The effect of temperature, farm density and foot-and-mouth disease restrictions on the 2007 UK bluetongue outbreak

Effects of crocin on the enhancement of in vitro neurogenesis: Involvement of Notch and CREB/BDNF signaling pathways

Objectives

Adult neurogenesis, the process of generating new neurons, continues throughout life. Unfortunately, this process is insufficient in pathological conditions and needs to be promoted. Crocin, the active component of saffron, affects neurogenesis in vivo and in vitro. We aimed to investigate the enhancing effects of crocin on the neurogenesis of adipose-derived mesenchymal stem cells in the presence of retinoic acid, as well as the molecular pathways involved.

Materials and Methods

Differentiation capacities and stemness potential of harvested ADSCs were evaluated by differentiating into osteocytes and adipocytes, and expression of mesenchymal CD markers by flow cytometry. The optimum dose of crocin was assessed with an MTT assay. Crocin, retinoic acid, CREB/BDNF, and Notch inhibitors and their combination were added to the culture medium. Jag1, Hes1, Notch, and BDNF gene expression were analyzed by RT-PCR on days 7, 14, and 21, while CREB, DCX, SOX2, and NeuN expression were analyzed by immunofluorescence.

Results

Expression of mesenchymal CD markers as well as adipogenic and osteogenic differentiation confirmed the origin and properties of ADSCs. The optimal dose of crocin was 1 mM. Crocin significantly (P<0.05) increased, while inhibitors (DATP&Naphthol) significantly (P<0.05) decreased Jag1, Hes1, Notch, and BDNF expression. Immunofluorescent assessments showed that expression of DCX, BDNF, NeuN, and Sox2 proteins increased significantly (P<0.05) after crocin administration and decreased significantly (P<0.05) after inhibitor administration.

Conclusion

Crocin can be used as an enhancer for neural differentiation of MSCs in vitro in the presence of retinoic acid. The mechanism is proposed through Notch and CREB/BDNF signaling pathways.

Impact of palmiped farm density on the resilience of the poultry sector to highly pathogenic avian influenza H5N8 in France

We analysed the interplay between palmiped farm density and the vulnerability of the poultry production system to highly pathogenic avian influenza (HPAI) H5N8. To do so, we used a spatially-explicit transmission model, which was calibrated to reproduce the observed spatio-temporal distribution of outbreaks in France during the 2016-2017 epidemic of HPAI. Six scenarios were investigated, in which the density of palmiped farms was decreased in the municipalities with the highest palmiped farm density. For each of the six scenarios, we first calculated the spatial distribution of the basic reproduction number (R₀), i.e. the expected number of farms a particular farm would be likely to infect, should all other farms be susceptible. We also ran in silico simulations of the adjusted model for each scenario to estimate epidemic sizes and time-varying effective reproduction numbers. We showed that reducing palmiped farm density in the densest municipalities decreased substantially the size of the areas with high R₀ values (> 1.5). In silico simulations suggested that reducing palmiped farm density, even slightly, in the densest municipalities was expected to decrease substantially the number of affected poultry farms and therefore provide benefits to the poultry sector as a whole. However, they also suggest that it would not have been sufficient, even in combination with the intervention measures implemented during the 2016-2017 epidemic, to completely prevent the virus from spreading. Therefore, the effectiveness of alternative structural preventive approaches now needs to be assessed, including flock size reduction and targeted vaccination.

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

Landscape Seroprevalence of Three Hemorrhagic Disease-Causing Viruses in a Wild Cervid

Disease plays a major role in shaping wildlife populations worldwide, and changes in landscape conditions can significantly influence risk of pathogen exposure, a threat to vulnerable wild species. Three viruses that cause hemorrhagic disease affect cervid populations in the USA (Odocoileus hemionus adenovirus, bluetongue virus, and epizootic hemorrhagic disease virus), but little is known of their distribution and prevalence in wild populations. We explored the distribution and co-occurrence of seroprevalence of these three pathogens in southern mule deer (Odocoileus hemionus fuliginatus), a subspecies of conservation concern and a harvested species native to southern California, to evaluate the distribution of exposure to these pathogens relative to landscape attributes. We found that habitat type, level of development, and proximity to livestock may affect hemorrhagic disease seroprevalence in southern mule deer. Continued monitoring of hemorrhagic disease-causing viruses in areas where deer are in proximity to cattle and human development is needed to better understand the implications of future outbreaks in wild populations and to identify opportunities to mitigate disease impacts in southern mule deer and other cervid species.

