Quantification of SO2 effects on physiological processes, plant growth and crop production

SO 2 may cause damage on crops and vegetation. This thesis aimes to explain the impact of SO 2 on plant growth and crop production on basis of a quantitative analysis of SO 2 effects on physiological processes. Photosynthesis of leaves was found to be depressed at high radiation levels, by competition between SO 2 and CO 2 for the carboxylating enzyme. A model for the flux of SO 2 into the leaf and effects of its metabolites on photosynthesis was developed and used to estimate values for model parameters at the biochemical level from data on the effect of SO 2 on photosynthesis. Differences in sensitivity between species and cultivars appeared to be largely based on variation in the rate of sulphite oxidation. The model was used to analyse the mechanism behind temperature effects on photosynthetic sensitivity to SO 2 . The submodel for SO 2 effects at the leaf level was coupled to a model for photosynthesis for leaf canopies. The effects Of SO 2 on canopy photosynthesis were simulated accurately.SO 2 effects on growth and production of broad bean (Vicia faba L.) crops were studied using an open-air exposure system. Yield was depressed by 9-17% at SO 2 concentrations ranging from 74-165 μg SO 2 m -3. Chronic injury, leaf damage in the older leaves after long exposures, caused substantial reductions in leaf area at the end of the growing period.The mechanism behind the observed depression in crop yield was analysed by mechanistic simulation models for crop growth, extended with the submodels for SO 2 effects on leaf photosynthesis. Direct effects of SO 2 on photosynthesis explained about 10% of the observed yield reduction. An increased rate of maintenance respiration, observed in field exposed leaves, explained an extra 10% of the observed yield reduction. The major part was explained by chronic leaf injury at the end of the growing period.Because chronic injury may be related to a disturbance of intracellular pH regulation, a conceptual model was proposed for regulation of intracellular pH in relation to uptake and assimilation of nutrients and uptake of N and S containing air pollutants by the shoots of plants.The results of this study are discussed in view of the development of mechanistic models for estimation of the impact of air pollutants on crops, forests and (semi-) natural vegetation

Optimal online and offline algorithms for robot-assisted restoration of barrier coverage

Cooperation between mobile robots and wireless sensor networks is a line of research that is currently attracting a lot of attention. In this context, we study the following problem of barrier coverage by stationary wireless sensors that are assisted by a mobile robot with the capacity to move sensors. Assume that $n$ sensors are initially arbitrarily distributed on a line segment barrier. Each sensor is said to cover the portion of the barrier that intersects with its sensing area. Owing to incorrect initial position, or the death of some of the sensors, the barrier is not completely covered by the sensors. We employ a mobile robot to move the sensors to final positions on the barrier such that barrier coverage is guaranteed. We seek algorithms that minimize the length of the robot's trajectory, since this allows the restoration of barrier coverage as soon as possible. We give an optimal linear-time offline algorithm that gives a minimum-length trajectory for a robot that starts at one end of the barrier and achieves the restoration of barrier coverage. We also study two different online models: one in which the online robot does not know the length of the barrier in advance, and the other in which the online robot knows the length of the barrier. For the case when the online robot does not know the length of the barrier, we prove a tight bound of $3/2$ on the competitive ratio, and we give a tight lower bound of $5/4$ on the competitive ratio in the other case. Thus for each case we give an optimal online algorithm.Comment: 20 page

Epitope-specific humoral responses to human cytomegalovirus glycoprotein-B vaccine with MF59 adjuvant in seropositive solid organ transplant recipients: anti-AD2 levels correlate with protection from viraemia

The human cytomegalovirus (HCMV) virion envelope protein glycoprotein B (gB) is essential for viral entry and represents a major target for humoral responses following infection. Previously, a phase-2 placebo-controlled clinical trial conducted in solid organ transplant candidates demonstrated that vaccination with gB plus MF59 adjuvant significantly increased gB ELISA antibody levels whose titer correlated directly with protection against post-transplant viremia. The aim of the current study was to investigate in more detail this protective humoral response in vaccinated seropositive transplant recipients. We focussed on four key antigenic domains (AD) of gB; AD1, AD2, AD4 and AD5 measuring antibody levels in patient sera and correlating these with post-transplant HCMV viremia. Vaccination of seropositive patients significantly boosted pre-existing antibody levels against the immunodominant region AD1 as well as against AD2, AD4 and AD5. A decreased incidence of viremia correlated with higher antibody titers against AD2 but not with antibody titers against the other three ADs. Overall, these data support the hypothesis that antibodies against AD2 are a major component of the immune protection of seropositives seen following vaccination with gB/MF59 vaccine and identify a correlate of protective immunity in allograft patients

