Generation of magnetized olfactory ensheathing cells for regenerative studies in the central and peripheral nervous tissue

As olfactory receptor axons grow from the peripheral to the central nervous system (CNS) aided by olfactory ensheathing cells (OECs), the transplantation of OECs has been suggested as a plausible therapy for spinal cord lesions. The problem with this hypothesis is that OECs do not represent a single homogeneous entity, but, instead, a functionally heterogeneous population that exhibits a variety of responses, including adhesion and repulsion during cell-matrix interactions. Some studies report that the migratory properties of OECs are compromised by inhibitory molecules and potentiated by chemical gradients. In this paper, we report a system based on modified OECs carrying magnetic nanoparticles as a proof of concept experiment enabling specific studies aimed at exploring the potential of OECs in the treatment of spinal cord injuries. Our studies have confirmed that magnetized OECs (i) survive well without exhibiting stress-associated cellular responses; (ii) in vitro, their migration can be modulated by magnetic fields; and (iii) their transplantation in organotypic slices of spinal cord and peripheral nerve showed positive integration in the model. Altogether, these findings indicate the therapeutic potential of magnetized OECs for CNS injuries

Automating nitrogen fertiliser management for cereals (Auto-N). AHDB Project Report No.561

Uncertainty in estimating fertiliser N requirements is large, with differences between recommended and measured N optima frequently exceeding 50 kg/ha. Precision farming technologies including yield mapping, canopy sensing, satellite imaging and soil mapping are now common-place on farm. The Auto-N project sought to apply the information readily available from these technologies within an ‘Auto-N logic’ to improve the precision of N fertiliser decision making. The ‘Auto-N logic’ was derived from that used to estimate fertiliser N requirements as set out in the AHDB Cereals & Oilseeds guide Nitrogen for winter wheat – management guidelines; this guide suggests that N requirements should be calculated by subtracting Soil N Supply (SNS) from Crop N Demand (CND: grain yield x crop N content) and dividing by Fertiliser N Recovery (FNR); thus the ‘Auto-N logic’ uses yield and protein maps to inform estimates of CND, canopy sensing to inform estimates of SNS and soil sensing to inform estimates of FNR. Novel chessboard N response experiments were set up on six commercial fields between harvest years 2010 and 2012 to quantify spatial variation in N requirement, to explain it in terms of CND, SNS and FNR, hence to develop the ‘Auto-N logic’. At each site, each farmer applied N as liquid urea plus ammonium nitrate (UAN) using the farm sprayer twice, in perpendicular directions, to create a systematic grid of ~400 plots (~12m × 12m) fertilised with N rates of 0, 120, 240 or 360 kg/ha; the area of each experiment exceeded 4 ha. Grain yields were measured by small-plot combine, grain samples were analysed for protein, and N harvest index and total N uptake were determined from pre-harvest grab samples. Values were then estimated for all variates and all N levels for all plots by kriging. Response curves were fitted, and N optima and their components (SNS, CND, FNR) were derived assuming 5 kg grain would pay for 1 kg fertiliser N. Within field variation in optimum N exceeded 100 kg/ha at all sites; spatial variation in optimal yield was greater than 2 t/ha at all sites and variation in SNS was generally greater than 50 kg/ha. Some of the spatial variation in optimum N was explained in terms of SNS and CND. However, the tendency for positive correlations between SNS and optimum yield was striking, and hindered complete explanation of spatial variation in optimum N: i.e. high yielding areas tended to have greater SNS, so the increased requirement from higher crop N demand was counteracted by the reduced requirement from higher SNS. Spatial variation in CND and SNS was reasonably well estimated from the use of past yield maps and crop sensing, respectively; often, similar within-field patterns showed through for both. However, variation in FNR was also large and was unpredictable. Using clustering techniques, zoning, performance mapping or simple averaging of data from five farms, it was shown that past yield maps could be used usefully to estimate variation in CND. In addition, variation in SNS could be predicted from canopy sensing in early spring (an algorithm was developed based on sensed NDVI and thermal time since sowing). Calibrations for crop N uptake, biomass and crop N status (Nitrogen Nutrition Index) from canopy sensing were explored, but no rational basis could be found to justify their inclusion in the ‘Auto-N logic’. Validation trials were set up with farmers on 11 fields in 2013 & 2014; these used adjacent tramlines to compare the Auto-N logic with the farm’s own practice, 50 kg/ha more N and 50 kg/ha less N. Evaluation of these trials along with economic analysis of the chessboard trials showed the benefits of precision in judging N requirements to be modest, whereas benefits of accuracy (proximity to the measured mean) were much greater. Whilst this work demonstrated the feasibility of automating judgements of N requirements within fields using precision information, the variability in CND, SNS and FNR, and crucially the interactions between them, meant that the use of such systems would not guarantee increased accuracy or precision of N use. The evidence suggests that variable rate N management can give only modest returns, even with a system making perfect predictions, if the field is already receiving the right average N rate. The results showed that the most important decisions concern N use for whole farms, then for whole fields, then for areas within fields. Precision technologies can help with all of these, especially through comparisons of crops between and within farms. However, the most effective aspect of precision farming technologies is probably the empowerment of farmers to test retrospectively the effects of their N decisions (or indeed any decisions) on-farm. Given the variation in and unpredictability of N requirements between fields and between farms the only way farmers can know for sure whether their chosen N rates were right is to test yield effects of different N rates – this is relatively easy now, by simply applying (say) 60 kg/ha more and 60 kg/ha less to adjacent tramlines. The chessboard trials initiated here have transformed our understanding of N responses and shown new possibilities for spatial experimentation, not only to empower on-farm testing, but to understand how soil variation affects husbandry outcomes. These trials show that N use is not the major cause of the very large spatial variation seen in yield. Thus, understanding the soil-related causes of yield variation should, and can, now become a priority for soil and agronomic research

