with r ¼ 0.57, but highly significant. Based on SSR markers, highest genetic similarity (GS) values were observed within the modern cultivars (37.3%), while the old cultivars showed a low level of GS (19.9%). Moreover, the modern cultivars showed low PIC and MI values. UPGMA Cluster analysis based on the combined AFLP and SSR data separated the wild wheat species from the durum wheat cultivars. The modern cultivars were separated from the old cultivars and form a distinct group.