Population genetics of a lethally managed medium-sized predator

Globally, levels of human–wildlife conflict are increasing as a direct consequence of the expansion of people into natural areas resulting in competition with wildlife for food and other resources. By being forced into increasingly smaller pockets of suitable habitat, many animal species are at risk of becoming susceptible to loss of genetic diversity, inbreeding depression and the associated inability to adapt to environmental changes. Predators are often lethally controlled due to their threat to livestock. Predators such as jackals (black backed, golden and side striped; Canis mesomelas, C. aureus and C. adustus, respectively), red foxes (Vulpes vulpes) and coyotes (C. latrans) are highly adaptable and may respond to ongoing persecution through compensatory reproduction such as reproducing at a younger age, producing larger litters and/or compensatory immigration including dispersal into vacant territories. Despite decades of lethal management, jackals are problematic predators of livestock in South Africa and, although considered a temporary measure, culling of jackals is still common. Culling may affect social groups, kinship structure, reproductive strategies and sex-biased dispersal in this species. Here, we investigated genetic structure, variation and relatedness of 178 culled jackals on private small-livestock farms in the central Karoo of South Africa using 13 microsatellites. Genetic variation was moderate to high and was similar per year and per farm. An absence of genetic differentiation was observed based on STRUCTURE, principal component analysis and AMOVA. Relatedness was significantly higher within farms (r = 0.189) than between farms (r = 0.077), a result corroborated by spatial autocorrelation analysis. We documented 18 occurrences of dispersal events where full siblings were detected on different farms (range: 0.78–42.93 km). Distance between identified parent–offspring varied from 0 to 36.49 km. No evidence for sex-biased dispersal was found. Our results suggest that in response to ongoing lethal management, this population is most likely able to maintain genetic diversity through physiological and behavioural compensation mechanisms.APPENDIX S1. Supplementary methods.SUPPLEMENTARY TABLES. TABLE S1. Primer details for microsatellite loci used to genotype black-backed jackals (Canis Mesomelas). TABLE S2. Per-locus summary statistics as calculated in Cervus v3.0.7. The non-exclusion probabilities and combined non-exclusion probabilities (final row, italics) are relevant indicators of the power of the loci for parentage and sibship analyses. TABLE S3. Summary statistics for 20 sampling localities (farms) with >1 sample and for all farms pooled. Produced using the basicStats command of the diveRsity package v1.9.90 in R v3.6.2 and RStudio v1.2.5033. Standard deviation was calculated across loci in Microsoft Excel (stdev.s). Sampling localities with only one sample are not shown. TABLE S4. Summary statistics per year and for all years pooled. Produced using the basicStats command of the diveRsity package v1.9.90 in R v3.6.2 and RStudio v1.2.5033. Standard deviation was calculated across loci in Microsoft Excel (STDEV.S). TABLE S5. Pairwise FST values between farms with the full dataset (below diagonal) and associated significance at a level of 0.05 (above diagonal), where significant values are indicated by a “+” and non-significant values by a “−”. Calculated in Arlequin 3.5.2.2. TABLE S6. Pairwise FST values between farms with relatives removed (below diagonal) and associated significance at a level of 0.05 (above diagonal), where significant values are indicated by a “+” and non-significant values by a “−”. Calculated in Arlequin 3.5.2.2. TABLE S7. Comparison of mean pairwise relatedness (r) between years and mean individual inbreeding coefficients (F) between years. P-values for the Wilcoxon tests for difference in means are shown on the inside of the table (bordered by grey), with P-values for inbreeding comparisons shown below the diagonal (bottom left) and P-values for relatedness comparisons shown above the diagonal (top right). The mean F for each year is shown in the left-most column “outside” the main table, with the mean r for each year shown in the top row “outside” the main table. The numbers in parentheses after each year are the number of observations/data points for that year (number of samples for F and number of pairwise relatedness comparisons for r).SUPPLEMENTARY FIGURES. FIGURE S1. STRUCTURE HARVESTER results for (a) Delta K values and (b) probability (-LnPr) of K = 1–27 averaged over 20 runs and (c) genetic differentiation between the jackal sample locations (farms) based on STRUCTURE analysis (performed with K = 2–6) of 1 = GV, 2 = BB, 3 = BR, 4 = BD, 5 = DS, 6 = GG, 7 = HK, 8 = KD, 9 = KW, 10 = KK, 11 = KT, 12 = NG, 13 = ND, 14 = OG, 15 = RV, 16 = RE, 17 = RT, 18 = RD, 19 = SG, 20 = SK, 21 = VR, 22 = WK, 23 = CL, 24 = KR, 25 = WB and 26 = TD. FIGURE S2. STRUCTURE HARVESTER results for (a) Delta K values and (b) probability (-LnPr) of K = 1–27 averaged over 20 runs and (c) genetic differentiation between the jackal sample locations (farms) based on STRUCTURE analysis (performed with K = 2–6 and K = 14) of 1 = GV, 2 = BB, 3 = BD, 4 = DS, 5 = GG, 6 = HK, 7 = KW, 8 = KT, 9 = NG, 10 = ND, 11 = OG, 12 = RV, 13 = RE, 14 = RD, 15 = SG, 16 = SK, 17 = VR, 18 = WK and 19 = CL. After removing relatives, some localities had no samples, hence fewer sampling localities as compared to the full dataset. Note: The Evanno method (DeltaK) does not evaluate K = 1. FIGURE S3. Principal component analysis (PCA) of the different jackal sampling locations (farms) with related individuals removed. FIGURE S4. Plot comparing the relatedness estimates using six estimators and simulated individuals of known relatedness. Di, Dyadic likelihood estimator “DyadML”; LL, Lynch-Li estimator; LR, Lynch and Ritland estimator; QG, Queller and Goodnight estimator; Tri, Triadic likelihood estimator “TrioML”; W, Wang estimator. Plot produced with ggplot2 3.3.0 (Wickham, 2016). FIGURE S5. Results of the spatial autocorrelation analysis for A females and B males. The blue line indicates the autocorrelation coefficient of the data, with the 95% confidence interval at each distance class indicated by the black error bars, as determined by 1000 bootstrap resampling replicates. The red dashed lines indicate the 95% confidence interval around the null hypothesis (no spatial structure, i.e. rauto = 0), as determined by permutation (999 steps). Thus, if the error bars around the blue line do not overlap with the red dashed lines in a distance class, then genotypes were more (positive rauto) or less (negative rauto) similar than expected under the null hypothesis in that distance class. Such cases are indicated with an asterisk (*).The National Zoological Gardens, Pretoria and the University of South Africa.https://zslpublications.onlinelibrary.wiley.com/journal/14697998hj2023BiochemistryGeneticsMicrobiology and Plant Patholog