Scenario-Driven Supply Chain Charaterization Using a Multi-Dimensional Approach

Extreme disruptive events, such as the volcano eruption in Iceland, the Japanese tsunami, and the COVID-19 pandemic, as well as constant changes in customers’ needs and expectations, have forced supply chains to continuously adapt to new environments. Consequently, it is paramount to understand the supply chain characteristics for possible future scenarios, in order to know how to respond to threats and take advantage of the opportunities that the next years will bring. This chapter focuses on describing the characteristics of the supply chain in each of the six macro-scenarios presented in Sardesai et al. (2020b), as final stage of the scenario building methodology. Supply chains for each scenario are characterized in eight dimensions: Products and Services, Supply Chain Paradigm, Sourcing and Distribution, Technology Level, Supply Chain Configuration, Manufacturing Systems, Sales Channel, and Sustainability

Generation of magnetized olfactory ensheathing cells for regenerative studies in the central and peripheral nervous tissue

As olfactory receptor axons grow from the peripheral to the central nervous system (CNS) aided by olfactory ensheathing cells (OECs), the transplantation of OECs has been suggested as a plausible therapy for spinal cord lesions. The problem with this hypothesis is that OECs do not represent a single homogeneous entity, but, instead, a functionally heterogeneous population that exhibits a variety of responses, including adhesion and repulsion during cell-matrix interactions. Some studies report that the migratory properties of OECs are compromised by inhibitory molecules and potentiated by chemical gradients. In this paper, we report a system based on modified OECs carrying magnetic nanoparticles as a proof of concept experiment enabling specific studies aimed at exploring the potential of OECs in the treatment of spinal cord injuries. Our studies have confirmed that magnetized OECs (i) survive well without exhibiting stress-associated cellular responses; (ii) in vitro, their migration can be modulated by magnetic fields; and (iii) their transplantation in organotypic slices of spinal cord and peripheral nerve showed positive integration in the model. Altogether, these findings indicate the therapeutic potential of magnetized OECs for CNS injuries

The Lichen Connections of Black Fungi

Many black meristematic fungi persist on rock surfaces\u2014hostile and exposed habitats where high doses of radiation and periods of desiccation alternate with rain and temperature extremes. To cope with these extremes, rock-inhabiting black fungi show phenotypic plasticity and produce melanin as cell wall pigments. The rather slow growth rate seems to be an additional prerequisite to oligotrophic conditions. At least some of these fungi can undergo facultative, lichen-like associations with photoautotrophs. Certain genera presenting different lifestyles are phylogenetic related among the superclass Dothideomyceta. In this paper, we focus on the genus Lichenothelia, which includes border-line lichens, that is, associations of melanised fungi with algae without forming proper lichen thalli. We provide a first phylogenetic hypothesis to show that Lichenothelia belongs to the superclass Dothideomyceta. Further, culture experiments revealed the presence of co-occurring fungi in Lichenothelia thalli. These fungi are related to plant pathogenic fungi (Mycosphaerellaceae) and to other rock-inhabiting lineages (Teratosphaeriaceae). The Lichenothelia thallus-forming fungi represent therefore consortia of different black fungal strains. Our results suggest a common link between rock-inhabiting meristematic and lichen-forming lifestyles of ascomycetous fungi