Biting midge dynamics and bluetongue transmission: a multiscale model linking catch data with climate and disease outbreaks

Bluetongue virus (BTV) serotype 8 has been circulating in Europe since a major outbreak occurred in 2006, causing economic losses to livestock farms. The unpredictability of the biting activity of midges that transmit BTV implies difficulty in computing accurate transmission models. This study uniquely integrates field collections of midges at a range of European latitudes (in Sweden, The Netherlands, and Italy), with a multi-scale modelling approach. We inferred the environmental factors that influence the dynamics of midge catching, and then directly linked predicted midge catches to BTV transmission dynamics. Catch predictions were linked to the observed prevalence amongst sentinel cattle during the 2007 BTV outbreak in The Netherlands using a dynamic transmission model. We were able to directly infer a scaling parameter between daily midge catch predictions and the true biting rate per cow per day. Compared to biting rate per cow per day the scaling parameter was around 50% of 24 h midge catches with traps. Extending the estimated biting rate across Europe, for different seasons and years, indicated that whilst intensity of transmission is expected to vary widely from herd to herd, around 95% of naïve herds in western Europe have been at risk of sustained transmission over the last 15 years.

Bayesian optimisation of restriction zones for bluetongue control

We investigate the restriction of animal movements as a method to control the spread of bluetongue, an infectious disease of livestock that is becoming increasingly prevalent due to the onset of climate change. We derive control policies for the UK that minimise the number of infected farms during an outbreak using Bayesian optimisation and a simulation-based model of BT. Two cases are presented: first, where the region of introduction is randomly selected from England and Wales to find a generalised strategy. This “national” model is shown to be just as effective at subduing the spread of bluetongue as the current strategy of the UK government. Our proposed controls are simpler to implement, affect fewer farms in the process and, in so doing, minimise the potential economic implications. Second, we consider policies that are tailored to the specific region in which the first infection was detected. Seven different regions in the UK were explored and improvements in efficiency from the use of specialised policies presented. As a consequence of the increasing temperatures associated with climate change, efficient control measures for vector-borne diseases such as this are expected to become increasingly important. Our work demonstrates the potential value of using Bayesian optimisation in developing cost-effective disease management strategies.

A network-based approach to modelling bluetongue spread in France

Bluetongue virus serotype 8 (BTV-8) was reported for the first time in Europe in 2006, causing the largest bluetongue outbreak ever recorded. France was mostly impacted in 2007/09. Trade restrictions were implemented all along. Vaccination became available from 2008: a limited number of doses was first administered in an emergency vaccination campaign, followed by two nationwide compulsory vaccination campaigns in 2009 and 2010. France regained a disease-free status in December 2012, but BTV may have kept circulating undetected as infected herds have been reported again since August 2015. We developed a stochastic dynamic compartmental model of BTV transmission in cattle and sheep to analyze the relative importance of vector active flight and host movements in disease spread, and assess the effectiveness of control measures. We represented BTV transmission both within and between French administrative subdivisions called cantons, during the 2007/09 outbreak and until the end of 2010, when compulsory vaccination was interrupted. Within-canton transmission was vector-borne, and between canton transmission could occur through three contact networks that accounted for movements of: (i) vectors between pastures located at close distance; (ii) cattle and sheep between pastures of the same farm; (iii) traded cattle. We estimated the model parameters by approximate Bayesian computation, using data from the 2007 French outbreak. With this framework, we were able to reproduce the BTV-8 epizootic wave. Host movements between distant pastures of the same farm were found to have a major contribution to BTV spread to disease-free areas, thus raising practical questions about herd management during outbreaks. We found that cattle trade restrictions had been well complied with; without them, the whole French territory would have been infected by winter 2007. The 2008 emergency vaccination campaign had little impact on disease spread as almost half vaccine doses had likely been administered to already immune cattle. Alternatively, establishing a vaccination buffer zone would have allowed a better control of BTV in 2008: limiting its spatial expansion and decreasing the number of infected cattle and sheep. We also showed a major role of compulsory vaccination in controlling the outbreak in 2009 and 2010, though we predicted a possible low-level circulation after the last detection.