Spatial maps and oscillations in the healthy hippocampus of Octodon degus, a natural model of sporadic Alzheimer’s disease

The Octodon degus is a South American rodent that is receiving increased attention as a potential model of aging and sporadic late-onset Alzheimer’s disease (AD). Impairments in spatial memory tasks in Octodon degus have been reported in relation to either advanced AD-like disease or hippocampal lesion, opening the way to investigate how the function of hippocampal networks affects behavior across AD stages. However, no characterization of hippocampal electrophysiology exists in this species. Here we describe in young, healthy specimens the activity of neurons and local field potential rhythms during spatial navigation tasks with and without objects. Our findings show similarities between the Octodon degus and laboratory rodents. First, place cells with characteristics similar to those found in rats and mice exist in the CA1 subfield of the Octodon degus. Second, the introduction of objects elicits novelty-related exploration and an increase in activity of CA1 cells, with location specific and unspecific components. Third, oscillations of the local field potential are organized according to their spectral content into bands similar to those found in laboratory rodents. These results suggest a common framework of underlying mechanisms, opening the way to future studies of hippocampal dysfunction in this species associated to aging and disease.Fil: Mugnaini, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Polania, Diana. Universidad de Chile; ChileFil: Díaz, Yannina Constanza. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Ezquer, Marcelo. Universidad del Desarrollo; ChileFil: Ezquer, Fernando. Universidad del Desarrollo; ChileFil: Deacon, Robert M. J.. Universidad de Chile; ChileFil: Cogram, Patricia. Universidad de Chile; Chile. University of California at Irvine; Estados UnidosFil: Kropff, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentin